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American Journal of  Smart 
Technology and Solutions (AJSTS)

Performance Evaluation of  an Engine Operated Weeding Machine
Degefa Woyessa1*

Volume 2 Issue 2, Year 2023
ISSN: 2837-0295 (Online)

DOI: https://doi.org/10.54536/ajsts.v2i2.1455
https://journals.e-palli.com/home/index.php/ajsts

Article Information ABSTRACT

Received: August 02, 2023

Accepted: September 07, 2023

Published: September 26, 2023

Weeds constitute a serious problem to wheat crops and cause a great loss to the yield. 
Manual weeding is labor-intensive and time-consuming. Chemical weed control has 
a negative impact on both the environment and humans. Today the agricultural sector 
requires non-chemical weed control that safeguards consumers’ demand for high-quality 
food products and pay special attention to food safety. The objectives of  the study was to 
evaluate the performance of  engine operated weeding machine  by evaluating the weeding 
efficiency, plant damage, effective field capacity, field efficiency, fuel consumption, 
performance index, energy consumption, and cost economics of  engine operated 
weeder in wheat crop. The experimental design was a randomized complete block design 
and evaluation was conducted at three weeder forward speeds (1.5, 2, and 2.5 km/hr), 
two depths of  operation (from 0 to 20 and from 0 to 40 mm), and three levels of  soil 
moisture content (9.4, 12.34 and 15.25%). The performance of  the weeder was found to 
be optimum at 15.25 percent soil moisture content with 0 to 40 mm depth of  operation 
at a forward speed of  1.5 km/hr. The results revealed that maximum weeding efficiency 
of  90.1 percent was obtained with lower plant damage of  3.31 percent whereas the 
effective field capacity, field efficiency, fuel consumption, performance index, and energy 
consumption were found to be 0.052 ha/hr, 85.99%, 0.41 l/hr, 276.78 ha/hp, and 481.71 
MJ/ha, respectively. The analysis revealed that forward speed, depth of  operation, and 
soil moisture had significant effects on weeding efficiency, plant damage, effective field 
capacity, and fuel consumption at P<0.05 level of  significance. The cost of  weeding per 
hectare was 758 ETB/ha and 1920 ETB/ha for engine-operated weeders and traditional 
weeding methods, respectively. Based on the performance results, it can be concluded that 
the weeding machine is an efficient, effective, and economically viable option with high 
scope for acceptability among small and medium-scale farmers.

Keywords
Cost of  Weeding, Energy 
Consumption, Field Eficiency, 
Plant Damage, Performance, 
WBeat, Weeding Machine, 
Weeding Efficiency

1 Oromia Agricultural Research Institute, Asella Agricultural Engineering Research Center, Asella, Ethiopia, P.O. Box 06 Asella, Ethiopia
* Corresponding author’s e-mail: degefawoyessa20007@gmail.com

INTRODUCTION
Wheat (Triticum aestivum L.) is one of  the most important 
food crops of  the world and a part of  the family Poaceae 
that includes major cereal crops of  the world such as 
maize, wheat, and rice. It is the staple food of  the diet of  
several Ethiopians and provides about 15% of  the caloric 
intake of  the population of  more than 90 million countries 
(FAO,2015). Wheat is one of  the most important crops in 
Ethiopia, ranking fourth in total cereal production after 
maize, sorghum, and teff  which contribute 10-12% each 
(Minot et al., 2015). More than 4.7 million households are 
involved in wheat production each year, producing about 
3.9 million tons of  wheat on 1.6 million hectares of  land, 
with a mean yield of  2.6 tons/ha (CSA,2013).
After South Africa, Ethiopia is the second-largest wheat 
producer in Sub-Saharan Africa (FAO,2015). Wheat is 
mainly grown in the highlands of  Ethiopia, with latitudes 
6 up to 16° N,  longitude 35 to 42°E, at altitudes 1500-
2800 meters above sea level, and an average minimum 
temperature of  60C to 110C (MoA, 2012). In Ethiopia, 
wheat covered an area of  1,696,082.59 hectares, with 
average productivity of  2.6 tons/ha during the main 
cropping season of  Meher and a total production of  
45,378,523.39 quintals (CSA,2016). According to (CSA, 
2014) reported that in the Oromia region, wheat covered 
an area of  875,641.45 hectares and total production was 

24,703,210.41 quintals, and in Arsi, 208,308.22 hectares 
which produce 6,484,360.05 quintals. Out of  the total 
grain crop area, 522,857.64 hectares were under cereals.
Despite its importance in Ethiopia, the national average 
wheat yield is 2.6 tons/ha, which is 12% below the 
average wheat yield in Africa and 24%  below the average 
wheat yield in the world (CSA,2016). Factors that reduce 
wheat yields are soil fertility decline, weeds, diseases, and 
insects. Weeds are one of  the major constraints of  wheat 
production and weed control is an important factor in 
increasing yields. There are many reasons for low wheat 
yields, but weed infestation is a fundamental and major 
factor in low yields in the crop production system 
(Shehzad et al., 2012). Weed infestation has been reported 
as a major problem to Ethiopia’s wheat production in 
both rural and governmental agricultural sectors. Weed 
control is one of  the most difficult tasks in agricultural 
production. Weed losses exceed those caused by any 
other agricultural pest. In Ethiopia, crop yield losses 
due to weeds vary from crop to crop and from region to 
region, due to different biotic and abiotic factors, it has 
been estimated that weeds cause a yield reduction due to 
delaying weeding 15 percent to 62 percent (Kebede,2000). 
The weed controls are mainly done by manual, chemical, 
and mechanical methods. In manual weeding, weeds are 
removed by using an indigenous tool, which is more 



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effective but it is expensive, labor-intensive as well as time-
consuming. In addition, the labor requirement for weeding 
depends on the weed flora, weed intensity, weeding time, 
and soil moisture content at the time of  weeding. 
Nowadays, the use of  herbicides is increasing day by day. It 
is preferred as a quick and effective weed control method 
without damaging the plants. But, it has adverse effects on 
human health and the environment. Today, the agricultural 
sector requires weed control without using chemicals to 
ensure food safety. Consumers demand high-quality food 
products and are particularly concerned about food safety. 
However, mechanical weeder is expected to encourage 
subsistence farmers leading to increased production and 
hence reducing poverty (Olukunle and Oguntunde,2006). 
Mechanical weed control is very effective as it helps to 
reduce the drudgery involved in manual weeding, kills 
the weeds and also keeps the soil surface loose ensuring 
soil aeration and water intake capacity (Hegazy et al., 
2014). Availability and cost of  labour for weed control 
are limiting its progress, and therefore development of  
suitable mechanised weeding method is imperative. The 
cost of  weeding by engine operated weeder is about one-
third of  weeding by manual labours (Tajuddin, 2006). 
But this method of  weed control has received much 
less scientific attention compared to the other weeding 
method in Ethiopia. In Ethiopia, weed control is done by 
manual weeding and chemicals using herbicides. Manual 
weeding tools are still popular in Ethiopia. Manually 
operated row crop weeder was developed at Asella 
Agricultural Engineering Research Center (AAERC) and 
is being used to control weeds which are more effective 
and affordable than traditional weeding methods but, 
labor-intensive and time-consuming (less field capacity), 
high drudgery and stress on labor (bending all the time 
to remove weeds). Generally, a few hand weeding is 
accomplished for cultivating wheat contingent on the type 
of  weeds and their density of  invasion. Notwithstanding, 
these techniques are difficult, less agreeable, tedious, and 
costly too. Nowadays herbicide usage is increasing. It is 

preferred as a quick and effective weed control method 
without damaging the crops. But, it has adverse effects on 
human health and the environment. It has consequences 
like cancer disease, environmental air pollution, increased 
acidity, and salinity of  the soil. It can contaminate the soil 
and the rainwater can carry these chemicals to other areas 
which will eventually pollute the air we breathe, the food 
we eat, and the water we drink. A mechanical rotary blade 
weeder for row-planted cereal crops was developed. But 
these types of  blades also are not efficient in weeding 
operations. Now mechanical wheat sowing machine 
is expanding in Ethiopia due to different government 
programs for mechanization. It is now necessary to 
develop an engine-operated weeding machine for row 
sowing wheat crops. The use of  a mechanical weeder 
is reducing drudgery, ensures ease of  operation during 
weeding, and resultantly increases production. Therefore, 
to assess the possibility of  mechanization of  the weeding 
operation, an engine-operated weeding machine was 
proposed to be designed and developed considering 
the optimum shape, size, and location of  the weeding 
blade, and performance evaluation was conducted for 
the end-users. Here comes the relevance of  mechanized 
weeding, which is reducing the time, and cost of  weeding 
operation, and significantly improves weeding efficiency 
as well as the quality of  weeding. Therefore, to increase 
agricultural production and reduce the time and cost of  
weeding operations there need to be adopting mechanical 
weeding. Hence, the study was taken to evaluate the 
performance of  the developed weeding machine based 
on weeding efficiency, plant damage, effective field 
capacity, performance index, and energy consumption, 
and to carry out the cost analysis of  the developed 
weeding machine.

MATERIALS AND METHODS
The study site was located 168.7 km away southeast of  
Addis Ababa, Asella Agricultural Engineering Research 
Center (AAERC). Fabrication and performance 

Figure 1: Location of  the study area



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evaluation of  the prototype was made at Asella 
Agricultural Engineering Research Center. The center 
was located at 6° 59’ to 8°49’ N latitudes and 38° 41’ to 
40° 44’ E longitudes, having an elevation of  2430 meters 
above sea level. The study was undertaken at farmers’ 
field Huruta Doro Kebele, Jaju Woreda in the Arsi Zone 
of  Oromia Regional State.

Description of  the Machine 
The engine-operated row weeding machine was easy to 
operate, better to handle, reduce drudgery, manufactured 
from locally available materials, and easily maintained. The 

power is transmitted from the engine to an intermediate 
shaft which should connect to the bevel gear and from 
the bevel gear shaft to the chain and sprocket then the 
ground wheel starts forward direction and the weeder 
was started and weeding operations were performed. It 
consisted of  the following main components; mainframe, 
weeder tine, ground drive wheel, power transmission 
system, handle, engaging and disengaging unit, bevel gear 
mechanism, and chain and sprocket mechanisms. The 
specifications of  the engine operated weeder were given 
in Table 1.

Table 1: Specifications of  an engine-operated weeder
Sr. No. Particulars Details
1 Name of  machine Engine operated weeder
2 Make of  machine AAERC
3 Overall dimension of  the machine (L × W × H) 1650 × 800 × 1050 mm
4 Weight of  machine 34.4 kg
5 Power source 5 hp petrol start diesel run engine
6 Fuel used diesel
7 Fuel tank capacity 3.9 lit
8 Engine details 4 stroke, 1 cylinder
9 Speed at engine 2800 rpm
10 Displacement 197 cm3
11 PTO shaft rotation Counter-clockwise from drive end
12 Weight of  engine 14 kg
13 Gear type Bevel
14 Chain drive ISO 10 B bush roller chain
15 Clutch Dog clutch
16 Axle 20 mm in diameter
17 Ground wheel 500 mm in diameter
18 Lug 33 no. 25 × 25 mm in size lugs welded at the periphery 

of  the ground wheel
19 Details of  weeding components

Frame dimension (L × B) mm 960 × 240 mm
Type of  blade Sweep type
No of  blade 3
Distance between blade Adjustable

20 Shank 25 mm × 25 mm × 2.5 mm in dia. and 500 in length

Performance Evaluation of  the Weeding Machine
The performance of  the engine-operated weeder was 
evaluated under field conditions. The parameters recorded 
before the weeding operations were the crop parameters 
(plants height) and field parameters (type of  soil, moisture 
content, bulk density, length, and width of  the field). The 
plant height was recorded by measuring the height of  the 
crop randomly in the field. Row to row spacing, length, 
and width of  the field were measured directly by using 

a standard measuring tape. The soil sample was taken 
randomly at different places within the experimental field 
to determine the moisture content and bulk density of  
the soil. To compare the field performance of  the weeder, 
different parameters: time taken for operation, plant 
damage and weed population, weeding efficiency, effective 
field capacity, field efficiency, performance index, fuel 
consumption, energy consumption, and cost of  weeding 
operation were calculated as per the procedure.



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Operational Parameters
Moisture Content of  the Soil
Moisture content of  the soil was determined using five 
samples collected randomly from the field.  The moisture 
content of  each sample was calculated by using the 
standard oven-dry method. The weight of  the sample 
with the box was taken and placed in the oven for drying. 
After 24 hours the oven-dry weight was taken and the 
moisture content was calculated by using the following 
formula (Rangapara J., 2014).
M (dry basis)=  (Ww-Wd)/Wd × 100                              (1)
Where, 
M = Moisture content of  soil, %
Ww = Weight of  wet soil, gm and
Wd = Weight of  oven-dry soil, gm.

Bulk Density of  Soil
The bulk density of  a soil indicates the degree of  
compactness of  the soil and is defined as mass per unit 
volume. Soil samples were collected randomly from 
treatments of  experimental plots with a core sampler. 
The core sampler was driven vertically deep enough (0 
to 15 cm) into the ground to fill the sampler can in the 
sampler. The weight of  each sample was measured and 
kept in an oven at a constant temperature of  1050C till 
the soil sample attained constant weight and the weight 
of  the oven-dried sample was taken. The bulk density 
of  each sample was calculated by using the following 
relationship (Rangapara J., 2014).

ρb  = M/V                                                                              (2)
Where,
ρb = bulk density of  soil, g/cm3

M = oven dry mass of  soil, gm and
V = volume of  core sampler, cm3

Plant Population
The total numbers of  plants were counted in an area of  
one square meter by a quadrate of  1m2 from randomly 
chosen places in each plot, before and after every weeding 
operation to observe plant damage percentage.

Plant Height
Any weeding and intercultural operational implement 
and machine performance are highly influenced by plant 
growth factors like height, branching pattern, canopy 
crown diameter, etc. In agricultural production practices, 
weed removal processes alone or in combination with 
intercultural operations are taken up at different time 
intervals. Majority of  farmers generally carry a minimum 
of  two such operations up to 50 days after sowing (DAS) 
in long-duration crops like wheat, barley, etc. However, 
the actual practice depends on some other factors. 
Keeping the crop growth factor’s importance in mind, 
the plant height was measured in two uniform plots at 25 
and 40 DAS.

Weed Population
Weed population per square meter was recorded randomly 
from each plot with the help of  1m2 quadrat, after 25 and 
40 days after sowing (DAS). All the weeds present in each 
plot were grouped under grasses and broadleaf  weeds.

Machine Performance Parameters
The machine performance parameters such as weeding 
efficiency, plant damage, effective field capacity, 
theoretical field capacity, field efficiency, performance 
index, energy consumption, and fuel consumption of  
power weeder were determined for the performance 
evaluation as follows.

Theoretical Field Capacity
It depends upon the speed and theoretical width of  the 
implement. It is the rate of  field coverage that should be 
obtained if  implements perform its function 100% of  
the time at the rated speed and always cover 100% of  its 
rated width. The theoretical field capacity was calculated 
as (Kepner et al., 2005).
TFC=  ( W × S   )/10                                                      (3)
Where,
TFC = Theoretical Field capacity, ha/h
S = Speed of  operation, km/hr and
W = Theoretical width of  implement, m

Effective Field Capacity
For calculating the effective field capacity, the time taken 
for actual work and the time used for other activities such 
as turning, cleaning, adjustment of  the machine, and time 

Figure 2: Performance testing during weeding



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spent for machine trouble are taken into consideration. 
The length and width of  the plot were measured and the 
area covered in that time was calculated. By calculating 
the area covered per hour, the actual field capacity was 
calculated. It is the actual average rate of  coverage by 
the implement. The total time required to complete the 
operation was recorded and effective field capacity was 
calculated as follows, (Kepner et al., 1978)
EFC=A/(Tp+Ti )                                                           (4)
Where:
EFC = Effective field capacity, ha/hr
A = Actual area covered, ha and
Tp = Productive time, hr
Ti =Non-productive time, hr

Field Efficiency
The field efficiency is the ratio of  the effective field 
capacity to the theoretical field capacity, usually measured 
in terms of  percentage. It includes the effect of  time lost 
in the field and of  failure to utilize the full width of  the 
machine (Kepner et al., 2005).
η = EFC/TFC  ×100                                                            (5)
Where:
η = Field efficiency (%)
TFC = Theoretical field capacity (ha/h)
EFC = Effective field capacity (ha/h)

Weeding Efficiency
It is the ratio of  the numbers of  weeds removed by a 
weeder to the number present in a unit area and it was 
expressed as a percentage. A square metallic frame of  1 
m2 was randomly cast in the test field and the numbers 
of  weeds included in the frame were counted before and 
after weeding. Three sets of  observations were taken in 
each replication of  the treatments. The weeding efficiency 
was calculated by the following formula (Tajuddin, 2006).
Weeding efficiency (%)=(W1-W2)/W1 × 100                (6)
Where:
W1 = Number of  weeds counted per unit area before 
weeding operation
W2 = Number of  weeds counted in the same unit area 
after the weeding operatio

Plant Damage
It is the ratio of  the number of  plants damaged in a row to 
the number of  plants present in that row. It was expressed 
in percentages. The plant damage was calculated by the 
following formula (Yadav & Pund, 2007)
Plant damage (%)=(1-q/p)  × 100                               (7)
Where: 
p = Number of  plants in a 10 m row length of  the field 
before weeding, q = Number of  plants in a 10 m row 
length of  the field after weeding

Fuel Consumption
Fuel consumption has a direct effect on the economics 
of  the weeding machine. It was measured by the top-fill 
method. The fuel tank was filled before the testing at 

level condition. After completion of  the test operation, 
the amount of  fuel required to top fill again is the fuel 
consumption for the test duration. This observation was 
used for the computation of  fuel consumption in l/hr 
(Nkakini et al., 2010)
Fc=fr/t                                                                          (8)
Where:
Fc= fuel consumption (l/hr)
fr=  Re-filled quantity of  fuel (l)
t= Total time of  weeding (hr)

Energy Consumption
For the engine-operated weeder, the total time taken for 
the operation, total fuel consumption, and the number 
of  laborers required were taken for energy calculation. 
Measurement of  fuel consumption in respect of  power 
was done on the plot size of  the field. The direct energy 
use per hectare for intercultural operation consists 
of  human labor energy and mechanical energy was 
computed by the following equation Karale et al., (2008).
ED = EDF  + EDO                                                      (9)
Where,
EDF = Mechanical energy based on fuel consumption 
(MJ/ha), EDO = Direct energy input of  operator 
(human energy) (MJ/ha), ED = Specific direct energy use 
for field operation (MJ/ha),
Human labor (man-hours) was converted into energy 
units by multiplying the number of  total human labor 
with working hours to the energy equivalent. The energy 
equivalent of  an adult man is 1.97 MJ/h and for an adult 
woman, it is 1.57 MJ/ha. The following equation was 
used for the conversion of  the physical unit of  human 
labor into energy unit according to Singh et al., (2002)
Human Energy (MJ/ha)= (NL×EE × Time (hr))/
(weeding area (ha))                    (10)
Where,
NL =No.of  labour 
EE = Energy equivalent of  person (MJ/manhr)
Mechanical energy inputs were calculated based on 
the fuel consumption (liter/hour) of  the machine and 
working hours per operation as well as the number of  
operations in the weeding area. The energy equivalent 
of  fuel 48.23 MJ/L for gasoline and 56.3MJ/L for diesel 
was given to convert the factor unit into the energy unit 
according to Singh et al., (2002).
Mechanical Energy(MJ/ha)= (FC×EE ×time (hr))/
(weeding area (ha))                   (11)
Where,
FC = Fuel consumption (l/hr), EE = Energy equivalent 
of  fuel (MJ/manhr)

Performance Index
The performance index of  the weeder was calculated by 
multiplying field capacity, weeding efficiency, and plant 
damage percentage and dividing the result with the power 
input of  the weeder (Monalisha et al., 2017)
PI=(a × q × e)/p                                                       (12)
Where:



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PI = Performance index, ha/hp
              a = Field capacity of  weeder, ha/h
              e = Weeding efficiency, %
              q= plant damage, %,  p = Power input, hp

Experimental Design and Treatment
The field experiment was conducted at selected farmer 
fields at Jaju district in Arsi Zone of  Oromia Regional 
State. The experiments were conducted in the field with 
three levels of  the forward speed of  the weeder (1.5, 2, 
and 2.5 km/hr), two depths of  operation (from 0 to 20 
mm and 0 to 40 mm), and three levels of  soil moisture 
content (9.4, 12.34, and 15.25%). Irrigation water was 
applied by using Parshall flume on the soil to maintain 
desired soil moisture. The experimental fields were 
divided into eighteen plots at once and each should have 
a 20 m by 5 m size. The experiment had three replications 
of  each treatment by using randomized complete block 
design (RCBD). Relevant observations of  each treatment 
regarding field conditions of  each were recorded before 
and after the weeding operation. The experimental design 
was laid as (3×2×3) with three replications and had a total 
of  54 test runs.

Statistical Analysis
Results of  the performance of  the engine-operated 
weeder under different treatments were analyzed by 
analysis of  variance (ANOVA) using statistical R-software 
(version 3.4.3, 2017). Statistical differences in effects of  
treatment mean were tested at 5% levels of  significance 
and separated using the least significant difference 
(LSD). The least significant difference (LSD) tests were 
performed for the mean values of  effective field capacity, 
weeding efficiency, plant damage, field efficiency, fuel 
consumption, energy consumption, and performance 
index. The level of  significance (P) for these relations 
was obtained by F-test based on analysis of  variance. The 
mean values and standard deviation (Mean ± Standard 
deviation) were used to present the results.

Costs Estimation of  Engine Operated Weeder
The initial cost of  engine operated weeder was calculated 
by adding up the cost of  individual components involved 
in the prototype fabrication at the prevalent market price. 
The cost of  the engine-operated weeder was divided 
under the two heads known as a fixed cost and variable 
cost. Estimates of  annual and hourly operational costs of  
the weeder were based on the capital cost of  the weeder, 
interest on capital, cost of  repairs and spare parts, labor 
cost, fuel cost, and depreciation. The operational cost 
components of  the prototype weeder were estimated in 
Birr (ETB) as follows;

a) Depreciation cost (DP): It was a measure of  the 
amount by which the value of  the machine decreases with 
time. The depreciation cost was calculated as follows:
Dp=(CC-SVC)/(EL × H),(ETB/hr)                                       (13)

b) Interest on capital (IC), Interest was calculated 
on the average investment of  the machine taking into 

consideration the value of  the machine in the first and 
last year. The interest on capital was calculated as follows:
IC=((CC+SVC)/2)×((I %)/NAOHW  ),(ETB/hr)      (14)

c) Shelter, insurance, and tax cost was calculated by 
1.5% of  the initial cost
Total fixed cost = (a+ b + c)

d) The fuel cost of  the weeder was calculated in fuel 
cost per hour by multiplying by the fuel consumption of  
the engine-operated weeder (in liters per hour) by fuel 
cost (in Birr/liters)

e) cost of  repairs and spares (Repair and maintenance 
at 5% of  the initial cost)
CRS=(CC × 5%)/AWHW,(ETB/hr)                                    (15)

f) Labor wages: Wage was calculated based on actual 
wages of  workers per hour
LW=DLW/DWH,(ETB/hr)                                              (16)
Total variable cost = (d + e + f)
The total cost of  weeding per hour of  the developed 
power weeder was calculated by summation of  total fixed 
cost per hour with total variable cost per hour.
The total cost of  weeding = variable cost of  the weeder 
+ fixed cost of  the weeder, Finally the cost of  operation 
of  the weeder was calculated by the multiplication of  the 
average effective field capacity of  the weeder with the 
total cost of  operation of  the weeder.
Where:      
Dp = Depreciation, ETB/hr
CC = Capital cost, ETB/hr
SVC = Salvage value 10% of  initial cost
CRS = Cost of  repairs and spares
EL= Estimate life (hr) (assume that estimate life 10 years)
IC = Interest on capital (ETB/hr)
LW = Labor wages
H = Number of  working hour per year
I = Interest, %
NAOHW = Number of  the annual operating hours of  
the weeder (ETB/hr)
AWHW = Annual working hours of  the weeder
DLW = Daily labor wage 

RESULTS AND DISCUSSION
This study was undertaken to evaluate the performance 
of  an engine-operated weeding machine for the wheat 
crops. The performance evaluation of  an engine-operated 
weeder, the results obtained and their discussions were 
presented in this section.  The performance indicator 
of  the engine-operated weeding machine was expressed 
in terms of  weeding efficiency, plant damage, field 
efficiency, fuel consumption, performance index, and 
energy consumption. The costs of  operation were 
calculated and the effects of  the machine and operational 
parameters on soil physical properties are presented. The 
performance of  the prototype machine was evaluated 
under field conditions and the results obtained were 
analyzed and discussed under the following sub-headings.

Physical Properties of  Soil
The performance of  the prototype was evaluated 



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under field conditions in sandy loam soil.  Soil physical 
properties concerning machine parameters are important 
from the design point of  any weeding system. Soil 
moisture content was an independent parameter while 
bulk density as a dependent parameter was measured 
at respective soil moisture content. The interactions 
between these parameters directly affect the performance 
of  the weeding system in terms of  weeding efficiency and 
power requirement to operate the machine under field 
conditions.

Soil Moisture Content 
Five soil samples were taken randomly at 5 different 
locations in the plot using a core sampler. The moisture 
content observed values were 15.25±0.26, 12.34±0.07, 
and 9.4±0.11% (d.b), respectively, and denoted by M1 in 
the range of  9.4±0.11%, M2 in the range of  12.34±0.07%, 
and M3 in the range of  15.25±0.26%, respectively

Effect of  Soil Moisture on Soil Bulk Density
Bulk density is an indicator of  soil compaction and 
soil health. Before conducting each experiment, the 
bulk density of  soil was observed for each experiment 
randomly at 5 locations at each soil moisture content level 
for studying the effect of  soil bulk density on different 
parameters. The observed values are presented in Fig.3 
which shows the variations in soil bulk density at different 
soil moisture contents. It was observed that bulk density 
decreased with an increase in soil moisture content 
The interactions between these parameters had a direct 
effect on the performance of  weeding efficiency and 
the power required to operate the machine under field 
conditions. Soil bulk density measured in the field at 
different soil moisture levels showed an inverse linear 
relationship. The soil bulk density measured were 1561, 
1448, and 1385 kg/m3 at the soil moisture content of  

9.4, 12.25,  and 15.25% (db), respectively as shown in 
Appendix Table 13.  Bulk density decreased by 12.7% 
with an increase in soil moisture content from 9.4 to 
15.25%. The relationship between soil moisture content 
and bulk density was given by y = -88x +1640.7 with an 
R² of  0.9738.

Plant Height
At 25 and 40 DAS, plant height was measured in two 
uniform plots, with mean values summarized in Table 
2. The result shows that the plant heights were very 
consistent throughout the two selected plots A1 and 
B2, but variation in data was not significant. The highest 
height varied in the range of  22.89±2.64 to 57.33±2.35 
cm as the growth period increased from 25 to 40 DAS. 
The plant height differences between the height and 
lowest values of  replications were supported by the 
coefficient of  variation statistical parameter.

Figure 3: Diagram showing soil bulk density change with 
soil moisture

Table 2: Plant height of  25 and 40 DAS
Plot A1 Plot B1

Rep DAS Mean (cm) Std CV (%) Mean (cm) Std CV (%)
R1 25 33.22 2.73 8.22 32.56 4.47 13.73
R2 31.11 3.48 11.18 27.33 2.11 7.72
R3 30.44 2.07 6.80 22.89 2.64 11.6
R4 28.01 1.87 6.67 26.02 3.23 12.41
R5 34.67 4.72 13.61 26.44 3.92 14.8
R6 32.33 3.57 11.04 32.56 2.63 8.07
R1 40 52.89 2.26 4.27 48.89 2.51 5.14
R2 53.67 3.20 5.97 48.00 3.43 7.15
R3 53.67 3.81 7.09 47.22 4.85 1.03
R4 57.33 2.35 4.09 49.89 2.64 5.29
R5 56.78 2.54 4.47 52.89 3.07 5.81
R6 55.33 2.35 4.24 45.33 3.71 8.18

Evaluation of  an Engine Operated Weeder
The engine-operated weeder was tested under field 
conditions to determine the operational performance 

parameters. The parameters selected for the study included 
three forward speeds (1.5, 2, and 2.5 km/hr), two depths 
of  operation (varied from 0 to 20 mm and 0 to 40 mm), 



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Am. J. Smart. Technol. Solutions 2(2) 21-42, 2023

and three levels of  soil moisture content (9.4, 12.34, and 
15.25%). The test procedure was explained in the above 
section. The effect of  operational parameters was studied 
to evaluate the performance of  the weeder in terms of  
weeding efficiency, plant damage, effective field capacity, 
field efficiency, fuel consumption, energy consumption, 
performance index, and cost of  the weeder, and also the 
results were discussed below.

Effect of  Soil Moisture and Machine Operational 
Parameters on Weeding Efficiency
The effects of  soil moisture and machine operational 
parameters on weeding efficiency are presented in Figure 
4 and Table 7. It is evident that as the depth of  operation 
increased from 0 to 20 and from 0 to 40 mm, the weeding 
efficiency increased from 73.2 to 78.99% and from 75.74 
to 90.1% with 1.5 km/hr weeder forward speed increased 
soil moisture content from 9.4% to 15.25% respectively. 
This shows that weeding efficiency decreased with 
increasing weeder forward speed. Weeding efficiency 
values decreased from 73.2 to 71.97% and from 75.74 to 
74.74% when the weeder forward speed increased from 
1.5 to 2 km/hr for two depths of  operation from 0 to 20 
and 0 to 40 mm respectively.
From Table 7, the minimum value of  weeding efficiency 
was 70.98% and obtained with a 2.5 km/hr weeder 
forward speed at depth of  operation ranging from 0 
to 20 mm and soil moisture content of  9.4% whereas 
the maximum value of  weeding efficiency was 90.1% 
and obtained with a 1.5 km/hr weeder forward speed 
at depth of  operation varied from 0 to 40 mm and 
15.25% soil moisture content. These findings are in 
close agreement with the result reported by Hegazy et 
al., (2014).  Generally, weeding efficiency increased as 
moisture content increased. The main reason behind it 

was that when moisture content increases slippage of  the 
ground wheel of  the weeder which considerably affects 
the turning length of  the weeder. As a result, weeding 
efficiency was more in the case of  12.34 and 15.25% 
soil moisture contents when compared with 9.4% soil 
moisture content. As the depth of  operation increased, 
the weeding efficiency increased. Similar results were 
observed for all depths of  operation.
The individual and combined effect of  operational 
parameters on weeding efficiency was analyzed statistically 
and presented in Table 3 and 7. ANOVA revealed that 
the depth of  operation (D) and moisture content (M) 
had a significant effect on weeding efficiency at (P<0.05) 
level of  significance and each variable individually had 
a significant effect on weeding efficiency whereas the 
speed of  operation had no significant effects on weeding 
efficiency, but there was a significant difference between 
lower and higher values at (p<0.05). The interaction 
effect of  (S×D), (S×M), and (D×M) are presented in 
Tables 4, 5, and 6 respectively. The interaction effect of  
(S×D), (S×M), and (D×M) had no significant effect on 
the weeding efficiency. The combined effect of  variables 
(D×S×M) also did not significantly influence the weeding 
efficiency at a 5% level of  significance.
Results of  the interaction effect of  forward speed and 
depth of  operation varied from 74.58 to 81.61% with 
non-significant (P>0.05) differences among the values of  
weeding efficiency. The lowest value was obtained from 
the combination of  forward speed (2.5 km/hr) and depth 
of  operation (from 0 to 20 mm) whereas the highest 
value was at the combination of  forward speed (1.5 km/
hr) and depth of  operation of  (0 to 40 mm). The data 
showed that depth of  operation had a stronger influence 
on weeding efficiency than forward speed.

Table 3: Main effect of  forward speed, depth, and soil moisture content on performance parameters of  weeder machine
Forward speed 
(km/hr)

WE (%) PD (%) EFC (ha/hr) FE (%) FC (l/hr) PI (ha/hp) EC (MJ/ha)

S1

78
.8

4±
5.

78
a

3.
53

±
0.

43
c

0.
04

7±
0.

00
41

c

82
.3

5±
5.

38
a

0.
45

±
0.

06
c

23
4.

8±
25

.5
1c

58
5.

91
±

 1
01

.3
7a

S2

77
.3

4±
4.

56
ab

3.
73

±
0.

90
b

0.
05

8±
0.

00
68

b

78
.9

1±
5.

66
b

0.
53

±
0.

06
b

28
8.

1±
42

.3
8b

55
5.

44
±

91
.1

5b

S3

77
.1

3±
4.

88
b

5.
61

±
1.

21
a

0.
06

4±
0.

00
54

a

75
.3

1±
5.

48
c

0.
59

±
0.

05
a

30
6.

4±
23

.2
9a

55
7.

59
±

63
.6

2ab

Soil Moisture



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Am. J. Smart. Technol. Solutions 2(2) 21-42, 2023

M1

73
.5

7±
2.

04
c

4.
92

±
1.

54
a

0.
05

6±
0.

00
86

a

73
.4

7±
3.

83
c

0.
58

±
0.

05
a

26
5.

3±
33

.5
9c

62
7.

15
±

63
.6

9a

M2

77
.2

8±
2.

72
b

4.
23

±
1.

16
b

0.
05

6±
0.

00
97

a

78
.2

1±
4.

71
b

0.
52

±
0.

06
b

27
1.

7±
39

.2
1b

56
8.

65
±

71
.6

8b

M3

82
.4

5±
5.

11
a

3.
71

±
0.

87
c

0.
05

5±
0.

00
88

a

84
.8

8±
3.

28
a

0.
47

±
0.

07
c

29
2.

2±
53

.2
6a

50
3.

14
±

 7
6.

81
c

LSD (5%) 1.23 0.13 0.003 0.19 0.01 15.80 28.42
SEM 0.43 0.04 0.001 0.09 0.00 5.50 9.89
Depth 
D1

75
.3

5±
 3

.5
6b

4.
19

±
0.

90
b

0.
05

7±
0.

00
93

a

78
.9

4±
6.

21
a

0.
51

±
0.

07
b

27
1.

4±
41

.9
9a

54
3.

22
±

69
.8

0b

D2

80
.1

9±
5.

23
a

4.
39

±
1.

61
a

0.
05

5±
0.

00
84

b

78
.7

6±
6.

16
a

0.
53

±
0.

09
a

28
1.

4±
45

.3
5a

58
9.

40
±

96
.0

1a
CV (%) 2.33 5.29 7.31 0.36 2.44 8.44 7.41
LSD (5%) 1.00 0.10 0.002 0.16 0.01 12.90 23.21
SEM 0.35 0.03 0.001 0.05 0.00 4.49 8.08

Where, WE = Weeding efficiency, PD = Plant damage, EFC = Effective field capacity, FE =Field efficiency, FC = Fuel consumed, 
PI =Performance index, EC=Energy consumption, Speed (S1= 1.5 km/hr, S2= 2 km/hr, S3= 2.5 km/hr), Depth (D1= 0 to 20 
mm, D2= 0 to 40 mm), Soil moisture content (M1= 9.4%, M2= 12.34% and M3= 15.25%), CV = coefficient of  variation; LSD = 
least significance difference, SEM= Standard error of  the mean,  Values are Mean ± SD. Mean values comparison arranged according 
to descending order with the same letter in a column are not significantly different at 5% level of  significance.



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Am. J. Smart. Technol. Solutions 2(2) 21-42, 2023

Effect of  Soil Moisture and Machine Operational 
Parameters on Plant Damage
The effects of  depth of  operation, forward speed, and 
soil moisture on plant damage are presented in Figure 5 
and Table 7. It was observed that the minimum value of  
plant damage obtained was 2.78% at 1.5 km/hr weeder 
forward speed when the soil moisture was 15.25% and 
the depth of  operation varied from 0 to 20 mm. The 
maximum value of  plant damage 7.56% was recorded 
with 2.5 km/hr at depth of  operation ranging from 0 to 40 

mm and 9.4% soil moisture. It is evident that as the depth 
of  operation increased, the plant damage percentage 
increased whereas soil moisture content increased, the 
plant damage percentage decreased. However, it was 
observed that as forward speed and depth operation 
increased, the plant damage percentage increased. This 
is mainly due to high speed and depth, the movement 
of  the weeder did not remain a straight line but sideward 
also, resulting in damage to plants. The mean comparison 
for the main effect of  variables on plant damage is 

Figure 4: Effect of  soil moisture and machine operational parameter weeding efficiency

Figure 5: Effect of  soil moisture and machine operational parameters on plant damage



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Am. J. Smart. Technol. Solutions 2(2) 21-42, 2023

summarized in Table 3. From this table, the higher plant 
damage 5.61% was obtained at 2.5 km/hr forward speed 
of  operation. The same trend occurred for the forward 
speeds of  1.5 and 2 km/hr which obtained 3.53 and 3.73 
percent of  plant damage respectively. However, the lowest 
plant damage was obtained at the forward speed of  1.5 
km/hr, and the depth of  operation ranged from 0 to 20 
mm. The individual effect of  operational parameters on 
plant damage was analyzed statistically and presented in 
Table 3. ANOVA revealed that forward speeds (S), depth 
of  operation (D), and soil moisture content (M) had 
significant effects on plant damage at (p<0.05) level of  
significance. Results revealed that there was a significant 

difference (P<0.05) in plant damage at the two depths 
of  operation. The interaction effects of  forward speed 
and depth of  operation (S×D), forward speed and soil 
moisture (S×M), depth of  operation, and soil moisture 
(D×M) on plant damage are presented in Tables 4, 5, 
and 6 respectively. The results revealed that the interaction 
effect of  variables (S×D) and (S×M), had significant effects 
on the plant damage at (P<0.05) level of  significance. The 
interaction effect (D×M) had no significant influence 
(P>0.05) on plant damage. The results of  the combined 
effect of  variables (D×S×M) are presented in Table 7 and 
revealed that there was no significant effect on the plant 
damage at (P>0.05) level of  significance.

Table 4: Interaction effect of  forward speed and depth of  operation on performance parameters of  weeder
Speed (km/hr) Depth (mm) WE 

(%)
PD 
(%)

EFC (ha/hr) FE 
(%)

FC (l/hr) PI (ha/hp) EC (MJ/ha)

S1 D1

76
.0

7±
3.

15
c

3.
25

±
0.

19
e

0.
04

6±
0.

00
1c

84
.3

1±
4.

64
a

0.
46

±
0.

06
e

22
5.

12
±

3.
56

c

59
2.

88
±

72
.1

4a

D2

81
.6

0 
±

6.
62

a

3.
80

±
0.

42
d

0.
04

7±
0.

00
6c

80
.3

9±
5.

59
c

0.
44

±
0.

07
f

24
4.

42
±

34
.0

8c

57
8.

92
±

12
8.

54
a

S2 D1

75
.3

9±
3.

73
c

4.
49

±
0.

52
c

0.
05

7±
0.

00
5b

75
.7

6±
5.

10
e

0.
51

±
0.

06
d

27
2.

24
 ±

22
.1

2b

53
2.

62
±

 6
6.

14
b

D2

79
.2

9±
4.

65
b

2.
97

±
0.

41
f

0.
05

8±
0.

00
8b

82
.0

5±
4.

43
b

0.
55

±
0.

04
c

30
3.

88
 ±

52
.5

9a

57
8.

25
±

11
0.

03
a

S3 D1

74
.5

8±
3.

99
c

4.
80

±
0.

87
b

0.
06

7±
0.

00
2a

76
.7

5±
5.

36
d

0.
57

±
0.

04
b

31
6.

92
 ±

22
.1

a

50
4.

15
±

40
.4

4b

D2

79
.6

8±
4.

47
b

6.
38

±
1.

00
a

0.
05

9±
0.

00
4b

73
.8

5±
5.

49
f

0.
61

±
0.

04
a

29
5.

85
 ±

20
.3

7a

61
1.

02
±

23
.2

2a

CV (%) 2.33 5.29 7.31 0.36 2.44 8.44 7.41
LSD (5%) 1.73 0.18 0.004 0.27 0.01 22.35 40.20
SEM 0.60 0.06 0.001 0.09 0.004 7.78 13.99

Where, WE = weeding efficiency, PD = plant damage, EFC= effective field capacity, FE = field efficiency, FC = fuel consumed, 
PI = performance index,  EC= Energy consumption, SEM= Standard error of  the mean, CV = coefficient of  variation; Speed 
(S1= 1.5 km/hr, S2= 2 km/hr, S3= 2.5 km/hr), Depth (D1= 0 to 20 mm, D2= 0  to 40 mm), values are Mean ± SD, LSD 
= least significance difference.  Means value comparison arranged according to descending order with the same letter in a column are not 
significantly different at 5% (p>0.05) level of  probability



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Effect of  Soil Moisture and Operational Parameters 
on Effective Field Capacity
The effective field capacity decreased as the depth of  the 
operation increased, as shown in Figure 6. The effective 
field capacity increased with the increase in forward 
speed, due to more area covered in less time.  With a 1.5 
km/hr weeder forward speed, the effective field capacity 
decreased from 0.047 to 0.045 ha/hr at 9.4 percent soil 
moisture content when the depth of  operation increased 
(from 0 to 20 and 0 to 40 mm). The results also revealed 
that at all levels of  soil moisture content, the effective field 
capacity increased with increasing weeder forward speed, 
whereas the effective field capacity decreased as the soil 
moisture level increased in all treatments. This may be 
due to the frequent sliding of  tines under higher moisture 
conditions. Values of  effective field capacity increased 
from 0.047 to 0.059 and from 0.045 to 0.055 ha/hr when 
the weeder forward speed increased from 1.5 to 2 km/
hr and depths of  operation ranged from 0 to 20 and 0 to 
40 mm respectively at 9.4% soil moisture content. At the 
different levels of  soil moisture content 9.4, 12.34 and 

15.25% the values of  effective field capacity were 0.047, 
0.046, and 0.046 ha/hr for 1.5 km/hr weeder forward 
speed at 0 to 20 mm depth of  operation. 
The maximum value of  effective field capacity was 0.068 
ha/hr at 2.5 km/hr weeder forward speed at depth of  
operation ranging from 0 to 20 mm and soil moisture 
content at 9.4 percent whereas the minimum value of  
effective field capacity was 0.044 ha/hr and achieved with 
1.5 km/hr weeder forward speed at depth of  operation 
varied from 0 to 40 mm and soil moisture content at 
12.34 percent. These findings are in close agreement with 
the result reported by Manian et al., (2004).
The individual and combined effect of  operational 
parameters on effective field capacity was analyzed 
statistically and presented in Table 3. Analysis of  
variance revealed that forward speed (S) had a significant 
influence on the effective field capacity at (p<0.05) level 
of  significance while the depth of  operation (D) and soil 
moisture content(M) had no significant influence on the 
effective field capacity at (p>0.05) level of  significance. 
The interaction effects in forward speed and depth of  

Figure 6: Effect of  soil moisture and machine operational parameter on effective field capacity

Table 5: Interaction effect of  forward speed and soil moisture content on performance parameters
Speed (km/hr) Soil Moisture

(%)
WE 
(%)

PD 
(%)

EFC (ha/hr) FE 
(%)

FC (l/hr) PI (ha/hp) EC (MJ/ha)

S1 M1

74
.4

7±
1.

90
de

3.
79

±
0.

54
e

0.
04

6±
0.

00
1d

76
.1

6±
3.

05
e

0.
52

±
0.

02
d

22
4.

03
±

4.
89

e

67
7.

55
±

50
.4

7a

M2

77
.4

9 
±

2.
14

c

3.
53

±
0.

35
f

0.
04

5±
0.

00
1d

83
.1

4±
1.

81
c

0.
44

±
0.

02
f

22
8.

19
 ±

8.
09

de

59
6.

45
±

43
.1

6bc

M3

84
.5

4±
6.

42
a

3.
25

±
0.

15
g

0.
04

9±
0.

00
6d

87
.7

4±
2.

05
a

0.
38

±
0.

03
g

25
2.

07
±

9.
69

cd

48
3.

71
±

 8
8.

24
e



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Am. J. Smart. Technol. Solutions 2(2) 21-42, 2023

S2 M1

73
.3

5±
1.

73
e

4.
27

±
0.

95
e

0.
05

7±
0.

00
5c

74
.0

5±
3.

80
f

0.
59

±
0.

02
b

27
4.

79
±

9.
16

bc

61
7.

14
±

66
.8

6b

M2

76
.9

8±
2.

02
cd

3.
66

±
0.

83
ef

0.
05

6±
0.

00
6c

77
.9

3±
3.

73
d

0.
53

±
0.

03
cd

27
2.

32
 ±

8.
35

bc

57
1.

79
±

93
.8

8b
cd

M3
81

.6
9 

±
4.

65
b

3.
27

±
0.

75
g

0.
06

1±
0.

00
8bc

84
.7

4±
3.

18
b

0.
47

±
0.

04
e

31
7.

06
±

12
.3

4a

47
7.

37
±

52
.5

3e

S3 M1

72
.8

9±
2.

45
e

6.
71

±
0.

99
a

0.
06

4±
0.

00
3ab

70
.1

9±
2.

01
g

0.
64

±
0.

02
a

29
7.

01
 ±

9.
38

ab

58
6.

75
±

40
.5

8bc
d

M2

77
.3

8±
4.

04
c

5.
51

±
0.

89
b

0.
06

5±
0.

00
5a

73
.5

5±
1.

71
f

0.
59

±
0.

03
b

31
4.

67
 ±

8.
42

a

53
7.

69
±

68
.5

7d

M3

81
.1

2±
4.

23
b

4.
62

±
0.

72
c

0.
06

0±
0.

00
6bc

82
.1

7±
1.

85
c

0.
54

±
0.

03
c

30
7.

47
±

5.
05

a

54
8.

32
±

76
.2

4cd

CV (%) 2.33 5.29 7.31 0.36 2.44 8.44 7.41
LSD (5%) 2.12 0.22 0.005 0.33 0.02 22.35 49.23
SEM 0.74 0.08 0.002 0.12 0.01 9.52 17.13

Where, WE = weeding efficiency, PD = plant damage, EFC= effective field capacity, FE = field efficiency, FC = fuel consumed, PI 
= performance index, EC= energy consumption; CV = Coefficient of  variation; LSD = least significance difference, Speed (S1= 1.5 
km/hr, S2= 2 km/hr, S3= 2.5 km/hr), soil moisture content (M1= 9.4%, M2= 12.34% and M3= 15.25%), values are mean ± 
SD. Mean values comparison arranged according to descending order with the same letter in a column are not significantly different at 5% 
level of  significance.

operation, forward speed and soil moisture (S×M), depth 
of  operation and soil moisture (D×M) on effective field 
capacity are presented in Tables 4, 5, and 6 respectively. 
However, the interaction effect of  variables (Speed×Depth), 
(Speed×Moisture), and (Depth×Moisture) were not 
significant influences (p>0.05) on the effective field 
capacity. The results of  the combined effect of  variables 
(D×S×M) are presented in Table 7. The results revealed 
that the combined effect of  forward speed, depth of  
operation, and soil moisture content had no significant 
effect on the effective field capacity at (p>0.05) level 
of  significance. In general, the effective field capacity 
increased with increasing forward speed and decreased 
with increasing soil moisture and depths of  operation.

Effect of  Soil Moisture and Machine Operational 
Parameters on the Field Efficiency
Effects of  forward speeds, depths of  operation, and soil 
moisture on the field efficiency of  the engine-operated 
weeder are presented in Figure 7. Field efficiency 
decreased with the increase in forward speed from 1.5 to 
2.5 km/hr and depth of  operation varied (from 0 to 20 
mm and 0 to 40 mm) whereas field efficiency increased as 
soil moisture content increased from 9.4 to 15.25 percent.
Table 3 shows that the average field efficiency of  the 
engine-operated weeder at forward speeds of  1.5, 2, and 
2.5 km/hr were found to be 82.35±5.38, 78.91±5.66, and 
75.31±5.48% respectively. The average field efficiencies at 
the soil moisture content of  9.4, 12.34, and 15.25% were 



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found to be 73.47±3.83, 78.21±4.71, and 84.88±3.28% 
respectively whereas the depths of  operation varied 
from 0 to 20 and 0 to 40 mm were obtained 78.94±6.21 
and 78.76±6.16%. However, the field efficiency of  
the weeder increased with an increase in soil moisture 
content and decreased with an increase in forward speed 
and operating depth.
Results indicated that the minimum field efficiency of  
68.54% was recorded with a 2.5 km/hr weeder operating 
speed at depth of  operation varied from 0 to 40 mm at 
9.4% soil moisture.  The maximum field efficiency of  
89.49% was recorded with a 1.5 km/hr weeder operating 
speed at depth of  operation varied from 0 to 20 mm and 
soil moisture content of  15.25%. The results revealed 
that the field efficiency decreased as the forward speeds 

increased for all soil moisture levels. The major reason 
for the reduction in field efficiency by increasing forward 
speed was due to the less theoretical time consumed in 
comparison with the other test plot. These findings are in 
close agreement with the result reported by Nkakini et al. 
(2010).The individual and combined effect of  operational 
parameters on the field efficiency were analyzed 
statistically and presented in Table 3. ANOVA revealed 
that forward speed (S) and moisture content (M) had 
significant effects on field efficiency at a 5% (p<0.05) level 
of  significance and each variable individually influenced 
the field efficiency. The significance was observed in the 
order of  speed (S) followed by moisture content (M) and 
depths of  operation (D).
The interaction effects of  operating speed and depth of  

Figure 7: Effect of  soil moisture and machine operational parameters on the field efficiency

Table 6: Interaction effect of  soil moisture content and depth of  operation on performance parameters
Speed (km/hr) Depth (mm) WE 

(%)
PD 
(%)

EFC (ha/hr) FE 
(%)

FC (l/hr) PI (ha/hp) EC (MJ/ha)

M1 D1

72
.0

5±
1.

58
d

4.
77

±
1.

17
b

0.
05

8±
0.

01
a

73
.8

0±
3.

96
e

0.
58

±
0.

04
a

26
5.

97
±

37
.2

9b

60
1.

25
±

67
.5

5b

D2

75
.2

7 
±

1.
60

c

4.
12

±
0.

69
d

0.
05

8±
0.

01
a

78
.0

0±
5.

01
d

0.
51

±
0.

05
c

27
6.

59
±

45
.6

7b

53
1.

32
±

66
.1

0c

M2 D1

78
.7

2±
3.

71
b

3.
70

±
0.

41
e

0.
05

5±
0.

01
ab

c

85
.0

2±
 3

.5
5a

0.
45

±
0.

06
e

27
1.

71
 ±

46
.8

1b

49
7.

09
±

 2
3.

66
c

D2

75
.0

9±
 1

.0
8c

5.
08

±
1.

90
a

0.
05

4±
0.

01
bc

73
.1

4±
3.

90
f

0.
59

±
0.

06
a

26
4.

58
 ±

31
.7

3b

65
3.

04
±

 5
0.

48
a



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M3 D1

79
.3

0±
2.

01
b

4.
35

±
1.

53
c

0.
05

3±
0.

01
c

78
.4

1±
4.

69
c

0.
53

±
0.

08
b

26
6.

86
 ±

33
.5

9b

60
5.

97
±

58
.4

2b

D2

86
.1

8±
3.

65
a

3.
73

±
1.

21
e

0.
05

7±
0.

01
ab

84
.7

5±
3.

18
b

0.
48

±
0.

08
d

31
2.

69
 ±

53
.7

8a

50
9.

19
±

10
9.

06
c

CV (%) 2.33 5.29 7.31 0.36 2.44 8.44 7.41
LSD (5%) 1.73 0.18 0.004 0.27 0.01 22.35 40. 20
SEM 0.60 0.06 0.001 0.09 0.004 7.78 13.99

Where, WE = weeding efficiency, PD = plant damage, EFC= effective field capacity, FE = field efficiency, FC = fuel consumed, PI 
= performance index, CV = coefficient of  variation, LSD = least significance difference, SEM = standard error of  the mean,   values 
are mean ± SD,  Depth (D1= 0 to 20 mm, D2= 0 to 40 mm),soil moisture content (M1= 9.4%, M2= 12.34% and M3= 15.25%),  
and Mean values comparison arranged according to descending order with the same letter in a column are not significantly different at 5% 
level of  significance

operation (S×D), forward speed and soil moisture content 
(S×M), depth of  operation, and soil moisture content 
(D×M) on the field efficiency are presented in Tables 4, 5, 
and 6 respectively. The results showed that the interaction 
effect of  variables (Depth×Moisture) had significant 
effects (p<0.05) on field efficiency. The interaction effect 
of  variables (Speed×Depth) and (Speed×Moisture) 
had significant effects (p<0.05) on field efficiency. The 
results of  the combined effect of  variables (Speed× 
Depth×Moisture) are presented in Table 7 and revealed 
that the combined effect of  depth of  operation, forward 
speed, and soil moisture content had significant effects on 
field efficiency at (p<0.05) level of  significance.

Effect of  Soil Moisture and Machine Operational 
Parameters on Fuel Consumption
Effects of  forward speed, depth of  operation, and soil 
moisture on fuel consumption of  the engine-operated 
weeder are presented in figure 8 and Table 7. The figure 
revealed that fuel consumption for depth of  operation 
from 0 to 20 mm and 0 to 40 mm with a forward speed 
of  1.5 km/hr was varied in the range of  0.53 to 0.39 l/
hr and 0.51 to 0.41 l/hr when the soil moisture content 
increased from 9.4 to 15.25% respectively. The fuel 
consumption for depth of  operation from 0 to 20 mm 
and 0 to 40 mm with a forward speed of  2 km/hr was 
varied in the range of  0.57 to 0.44 l/hr and 0.60 to 0.50 l/
hr when the soil moisture content was varied from 9.4 to 
15.25% respectively.
The fuel consumption for depth of  operation varied 
from 0 to 20 mm and 0 to 40 mm with a forward speed 
of  2.5 km/hr varied in the range of  0.62 to 0.52 l/hr 
and 0.65 to 0.57 l/hr, respectively. It is evident that fuel 
consumption increased as forward speed and depth of  
operation increased from 1.5 to 2.5 km/hr and from 0 to 

20 and 0 to 40 mm respectively.
The means comparison for fuel consumption in all 
treatments is shown in Table 7. Results indicated that 
the minimum value of  fuel consumption 0.39 l/hr was 
recorded at 1.5 km/hr weeder forward speed, depth of  
operation varied from 0 to 20 mm, and soil moisture 
content 15.25%. While the maximum value of  fuel 
consumption 0.65 l/hr was recorded at 2.5 km/hr weeder 
forward speed, depth of  operation of  0 to 40 mm, and 
soil moisture content of  9.4 percent. Hence, maximum 
fuel consumption was obtained at a maximum forward 
speed and depth of  operation. Similar results were 
reported by Manuwa et al., (2009).
The main effect of  operational parameters on fuel 
consumption was analyzed statistically and presented in 
Table 3. Analysis of  variance revealed that the influence 
in forward speed, depths of  operation, and moisture 
content had a significant influence on fuel consumption at 
(p<0.05) level of  significance. Each variable significantly 
affects the fuel consumption in the order of  speed (S) 
followed by depths of  operation (D). The interactive 
effect of  variables, forward speed and depth of  operation 
(S×D), forward speed and soil moisture content (S×M), 
depth of  operation and soil moisture content (D×M) 
on fuel consumption are presented in Tables 4, 5, and 
6 respectively. The results showed that the interaction 
effect in forward speed and depth of  operation(S×D) 
had significant effects (p<0.05) whereas the interaction 
effect (Depth×Moisture) and (Speed×Moisture) had no 
significant effects (p>0.05) on fuel consumption. Table 
7 shows the results of  the combined effect of  variables 
(Speed× Depth×Moisture). It revealed that the combined 
effect of  depth of  operation, forward speed, and soil 
moisture was not significant effects on fuel consumption 
at a 5% (p>0.05) level of  significance.



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Figure 8: Effect of  soil moisture and machine operation parameter on fuel consumption

Table 7: Combined effect of  forward speed, depth of  operation and soil moisture content on performance of  the weeder
Speed 
(km/hr)

Depth 
(mm)

Moisture 
(%)

WE 
(%)

PD 
(%)

EFC (ha/hr) FE 
(%)

FC (l/hr) PI (ha/hp) EC (MJ/ha)

1.5 20 M1

73
.2

0±
1.

52
gh

i

3.
32

±
0.

37
ij

0.
04

7±
0.

00
1hi

j

78
.8

9±
0.

82
g

0.
53

±
0.

01
ef

g

22
1.

63
±

4.
22

e

67
1.

50
±

35
.0

3ab

M2

76
.0

1±
1.

76
ef

g

3.
00

±
 0

.3
8jk

0.
04

6±
0.

00
1hi

j

84
.5

4±
0.

88
cd

0.
45

±
0.

00
h

22
6.

36
±

2.
06

e

59
1.

88
±

18
.1

9ab
cd

e
M3

78
.9

9±
3.

03
de

2.
78

±
0.

55
k

0.
04

6±
0.

00
1hi

j

89
.4

9±
0.

67
a

0.
39

±
0.

02
i

22
7.

36
±

0.
68

e

51
5.

28
±

30
.8

9ef
gh

40 M1

75
.7

4 
±

1.
36

ef
gh

4.
27

±
0.

12
fg

0.
04

5±
0.

00
2ij

73
.4

3±
0.

57
hi

0.
51

±
0.

03
g

22
6.

43
±

4.
96

e

62
2.

66
±

15
.7

5ab
c

M2

78
.9

8±
1.

35
de

3.
83

±
0.

10
h

0.
04

4±
0.

00
2i

81
.7

3±
1.

22
f

0.
43

±
0.

03
h

23
0.

04
±

12
.2

1de

60
1.

03
±

65
.2

9ab
cd

e

M3

90
.1

±
1.

17
a

3.
31

±
0.

09
ij

0.
05

2±
0.

00
8gh

i

85
.9

9±
0.

96
bc

0.
41

±
0.

02
i

27
6.

78
 ±

45
.9

0c

48
1.

71
±

12
.3

4gh



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2 20 M1

71
.9

7±
0.

34
hi

5.
12

±
0.

11
d

0.
05

9±
0.

00
6cd

ef

70
.6

7±
1.

29
j

0.
57

±
0.

01
cd

27
4.

49
 ±

20
.2

4c

58
1.

42
±

72
.2

4bc
de

f

M2

75
.3

6±
1.

43
ef

gh

4.
41

±
0.

08
ef

0.
05

8±
0.

00
5de

fg

74
.6

3±
1.

35
h

0.
50

±
0.

02
g

27
4.

79
 ±

19
.0

4c

52
2.

16
±

77
.9

1de
fg

h

M3

78
.8

5±
4.

24
de

3.
94

±
0.

11
gh

0.
05

5±
0.

00
5ef

g

81
.9

8±
1.

40
ef

0.
44

±
0.

03
h

26
7.

43
±

33
.6

4cd

49
4.

28
±

16
.4

0fg
h

40 M1

74
.7

4±
1.

27
fg

hi

3.
42

±
0.

27
i

0.
05

5±
0.

00
5ef

g

77
.4

3±
0.

45
g

0.
60

±
0.

02
bc

27
5.

09
 ±

22
.5

3c

65
2.

86
±

46
.1

3ab

M2

78
.6

0±
0.

51
de

3.
15

±
0.

02
ijk

0.
05

3±
0.

00
6fg

h

81
.2

3±
0.

60
f

0.
54

±
0.

01
de

f

26
9.

84
 ±

21
.4

8c

62
1.

42
±

92
.6

1ab
c

M3

84
.5

3±
3.

46
b

3.
11

±
0.

05
ijk

0.
06

6±
0.

00
8ab

c

87
.4

9±
0.

75
b

0.
50

±
0.

00
g

36
6.

69
 ±

34
.5

8a

46
0.

47
±

75
.9

6gh

2.5 20 M1

70
.9

7±
1.

93
i

5.
86

±
0.

51
c

0.
06

8±
0.

00
3a

71
.8

4±
1.

03
ij

0.
62

±
0.

01
ab

30
1.

79
 ±

12
.3

0bc

55
0.

83
±

0.
54

cd
ef

g

M2

74
.4

4±
1.

80
gh

i

4.
71

±
0.

13
e

0.
06

7±
0.

00
1ab

74
.8

4±
1.

22
h

0.
56

±
0.

02
de

32
8.

63
 ±

11
.2

5b

47
9.

91
±

38
.5

3gh

M3

78
.3

3-
±

4.
04

de

3.
96

±
0.

18
gh

0.
06

5±
0.

00
2ab

cd

83
.5

8±
1.

08
de

0.
52

±
0.

01
fg

32
0.

32
 ±

33
.3

2b

45
2.

15
±

24
.5

9h

40 M1

74
.8

1±
0.

51
fg

h

7.
56

±
0.

26
a

0.
06

2±
0.

00
bc

de

68
.5

4±
0.

95
k

0.
65

±
0.

02
a

29
2.

22
±

0.
57

bc

68
3.

60
±

70
.9

5a



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M2

80
.3

3±
3.

40
cd

6.
32

±
0.

17
b

0.
06

1±
0.

00
1bc

de

72
.2

6±
0.

89
ij

0.
61

±
0.

01
ab

30
0.

71
±

11
.6

8bc

59
5.

47
±

15
.9

1ab
cd

e

M3

83
.9

0±
2.

26
bc

5.
27

±
0.

02
d

0.
05

5±
0.

00
5ef

g

80
.7

5±
1.

17
f

0.
57

±
0.

03
cd

29
4.

62
±

38
.2

9bc

61
4.

94
±

32
.6

8ab
cd

CV (%) 2.23 5.29 7.31 0.36 2.44 8.44 7.41
LSD (5%) 3.00 0.38 0.01 0.47 0.02 38.71 69.62

Where, WE = weeding efficiency, PD = plant damage, EFC= effective field capacity, FE = field efficiency, FC = fuel consumed, PI 
= performance index, CV = coefficient of  variation; values are mean ± SD and mean values with the same letter in a column are not 
significantly different at 5% level of  significance; LSD = least significance difference, soil moisture (9.4, 12.34 and 15.25%) and Mean 
values comparison arranged according to descending order with the same letter in a column are not significantly different at 5% level of  
significance

Effect of  Soil Moisture and Machine Operational 
Parameters on Performance Index
Effects of  soil moisture, forward speed, and depth of  
operation on performance index are presented in Table 7 
and the result showed that the highest performance index 
of  366.69 ha/hp was obtained at 2 km/hr forward speed 
and depth of  operation varied from 0 to 40 mm. The next 
was at the forward speeds of  2.5 km/hr which recorded 
320.3 ha/hp performance index at the soil moisture 
content of  15.25%. However, the lowest performance 
index of  221.6 ha/hp was recorded at a forward speed of  
1.5 km/hr and the depth of  operation ranged from 0 to 
20 mm at soil moisture content 9.4 percent. 
From Figure 9, it was observed that performance index 
increased with increase in forward speed and depth of  
operation at all levels of  soil moisture content. However, 
the performance index increased as the soil moisture 
level increased at all the treatments because of  the high-
performance index at higher speeds. The same trend 
was observed at all levels of  soil moisture content and 
forward speeds.

Analysis of  variance (ANOVA) revealed that the effect 
of  forward speed (S) had a significant influence on the 
performance index at a 5% (p<0.05) level of  significance. 
It was also observed that there was no significant 
difference in performance index with depths of  operation 
(D) and soil moisture content (M) at (p >0.05) level of  
significance. 
The interaction effects in forward speed and depth 
of  operation (S×D), forward speed and soil moisture 
content (S×M), depth of  operation, and soil moisture 
content (D×M) on the performance index are presented 
in Tables 4, 5, and 6 respectively. The mean results 
observed from the data revealed that the interaction 
effect (Speed×Depth), (Depth×Moisture), and 
(Speed×Moisture) were not significantly influenced by 
the performance index at p>0.05 level of  significance. 
Analysis of  variance revealed that the combined effect 
of  forward speed, depth of  operation, and soil moisture 
content (Speed×Depth×Moisture) had no significant 
effects on the performance index at (p > 0.05) level of  
significance.



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Figure 9: Effect of  soil moisture and machine operational parameters on a performance index

Effect of  Soil Moisture and Machine Operational 
Parameters on Energy Consumption
The use of  energy per hectare for weeding operation by 
the engine-operated weeder was estimated at different 
intervals of  crop period. From Table 7, it is observed that 
the energy consumption for weeding operation at 1.5 to 
2.5 km/hr forward speed of  the engine operated weeder 

was in the range of  671.50 to 550.83 MJ/ha and 515.3 to 
452.15 MJ/ha with the depth of  operation varied from 
0 to 20 mm at 9.4% and 15.25% soil moisture content 
respectively. The result showed that energy consumption 
for weeding operation at 1.5 to 2.5 km/hr forward speed 
of  weeder was in the range of  683.60 to 622.66 MJ/ha 
and 548.30 to 452.2 MJ/ha with the depth of  operation 

Figure 10: Effect of  soil moisture and machine operational parameters on energy consumption



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varied from 0 to 40 mm at 9.4% and 15.25% soil moisture 
content respectively. Energy consumption at the initial 
stages of  the plant was less because of  obstruction-free 
travel between the rows. Whereas in the case of  a fully 
grown field, it was difficult to travel between the rows, 
and as a result, energy consumption is higher.
The mean comparison for energy consumption in all 
treatments is shown in Table 7. A result indicated that 
the minimum energy consumption of  452.2 MJ/ha was 
obtained by using a 2.5 km/hr weeder forward speed 
at depth of  operation varied from 0 to 40 mm and soil 
moisture content 15.25%. The maximum value of  energy 
consumption of  683.6 MJ/ha was obtained by using a 
1.5 km/hr weeder forward speed at depth of  operation 
varied from 0 to 40 mm and soil moisture content 9.4%. 
The results trend obtained and represented on Figure 
10 revealed that as forward speed and moisture content 
increased, energy consumption decreased. As the depth 
of  operation increased, energy consumption for the 
machine increased. Therefore, depth of  operation and 
energy consumption is a positive relationship.
The main and combined effects of  operational parameters 
on energy consumption were analyzed statistically and 
presented in Table 3. Analysis of  Variance (ANOVA) 
revealed from the tables that the effect of  forward speed 
(S) had no significant effects on energy consumption at 
(P>0.05) level of  significance. But there was a significantly 
different between higher and lower values of  forwarding 
speed. From the ANOVA table, depths of  operation (D) 
and moisture content (M) had a significant influence on 
energy consumption at(P<0.05) level of  significance 
and each variable individually influenced the energy 
consumption and also significance was observed in the 
order of  speed (S) followed depths of  operation (D). 
The interaction effects of  forward speed and depth 

of  operation (S×D), forward speed and soil moisture 
content (S×M), depth of  operation, and soil moisture 
content (D×M) on energy consumption are presented in 
Tables 4, 5, and 6 respectively.
The results observed from the data revealed that the 
interaction effect of  variables (Speed×Depth) and 
(Speed×Moisture) had significant effects on energy 
consumption at (P<0.05) level of  significance. The 
interaction effect of  variables (Depth×Moisture) had no 
significant influence (P>0.05) on energy consumption. 
The results of  the combined effect of  variables 
(Speed×Depth×Moisture) are presented in Table 7. 
Results revealed that the combined effect of  depth of  
operation, forward speed, and soil moisture content had 
no significant effects on energy consumption at (P>0.05) 
level of  significance.

Cost estimation of  Engine Operated Weeder
The engine-operated weeder was evaluated for the 
estimation of  cost of  operation and compared with the 
traditional method of  weeding. The total fabrication 
cost of  the weeding machine was 11,409.92 ETB. The 
calculated results of  fixed and variable costs were 8.638 
and 33.058 ETB/hr, respectively. The cost of  operation 
for an engine operated weeding and traditional method 
were 758 and 1920 ETB/ha respectively as shown in 
Figure 11. The saved cost of  weeding was 60.52% and 
the saved in time was 65.25% compared to manual 
weeding. Similar findings were reported by Sirmour 
and Verma (2018). Also, the cost and time of  operation 
increased as the days after sowing increased. The dense 
canopy prevents the easy working of  the weeder between 
the rows and increases the duration of  weeding. As the 
duration of  weeding increases, the field efficiency of  the 
weeder decreases as a result of  increased working hours.

Figure 11: Diagram showing the cost of  engine operated weeder and manual weeding operation

CONCLUSIONS 
This study was undertaken to evaluate the performance of  
an engine-operated weeding machine for the wheat crop. 
The engine-operated weeding machine was successfully 
evaluated. This test was conducted at different levels of  
operating parameters viz., depths of  operation (from 0 
to 20 and 0 to 40 mm), forward speed (1.5, 2, and 2.5 
km/hr), and soil moisture contents (9.4, 12.34, and 
15.25%). The performance of  the developed machine 

was evaluated in terms of  performance parameters. 
Based on measurements made and analysis carried out, 
the following conclusions were drawn from the study:
Soil bulk density decreased from 1561±0.87 to 1385±0.31 
kg/m3 with increased soil moisture content from 
9.40±0.11 to 5.25±0.26 percent. Bulk density decreased 
by 12.7% with an increase in soil moisture content from 
9.40±0.11 to 15.25±0.26 percent. Weeding efficiency is 
increased with increasing depth of  operation and soil 



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Am. J. Smart. Technol. Solutions 2(2) 21-42, 2023

moisture content and decreased with increasing weeder 
forward speed. It was optimum at 12.34 and 15.25 percent 
soil moisture as it gave a reasonably higher working range. 
Plant damage is low when operated at lower speeds, but 
high plant damage occurs when operated at high rates. 
The maximum value of  plant damage 7.56% was obtained 
with 2.5 km/hr at depth of  operation ranging from 0 to 
40 mm and 9.4 percent soil moisture content.
The maximum effective field capacity of  0.068 ha/hr was 
obtained at 2.5 km/hr weeder forward speed, a depth of  
operation ranging up to 20 mm, and soil moisture content 
of  15.25 percent. As the depth of  operation increased, 
the effective field capacity decreased. The effective field 
capacity increased with the increasing forward speed, as a 
result of  more area being covered in less time.
The field efficiency of  the engine-operated weeder is 
higher when operated at low forward speed and low 
depth of  operation within high soil moisture content.
Fuel consumption increased as the forward speed and 
depth of  operation increased and decreased as moisture 
content increased.
In conclusion, the performance of  the weeder was found 
to be optimum at 15.25 percent moisture content with 0 
to 40 mm depth of  operation at a forward speed of  1.5 
km/hr.
Hence, maximum weeding efficiency of  90.1 percent was 
recorded with lower plant damage of  3.31 percent while the 
effective field capacity, field efficiency, fuel consumption, 
performance index, and energy consumption were found 
to be 0.052 ha/hr, 85.99%, 0.41 l/hr, 276.78 ha/hp, and 
481.71 MJ/ha, respectively.
The costs of  weeding per hectare were observed as 758 
birr/ha and 1920 birr/ha for engine-operated weeder and 
traditional weeding methods, respectively.
Based on the findings, it is concluded that the 
performance of  the engine-operated weeder can be an 
efficient, effective, and economically possible option with 
the high prospect of  extending technology for small and 
medium-scale farmers. However, this plenty of  scope for 
improvement on the machine.

RECOMMENDATIONS
The prototype weeder performance evaluation revealed 
that it can be used successfully on the farm for weeding 
operations. To make the weeder applicable and acceptable 
among farmers, the following steps are recommended for 
further study and improvement on the machine: 

• The machine should be tested on different soil types,
• Different types of  weeding blades should be designed 

and tested, 
• Adaptation, modification, and performance test of  

the machine for multi-crops weeding operation should be 
done and

• Demonstration and scaling up of  this machine should 
be undertaken at the farm level.

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