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J.Valarmathi1, V.T.Kruthika2 

 

A Philosophical Study of Agricultural Image Processing Techniques 

 

J.Valarmathi1, V.T.Kruthika2 

1,2Assistant Professor,  PG and Research Department of Computer Science and Applications, 

Vivekanandha College of Arts and Sciences for Women(Autonomous), Tiruchengode – 637 205, India. 
1valarmathij@vicas.org, 2kiruthika@vicas.org 

 

 

 

Abstract 

The development of agriculture in China has been substantially aided by the development of image 

processing technologies. It is simple for people to comprehend the significance of image processing 

technology for agricultural development by presenting the application status of image processing 

technology in agriculture and its impact on agricultural production value. This research examines how 

image processing technology is used in agriculture on the basis of that information. This study first 

examines how image processing technology is used in the world of agriculture. Second, this study applies 

both classic machine recognition technology and image processing technology to crop pest identification, 

analyses their effects, and highlights the application effect of image processing technology in the 

agricultural industry. The findings indicate that this approach has a recognition rate of 86%, 89%, 91%, 

83%, 78%, and 79%, respectively. It is evident that the detection of crop diseases and insect pests is 

improved by the use of image processing technology. 

Keywords: Agriculture, Diseases, insect pests, Image Processing, machine recognition 

Abstrak 

Perkembangan pertanian di China telah banyak dibantu oleh perkembangan teknologi pemrosesan citra. 

Sangat mudah bagi orang untuk memahami pentingnya teknologi pengolahan citra untuk pembangunan 

pertanian dengan menyajikan status penerapan teknologi pengolahan citra di bidang pertanian dan 

dampaknya terhadap nilai produksi pertanian. Penelitian ini mengkaji bagaimana teknologi pemrosesan 

citra digunakan dalam pertanian berdasarkan informasi tersebut. Penelitian ini pertama kali mengkaji 

bagaimana teknologi image processing digunakan dalam dunia pertanian. Kedua, penelitian ini 

menerapkan teknologi pengenalan mesin klasik dan teknologi pemrosesan gambar untuk identifikasi 

hama tanaman, menganalisis pengaruhnya, dan menyoroti efek penerapan teknologi pemrosesan gambar 

dalam industri pertanian. Temuan menunjukkan bahwa pendekatan ini memiliki tingkat pengenalan 

masing-masing 86%, 89%, 91%, 83%, 78%, dan 79%. Jelas bahwa deteksi penyakit tanaman dan 

serangga hama ditingkatkan dengan penggunaan teknologi pemrosesan gambar. 

 Kata kunci: Pertanian, Penyakit, serangga hama, Image Processing, pengenalan mesin 

 

P-ISSN : 2715-2448 | E-ISSN : 2715-7199 

Vol.4 No.2 July 2023 

Buana Information Technology and Computer Sciences (BIT and CS) 

mailto:1valarmathij@vicas.org
mailto:kiruthika@vicas.org


Vol. 4, No.2 July 2023 | 33  

 

I. Introduction 

The first use of digital image processing was in 1920. The time it takes for a photograph to be 

transferred from the Atlantic side has decreased from more than one week to three hours since the 

implementation of Batland's cable image transmission system [1-2]. Image processing technology has 

advanced with the quick development of computers [3–4]. Humans rely heavily on images to exchange 

and collect information. Every element of human work and life has steadily been impacted by the 

application of picture analysis and processing. The development of this technology is developing by leaps 

and bounds, and its application domains are continuously expanding along with the breadth of human 

activities expanding and the ceaseless appearance of scientific theories, and its great achievements are 

also accumulating day by day [5-6]. 

The efficiency of physical labour was historically quite poor, and agricultural growth trends were 

difficult to manage [7]. According to their work experience, the staff discovered that monitoring the 

growth of crops can provide them with information about the soil, water, and air humidity, which they can 

use to improve subsequent work appropriately and ensure that the crops grow normally and produce 

higher overall output values [8]. As science has advanced quickly, digital image processing technology 

has become increasingly widely used in agriculture. Examples include the detection of pesticides in 

vegetables, the control of pests in crops, the identification of crop growth trends, and the colour coding of 

crops. Digital image processing technology plays an indispensable role [9-10]. 

For the development of the agricultural field, it is therefore crucial to examine the image processing 

technology's current state of use. This research paper provides a quick overview of image processing 

technologies. Four categories of image processing technology methods are covered in this article: image 

denoising, image rectification, image segmentation, and picture feature extraction. The implementation of 

image processing technologies in the sphere of agriculture is also examined in this research. Additionally, 

the impacts of applying standard machine recognition technology and image processing technology to 

crop pest identification are compared in order to emphasise the application effect of image processing 

technology in the agricultural industry. The findings of the trial demonstrate how much more effective the 

image processing-based detection method is. 

II. Overview of Image Processing Technology 

Image denoising, correction, segmentation, feature extraction, and other techniques are all examples 

of image processing technology. 

A. Image Denoising 

Noise is introduced and the image signal is contaminated during the processes of image acquisition 

and transmission due to the influence of equipment and external variables. Denoising is a well-known and 

fundamental issue in image processing and analysis. Gaussian white noise and salt-and-pepper noise are 

two types of typical picture noise. 

B. Image Correction 

Image skew correction is the process of recovering the image that does not comply with the standard 

before processing the image. Image skew is primarily caused by the deviation of scanning layout during 

the process of acquisition. It is highly typical to have diverse image effects during the image acquisition 

process, particularly the image tilt, due to the varied technology used by each individual. If there is no 

pertinent tilt correction applied before processing an image with skew, the final automatic recognition 

will be severely hampered. The image is first examined, and the degree of image skew is determined. 

Then, to perform the picture rectification, the same angle is rotated in the original coordinate in 



Vol. 4, No.2 July 2023 | 34  

 

accordance with the determined tilt angle. The projection method, nearest neighbour method, Hough 

transform, Radon transform, and more methods are currently available. 

C. Image Segmentation 

One of the challenges in image processing and a significant issue in picture analysis is image 

segmentation. Nevertheless, picture segmentation is the initial stage of the image processing process. The 

retention and display of image features after segmentation have a significant impact on the subsequent 

image processing. Image segmentation can be considered to have a direct impact on the outcomes of 

image analysis and processing, and effective image segmentation will set up the final image processing on 

a solid foundation. The most well-known and often used segmentation technique is threshold-based 

segmentation. 

D. Image Feature Extraction 

The pixel (x, y) in the image is assumed to be integrated throughout the image feature extraction 

process, and the total of all pixels above the pixel is represented by a sub integration system. 

III. Methodology 

This research applies image processing technology and classical machine recognition technology to 

crop pest identification, respectively, and evaluates their results in order to highlight the application effect 

of image processing technology in the agricultural industry. 

A. Subjects 

Two crops that were the same size were chosen as the experimental objects in this paper. The two 

fields' agricultural development patterns were comparable, they were in the same area, and other 

circumstances were largely the same. 

B. Test Object 

This investigation's goal is to find crop pests. Pests and illnesses, such as the alfalfa armyworm, blue 

grey butterfly, bean grey butterfly, flame noctuid, and bean leaf roller, were chosen as the detection items 

in this study. 

C. Detection Index 

The detection index in this research is based on the degree of recognition for the two approaches. 

The detection effect improves with higher recognition degrees. 

 

IV. Results and Discussions 

A. Analysis of Image Processing Technology in Agricultural Field 

In this paper, the application of image processing technology in agricultural field is analyzed, 

 

 

Agriculture uses image processing technology extensively. The prevalent uses of image processing 

technology in agriculture are examined in this research. According to Table 1 and Figure 1, the current 

usage of image processing technology in agriculture focuses mostly on crop growth monitoring, disease 



Vol. 4, No.2 July 2023 | 35  

 

and insect pest diagnosis, nutritional status monitoring, crop maturity monitoring, and crop colour 

identification. In order to effectively assess the growth state of crops, 29.3% of them are utilised to 

monitor crop growth. 

 

Figure 1. Aplication of Image Processing Technology in Agriculture 

14.5% of cases of illnesses, insect pests, and weeds are diagnosed. It is used to provide crops with 

additional nourishment and water as needed. 16.7% of applications for crop maturity monitoring are made 

with the goal of increasing crop production effectiveness. Crop colour identification employed 17.4% of 

the crop's colour, and its application goal was to categorise crops. This study then goes into further detail 

about this. 

B. Monitoring crop growth 

In general, computer vision technology can be utilised extensively during the entire process of plant 

growth, monitoring plant growth and development, and if abnormal conditions are identified, it is useful 

to remedy the problem as soon as possible. Crop leaf thickness, rhizome length, and water content are the 

key monitoring targets, and all pertinent information is meticulously documented. When paired with final 

data, we can assess crop production comprehensively; when combined with crop fruit photographs, we 

can determine at any moment whether the fruits are mature, lacking in food and water. 

C. Diagnosis of diseases, insect pests and weeds 

In addition to giving crops the nutrients they require to grow properly on schedule, it is also essential 

to deal with the diseases, pests, and weeds that impede crop growth. In the past, this component of work 

was greatly influenced by agriculture's poor production value. With the development of image processing 

technology, its use in agricultural work has continued. This liberates the laborious statistical work of 

crops and significantly reduces the difficulty of staff job. In order for the personnel to carry out preventive 

work, image processing can be used to forecast potential issues that could arise during the early stages of 

crop growth. 

D. Monitoring nutritional status 

Real-time photographs of crop leaves and rhizomes can be captured using image processing 

technology during the growing process, allowing for the monitoring of crop leaf size and rhizome 

thickness. Through the monitoring data, crop-related data can be compared to the average state to 

determine whether there are any nutritional deficiencies or other problems. This allows for the timely 



Vol. 4, No.2 July 2023 | 36  

 

development of an effective remediation plan, which ensures that crops grow normally and receive 

enough water and nutrition. 

 

 

E. Monitoring maturity 

The crop fruit picture points may be gathered from a wide range using browser image and other 

relevant analysis technologies, and the crop growth and maturity can be precisely determined by the 

obtained parameters. These technologies allow us to assess the fruit's maturity and create efficient 

defences. For instance, the earlier-maturing fruit can be plucked earlier to prevent decay and other 

decline, which is helpful for the systematic management of the fruit situation and increases the 

effectiveness of production. 

F. Identify crop colors 

The visual characteristic of colour makes it simple to assess the quality of crops. Digital imaging 

technology's gathering and analysis of colour traits transform into a detection method to determine 

whether the crops are of excellent quality. A theoretical basis was established for the systematic and 

uniform development of maize quality inspection by using the detection of corn quality as an example. 

The analysis of multiple image indicators, such as colour saturation and sensitivity of corn kernels, can be 

used as the quality grading standard of corn kernel sweetness and fineness. 

G. Analysis of Image Processing Technology in Agricultural Field 

The traditional machine recognition technology and the image processing technology detection 

method are used to identify crop diseases and insect pests in order to study the application effect of image 

processing technology in agricultural fields, and the application effect of the two methods is compared. 

 

 
 

In this study, six different illnesses and insect pests—the bean leaf roller, bean leaf borer, flame 

armyworm, blue grey butterfly, bean grey butterfly, and alfalfa armyworm—were chosen as detecting 

objects. Table 2 and Figure 2 show that there are some discrepancies in the recognition rates of the two 

distinct detection methods for crop diseases and insect pests. 



Vol. 4, No.2 July 2023 | 37  

 

 
The classic machine recognition technique among them has a recognition rate of 65%, 71%, 74%, 

63%, 64%, and 62%, respectively. Additionally, this approach had a recognition rate of 86%, 89%, 91%, 

83%, 78%, and 79%, respectively. The detection approach utilising image processing technology can 

more successfully detect illnesses and pests based on the recognition data of the two detection methods. 

 

V. Conclusions 

In order to achieve the modernization of agriculture level, digital image processing technology is 

widely applied in all facets of agriculture. Despite a late start, the use of image processing technologies in 

Chinese agriculture still produced positive outcomes. In this essay, the use of image processing 

technology in agriculture was examined, and its effects were researched. This study demonstrates how 

image processing technology is mostly employed in agriculture for the following five purposes: crop 

growth monitoring, disease and insect pest detection, maturity monitoring, and crop colour identification. 

Additionally, the use of image processing technology in agriculture has produced positive outcomes, 

which encourages the growth of agriculture.  

 

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Journal Of Innovative Research In Electrical, Electronics, Instrumentation And Control 

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[3]   Nan Xu, "Image Processing Technology in Agriculture", The 2nd International Conference on 

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[4]   Kousalya,Kabilesh,Mohan Prasath,Jayapriya, "Image Processing Techniques In Agriculture For 

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[6]     Prakash, Saravanamoorthi, Sathishkumar, Parimala, "A Study of Image Processing in Agriculture", 

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