Global Sustainability Research                                                    ISSN: 2833-986X                                                 
https://doi.org/10.56556/gssr.v3i1.636 

                                                                  

 

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REVIEW ARTICLE  

A Systematic Review of Geographic Information Systems (GIS) in Agriculture 

for Evidence-Based Decision Making and Sustainability 

 

Asif Raihan 
 

Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia 

 

Corresponding Author: Asif Raihan: asifraihan666@gmail.com 

Received: 07 September, 2023, Accepted: 03 January, 2024, Published: 07 January, 2024 

 

Abstract 

The aim of this study was to consolidate current information on the utilization of Geographic Information Systems 

(GIS) and Remote Sensing (RS) in the agricultural sector, with a focus on their role in promoting evidence-based 

policies and practices to enhance agricultural sustainability. Additionally, this review sought to identify the 

challenges hindering the widespread adoption of GIS and RS applications, particularly in low- and middle-income 

nations. This study employed the methodology of systematic literature review. The findings indicate that the 

utilization of GIS technology in the agricultural sector has experienced a notable increase over the past few years. 

The primary areas of use for GIS that have been identified encompass crop yield estimation, assessment of soil 

fertility, monitoring of cropping patterns, evaluation of drought conditions, detection and management of pests and 

crop diseases, implementation of precision agriculture techniques, and management of fertilizer and weed control. 

GIS technology possesses the capacity to augment the sustainability of agriculture by incorporating the spatial 

aspect of agricultural practices into agricultural policies. Furthermore, the potential of GIS in facilitating evidence-

based decision making is expanding. Given the escalating peril of climate change on agriculture and food security, 

there exists a heightened imperative to include GIS into policy formulation and decision-making processes to 

enhance the sustainability of agricultural practices. The findings of this study might be beneficial in informing the 

development of policies that effectively integrate sustainable and climate-smart practices in agriculture. 

 

Keywords: GIS; Remote sensing; Agri-spatial; Decision making; Policy integration; Sustainable agriculture 

 

Introduction  

 

The global demand for food has experienced a significant growth and is projected to further climb by 59-98% by 

the year 2050, as stated by Elferink and Schierhorn (2016). Nevertheless, there is a mounting apprehension 

regarding the inability of agricultural food production systems to meet the substantial demand, particularly in 

impoverished nations, hence exacerbating the issue of food insecurity (Bjornlund et al., 2022; Akter et al., 2023; 

Raihan, 2023a). The presence of inefficiencies within food production systems has been identified as a contributing 

factor to the issue of food insecurity (Ali et al., 2022; Viana et al., 2022; Raihan, 2023b). Effectively promoting 

enhanced food production while ensuring the preservation of land and water resources, energy sustainability, and 

environmental integrity poses a significant challenge that necessitates attention from governmental bodies and 

policymakers (Brgum et al., 2020; Zhang et al., 2023; Raihan, 2023c). 



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In numerous low- and middle-income countries (LMICs), the primary source of food production is predominantly 

situated in rural areas, where smallholder and subsistence farmers play a dominant role (Moustier et al., 2023; 

Raihan, 2024a). In order to promote the sustainability of smallholder farmers, it is crucial to empower them with 

practical knowledge (Raihan et al., 2022a; Ghosh et al., 2023). 

This information should help farmers to make decisions based on evidence and effectively execute strategies that 

can enhance their farm productivity and overall sustainability (Raihan et al., 2022b). In order to address the 

limitations of traditional subsistence production practices, it is imperative to adopt sustainable production 

approaches that promote efficiency and improved agronomic practices (Raihan et al., 2022c). The strategies 

encompassed in this approach consist of the cultivation of crops that are resilient to climate conditions, the 

utilization of crop types that have high yields, the implementation of methods for predicting crop yields, the 

adoption of integrated pest management techniques, and the incorporation of biodiversity solutions into sustainable 

food production systems (Giri, 2023; Raihan et al., 2023a). In order to implement these innovative interventions 

effectively, it is imperative to possess complete and current datasets encompassing both spatial and non-spatial 

information. Additionally, the utilization of advanced GIS technologies capable of amalgamating and synthesizing 

various types of data, such as spatial, social, demographic, economic, and environmental, is crucial (Kross et al., 

2022; Raihan et al., 2023b). The synthesis process would yield geographical knowledge that is grounded in 

evidence, so enhancing our comprehension of agricultural sustainability and facilitating more effective policy-

making and decision-making endeavors (Raihan et al., 2023c). The current advancements in GIS, RS, and 

Geographic Positioning Systems (GPS) technology offer a potential avenue for obtaining and implementing high-

resolution satellite imagery and digital spatial data (Trivedi et al., 2022; Raihan, 2023d). Spatial data in the field 

of agriculture have been essential in examining the spatial connections between social, physical, agroecological, 

and environmental factors, and their impact on the sustainability of agricultural practices (Raihan et al., 2023d). 

The utilization of GIS technology offers users a comprehensive array of tools and methodologies for managing 

geospatial information. This technology enables users to gather, store, merge, interrogate, present, and examine 

geospatial data across different levels of detail (Avanidou et al., 2023; Raihan & Tuspekova, 2022a). Remote 

sensing technology is utilized to obtain images and gather various data pertaining to crops and soil. This is achieved 

through the utilization of sensors that are installed on diverse platforms such as satellites, airborne remote sensing 

devices (including manned drones and unmanned aerial vehicles), as well as ground-based equipment. 

Subsequently, these acquired data are processed by computers to support agricultural decision-making systems 

(Awais et al., 2022; Raihan & Tuspekova, 2022b; Huang et al., 2023). 

The examination of agriculture's geographical context can be approached by considering the varying levels of 

access that farmers have to livelihood capitals, local resources, and critical infrastructure and services within a 

certain geographic area (Wang et al., 2023; Raihan & Tuspekova, 2022c). The deconstruction of data encompassing 

many aspects inside a GIS can be achieved through the organization of nested spatial layers. These layers are 

established based on local geography, with geographic coordinates obtained through the utilization of 

GPS technology (Raihan & Tuspekova, 2022d; Warren et al., 2023). The geographical layers can be further 

subjected to processing and analysis inside a GIS platform, enabling the exploration of various aspects such as 

crop and soil conditions, spatial relationships, crop trend prediction, land-use change monitoring, pest surveillance, 

and biodiversity protection (Raihan & Tuspekova, 2022e; Taiwo et al., 2023). Furthermore, these tools can also be 

employed to effectively delineate and expose spatial barriers that hinder agricultural productivity, or even generate 

novel insights to enhance agricultural sustainability (Raihan & Tuspekova, 2022f). 

In contemporary times, policymakers have shown a growing interest in exploring the potential of advanced GIS, 

RS, and GPS technologies to enhance agricultural productivity and optimize production practices. This interest 

stems from the recognition of the escalating intricacy inherent in agricultural production systems (Raihan & 



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Tuspekova, 2022g; Yadav et al., 2023). The utilization of GIS in the field of agriculture has witnessed a notable 

rise, leading to enhanced prospects for the establishment of more advanced spatially explicit frameworks. These 

frameworks serve to facilitate the construction of dynamic agricultural databases and interactive systems (Raihan 

& Tuspekova, 2022h; Chen et al., 2023). These database systems enable users to engage with farm data that is 

geographically referenced in real-time, offering accurate positional data and so enhancing decision-making 

frameworks. Emerging applications of GIS have been observed in the agricultural sector. The aforementioned areas 

encompass precision agriculture, crop yield forecasting, automated farm systems, climate change detection, and 

real-time monitoring of agricultural production (Karunathilake et al., 2023; Raihan & Tuspekova, 2022i; Khan et 

al., 2023). These technologies possess the potential to enhance agricultural productivity and ensure food security. 

Several recent comprehensive literature evaluations have been done to elucidate and consolidate the diverse 

applications of GIS, RS, and GPS technologies in the agricultural sector. Systematic mapping analysis has been 

conducted by García-Berná et al. (2020) to examine the prevailing trajectory and emerging prospects of remote 

sensing techniques in the field of agriculture. The researchers observed a notable rise in the adoption of 

RS technologies for the collection and extraction of georeferenced information gathered from imagery from 

satellites and unmanned aerial vehicles (UAVs) in their investigation. Spatial data derived from these advanced 

technologies has been utilized in several domains such as estimating crop growth and production, extracting 

parameters related to cropland, detecting weeds and diseases, and monitoring the availability of water and nutrients 

in plants. The authors did not provide an elaboration on how this application could be used to enhance spatial-

based agriculture policymaking. Moreover, Al-Ismaili (2021) emphasized the utilization of RS and 

GIS methodologies in the field of precision agriculture. These techniques have proven to be effective in the 

accurate mapping, identification, and categorization of greenhouses using aerial imagery and satellite data. The 

potential integration of this technique into the improvement of policymaking was not discussed. Weiss et al. (2020) 

conducted a meta-review that emphasized the growing advancements in RS and its relevance to several domains 

such as crop breeding, crop yield forecasting, agricultural land use monitoring, and biodiversity loss. Sharma et al. 

(2018) examined the utilization of GIS data applications in the advancement of precision agriculture. The proposed 

framework, referred to as "Big GIS Analytic," was put up by the authors as a means to provide guidance on the 

appropriate utilization of large-scale GIS data within the context of the agriculture supply chain. The framework 

proposed by the authors establishes a theoretical basis for enhancing the efficacy of GIS data utilization in the 

agricultural sector, with the ultimate goal of increasing productivity. These studies contribute to the comprehension 

of the advancements in the uses of GIS and RS in agricultural production systems. Nevertheless, the existing 

systematic evaluations appear to lack precise information on how GIS and RS technologies might effectively 

promote the integration of the spatial aspect of agriculture into policy frameworks and actions. 

There is a growing need for evidence-based decision-making to aid policymakers in evaluating the specific 

requirements of farmers at the local level, enhancing production and supply value chains, and implementing 

spatially targeted interventions (Raihan & Tuspekova, 2022j). This study seeks to consolidate current information 

on the utilization of GIS and RS in the agricultural sector. The objective is to explore how these technologies might 

contribute to evidence-based policymaking for the enhancement of agricultural sustainability. Additionally, the 

review aims to highlight the challenges and barriers that hinder the widespread adoption of GIS and RS 

applications, with a special focus on LMICs. The present investigation explored the contemporary and prospective 

viewpoints regarding the incorporation of GIS into policies aimed at promoting agricultural sustainability. The 

primary contributions of this study are to furnish researchers and policymakers with empirical data about the 

utilization of GIS technology within the agricultural sector. This evidence sheds light on how GIS technology has 

enhanced agricultural production methods and offers insights into its potential adoption for improving evidence-



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based decision-making processes and policies. The results of this study could potentially contribute to the 

formulation of policies that successfully include sustainable and climate-smart agricultural practices. 

 

Methodology 

 

This paper aims to present a concise overview of the utilization of GIS and RS techniques in agriculture, with a 

focus on the most current research findings. The present study employed the systematic literature review 

methodology as suggested by Tawfik et al. (2019). According to Benita (2021), the systematic literature review 

framework is considered to be a dependable approach. A preliminary review of the literature was conducted to 

identify pertinent articles, validate the proposed idea, avoid redundancy with previously covered issues, and ensure 

the availability of sufficient articles for conducting a comprehensive analysis of the subject matter. Moreover, the 

focal point of the themes was to explore the application of GIS and RS across multiple agricultural segments. 

According to Tawfik et al. (2019), it is crucial to enhance the retrieval of results by acquiring a comprehensive 

understanding and familiarity with the study topic through the examination of pertinent materials and active 

engagement in relevant debates. This objective can be achieved by conducting a thorough examination of pertinent 

literature and actively participating in pertinent academic conversations.  

An in-depth review was undertaken on a total of 56 scholarly articles obtained from the Scopus, Web of Science, 

and Google Scholar databases. The manual search results were initially enhanced and polished through the process 

of examining the reference lists of the included publications. Subsequently, the investigation also engaged in the 

practice of citation tracking, a method involving the systematic monitoring of all the scholarly works that reference 

each of the papers incorporated in the collection. In conjunction with the manual search, an online search of 

databases was also undertaken as an integral component of the comprehensive search process. The evaluation and 

categorization of scholarly articles were performed by taking into account their specific domains of application. 

The publications were categorized based on the major research topics on GIS and RS application in agriculture. 

Following the identification of each topic, a comprehensive review was conducted, primarily on the presented 

issues. The emphasis was given to the current information on the utilization of GIS and RS in the agricultural 

sector, with a focus on their role in promoting evidence-based policies and practices to enhance agricultural 

sustainability. Additionally, this review sought to identify the challenges hindering the widespread adoption of GIS 

and RS applications, particularly in low- and middle-income nations. 

This study exclusively relied on research articles published in peer-reviewed journals, ensuring the reliability and 

validity of the findings. The publications were thereafter evaluated to ascertain whether their main subject matter 

bore a resemblance to that of the present inquiry. Priority consideration was given to papers published after the 

year 2010. The primary justifications for the elimination of papers are their lack of relevance, duplication, 

incomplete textual content, or limited presence of abstracts. The predetermined exclusion criteria were established 

to safeguard the researcher against potential biases that could influence their findings. Figure 1 illustrates the 

progression of review criteria employed for the selection of suitable documents for review analysis. Moreover, 

Figure 2 presents the systematic review procedure utilized in the current study. After the research topic was chosen, 

this study proceeded to find and locate relevant articles, do an analysis and synthesis of diverse literature sources, 

and create written materials for article review. The synthesis phase encompassed the collection of a wide range of 

publications, which were subsequently amalgamated into conceptual or empirical analyses that were relevant to 

the finalized research. 

 



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Figure 1. The development of criteria for the selection of documents. 

 

 
Figure 2. The procedure of systematic review conducted by the study. 

 

Results and Discussion 

 

GIS in agriculture and policy implications 

 

The principal study themes within the current body of literature pertaining to the utilization of GIS in the field of 

agriculture are depicted in Figure 3. There are seven distinct application areas of GIS within the field of agriculture. 

These areas encompass crop yield estimation and forecasting, assessment of soil fertility, analysis of cropping 

patterns and agricultural monitoring, evaluation of drought conditions, detection and control of pests and crop 

diseases, implementation of precision agriculture techniques, and management of fertilizers and weeds. GIS can 

assist in implementing agricultural policies through several means. GIS can facilitate the enforcement of 

regulations and provide a visual representation of the economic consequences of policy (Boda et al., 2023; Raihan 

& Tuspekova, 2022k). GIS has the capability to uncover environmental health concerns as well as issues related 

to animal health and welfare (Niloofar et al., 2021; Raihan & Tuspekova, 2023a). GIS has the ability to mediate 

land use disputes (Yanbo et al., 2023; Raihan & Tuspekova, 2023b). GIS software has the capability to examine 



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soil data and monitor the advancement of a project (Alaloul et al., 2021; Raihan & Voumik, 2022a). GIS can 

enhance agricultural productivity and minimize expenses by facilitating improved land resource management 

(Diehl et al., 2020; Raihan & Voumik, 2022b). GIS enables organizations to assess crop health by utilizing data 

obtained from satellite imagery, thermal sensors, and multispectral cameras (Olson & Anderson, 2021; Raihan, 

2023e; Sultana et al., 2023a). GIS software aids farmers in identifying optimal sites and environmental factors for 

cultivating various crops (Roy et al., 2023; Sultana et al., 2023b). GIS-based models offer empirically supported 

approaches to enhance soil quality management (Tsegaye & Bharti, 2021; Raihan, 2023f; Voumik et al., 2022; 

Himu & Raihan, 2023). The subsequent sub-sections provide a description of the main research areas explored in 

the current body of literature pertaining to the application of GIS and RS in the field of agriculture. 

 

 
Figure 3. Major research topics on GIS application in agriculture. 

 

Crop yield estimation and forecasting  

 

The monitoring of crop growth and the early forecasting of crop yield in agricultural fields are crucial processes 

for the purpose of food security planning and the prediction of agricultural economic returns (Al-Adhaileh et al., 

2022; Raihan, 2023g; Voumik et al., 2023a). According to Dhanaraju et al. (2022), the ongoing progress in RS and 

GIS technologies has resulted in enhancements to the methods and approaches employed for monitoring 

agricultural development and calculating crop yields. Figure 4 depicts the utilization of remote sensing data to 



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prepare crop yield map. Numerous research have showcased the utilization of combined GIS and RS technology 

for the purpose of estimating crop production. Memon et al. (2019) showcased the efficacy of integrating 

multispectral Landsat satellite images and comparing several remote sensing-based spectral indices in quantifying 

the proportion of wheat straw cover. Furthermore, the study successfully determined the impact of wheat straw 

cover on rice crop yields. The acquisition of knowledge can contribute to the development of long-term strategies 

for promoting agricultural sustainability within rice-wheat cropping systems. Hassan and Goheer (2021) indicate 

that it is possible to accurately estimate wheat crop yield in advance of harvesting by utilizing vegetation indices 

derived from moderate resolution imaging spectroradiometer satellite imagery, coupled with crop yield data and a 

GIS modeling approach. In a further investigation, Muslim et al. (2015) employed a GIS-based framework that 

linked climate modeling with environmental policy considerations. This approach offered a pragmatic solution for 

predicting rice crop yield. The study utilized a comprehensive model that integrated data at the regional level, 

including crop-level data, soil data, farm management data, and climatic data, in order to estimate geographic 

differences in crop production.  

 

 
Figure 4. Utilization of remote sensing data to prepare crop yield map. 

 

Similarly, Al-Gaadi et al. (2016) employed the extraction of the normalized difference vegetation index and soil-

adjusted vegetation index from Landsat satellite pictures obtained throughout the various growth stages of potato 

plants in order to forecast the yield of potato tubers. Crop yield forecasting models that utilize GIS and RS have 

the potential for broader application in providing valuable insights for spatially oriented agricultural strategies. As 

demonstrated by the findings of these models, it is possible to develop policy interventions that target the various 

factors influencing crop yields, such as farm management techniques, plant health, water availability, weather 

conditions, terrain, altitude, and policy intervention itself (Mann & Warner, 2017; Raihan, 2023h; Voumik et al., 

2023b). The ability to accurately predict crop yields well in advance of harvest is of utmost importance, particularly 



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in a geographical area that is known for its unpredictable climate conditions. The monitoring of agricultural crop 

growth conditions and the forecast of prospective crop yield play a crucial role in the strategic planning and policy 

formulation for ensuring food security and predicting agricultural economic returns (Abhishek et al., 2023; Raihan, 

2023i; Voumik et al., 2023c). One potential avenue for addressing this issue is the formulation of policies aimed 

at enhancing both the production and sustainability of agriculture (Pawlak & Kołodziejczak, 2020; Raihan & Said, 

2022). In order to address the challenge of feeding a growing population in LMICs, it is imperative for agricultural 

production systems to focus on narrowing the gap between the present yields achieved by farmers and the potential 

yields that can be attained in rainfed subsistence farming systems. Hochman et al. (2013) aimed to resolve the 

discrepancy between actual and potential wheat yields. To achieve this, the researchers constructed a 

comprehensive model that incorporated statistical yield and cropping area data, remotely sensed data, cropping 

system simulation, and GIS mapping. This integrated approach allowed for the assessment and mapping of wheat 

yield gaps. 

 

Assessment of soil fertility  

 

The evaluation of soil quality holds significant importance in the development of sustainable agricultural strategies 

aimed at addressing the existing disparity between food supply and demand, hence contributing to the resolution 

of food security concerns (Wijerathna-Yapa & Pathirana, 2022; Raihan & Himu, 2023). The utilization of 

RS datasets and GIS spatial modeling approaches presents novel prospects for quantifying and assessing soil 

quality across various spatial extents (Guo et al., 2023; Raihan et al., 2023e). Shokr et al. (2021) devised a soil 

quality model that incorporates the physical, chemical, and biological attributes of soil in a spatially 

explicit manner. This model was further enhanced by merging a digital elevation model with a Sentinel-2 satellite 

image, resulting in the generation of digital soil maps. Abdelfattah and Kumar (2015) elucidate the utilization of a 

GIS-enabled web-based soil information system. This system offers a comprehensive soil database that 

incorporates descriptive, quantitative, and geographic data, all presented through a user-friendly interface. The 

application of the technique was utilized to assess the capacity of soils in terms of their suitability for plant 

cultivation and effective maintenance. Abdellatif et al. (2021) employed GIS and RS technologies to construct a 

spatial model aimed at evaluating soil quality. The researcher's model integrated four primary indicators of soil 

quality, namely the soil fertility index, soil physical index, soil chemical index, and geomorphological 

characteristics index. Additionally, the model employed GIS conventional kriging spatial interpolation techniques 

to provide a comprehensive map of the soil quality index. The utilization of these GIS-based models offers 

empirically supported approaches for enhancing soil quality management (Raihan et al., 2023f). The 

implementation of this approach would provide decision-makers, policymakers, land-use planners, and agricultural 

professionals with the means to effectively oversee soil resources and promote the sustainable utilization of 

agricultural lands based on their inherent capabilities (AbdelRahman et al., 2021; Tunçay et al., 2021; Raihan et 

al., 2023g). Therefore, the evaluation of soil quality indicators holds significance in the context of sustainable 

agricultural policies and practices, as well as in the pursuit of food security.  

 

Analysis of cropping patterns and agricultural monitoring  

 

In the context of climate variability, the utilization of agricultural crop monitoring analysis has the potential to 

assist governmental officials and farmers in formulating strategic approaches for crop planning and designing that 

are responsive to fluctuations in water resources (Njoya et al., 2022; Raihan et al., 2023h). Agricultural monitoring 

systems incorporate various geographic data sets and cropping system models into computational algorithms in 



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order to spatially calculate and simulate optimal scenarios for site-specific conditions in crop production (Luo et 

al., 2023; Raihan et al., 2023i). Shafi et al. (2020) have created a crop monitoring system that combines geospatial 

data acquired through high-resolution remote sensing with a web-based GIS geoportal interface. The existing 

literature provides evidence for the distinctiveness of integrating GIS and RS into a tool designed for crop selection 

and rotation analysis at the farm level, with the aim of enhancing decision-making in crop management (Kumar & 

Babu, 2016; Raihan et al., 2023j). The modeling of cropping patterns is influenced by the availability of irrigation 

water, which is, in turn, influenced by climate variations and policy regarding the extraction of irrigation water. 

Wang et al. (2011) integrated GIS with irrigation water availability simulation models in order to examine cropping 

patterns. This analysis was conducted by utilizing forecasts of irrigation water availability. According to Kumar 

and Babu (2016), the implementation of a GIS that is accessible through the web can serve as a valuable tool for 

farmers at the individual farm level. This system would enable farmers to obtain pertinent information and make 

informed decisions aimed at enhancing agricultural productivity. Singha et al. (2020) assert that this system 

possesses a broader range of potential applications in facilitating agronomic decision-making processes, such as 

the optimization of land and labor efficiencies, the promotion of increased cropping intensities, and the generation 

of improved crop yields. This practice has the potential to enhance agricultural yield and optimize crop 

management practices, particularly in terms of precision irrigation management, over an extended period of time. 

Figure 5 presents the usage of Internet of Things (IoT) and smart sensors for agricultural monitoring. 

 

 
Figure 5. The usage of IoT and smart sensors for agricultural monitoring (Rajak et al., 2023).  



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Evaluation of drought conditions  

 

The utilization of spatial datasets derived from satellite RS and GIS technology provides valuable insights for 

evaluating and modeling agricultural drought-risk patterns, monitoring drought conditions, and generating maps 

depicting drought vulnerability (Mullapudi et al., 2023; Raihan et al., 2023k). Hoque et al. (2021) involved the 

integration of geospatial methodologies and fuzzy logic in order to create a complete spatial drought risk inventory 

model that can be utilized for effective operational drought management. The model effectively detected and 

characterized the geographical boundaries and patterns associated with agricultural drought vulnerability. Sehgal 

and Dhakar (2016) employed GIS and high-resolution RS data to create indicators of crop sensitivity. These 

indicators were then utilized to establish a technique for evaluating and mapping the primary biophysical elements 

that contribute to vulnerability to agricultural drought, specifically at a local level. According to Aziz et al. (2022), 

policymakers can utilize drought risk maps to develop policies that are geographically specific for mitigating and 

intervening in drought situations. Furthermore, the utilization of vulnerability maps can serve as a valuable tool in 

identifying areas that need prioritization for socioeconomic development policy initiatives (Isfat & Raihan, 2022; 

Saha et al., 2023; Raihan & Bijoy, 2023). 

 

Detection and control of pests and crop diseases  

 

Ahmad and Sharma (2023) assert that ongoing advancements in geospatial tools and techniques are being made 

with the aim of assisting farmers in their efforts to control and manage crop diseases. Several research have 

demonstrated the pragmatic utilization of satellite RS data and Geospatial techniques in the context of detecting 

and managing crop diseases in a sustainable manner (Roberts et al., 2021; Raihan et al., 2018; Pande & Moharir, 

2023). RS technology, specifically the utilization of airborne and satellite imagery during the periods of crop 

growth, has been employed for the purposes of early detection and mapping of certain crop diseases, as well as for 

managing the recurrence of diseases in subsequent seasons and evaluating the economic impact resulting from 

frost damage (Jaafar et al., 2020; Sishodia et al., 2020; Raihan et al., 2019; Wu et al., 2023). Santoso et al. (2011) 

employed high-resolution QuickBird satellite images to successfully identify regional distributions of oil palm 

plants afflicted with basal stem rot disease. Six vegetation indicators were employed, which were generated from 

satellite imagery's visible and near-infrared bands, in order to effectively differentiate between oil palms that were 

healthy and those that were sick. Yang (2020) demonstrated the successful implementation of site-specific 

fungicide applications for disease management through the utilization of precision agriculture technologies and 

remotely sensed imaging. In the forthcoming years, novel methodologies that employ geoinformation technology 

for the purpose of monitoring and managing pest and crop disease detection have the potential to mitigate the 

environmental impact of pesticides and herbicide chemicals. 

 

Precision agriculture  

 

Automated geospatial analysis and decision support algorithms in the field of precision agriculture have the 

potential to offer policymakers significant scientific insights, hence enhancing the creation of agriculture policies 

(Raihan et al., 2021a; Saliu & Deari, 2023). The utilization of integrated GIS, RS, and GPS technology has led to 

the increasing recognition of precision agriculture methods. These practices have demonstrated their effectiveness 

in enhancing crop output, enabling site-specific crop management, and minimizing the use of agrochemicals 

(Karunathilake et al., 2023; Raihan et al., 2021b). Toscano et al. (2019) showcased the efficacy of utilizing 

Sentinel-2 and Landsat-8 imagery for representing the spatial heterogeneity of wheat production within a given 



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field. This information is crucial for the implementation of precision farming methodologies. This offered a 

prospective alternative to conventional farming methods through the enhancement of site-specific management 

and agricultural yield. García et al. (2020) conducted an investigation to evaluate the efficacy of remote-

sensing drones in serving as mobile gateways for facilitating Wi-Fi data transfer between sensor nodes and the 

gateway in precision agriculture systems. The authors aimed to provide insights into the ideal drone parameters 

that would ensure successful data transmission in this context. The research effectively showcased the utilization 

of drones as a remote sensing instrument for collecting data from field-deployed nodes to facilitate crop monitoring 

and management. The drones operated at a minimal pace, maintained a height of 24 m, and were equipped with an 

antenna providing a coverage range of 25 m. This had the potential to enhance the uptake of precision agriculture 

among smallholder farmers.  

Segarra et al. (2020) had a specific objective of comprehending the characteristics of the twin Sentinel-2 satellites 

belonging to the European Space Agency, as well as their utilization in the field of precision agriculture. The 

research conducted emphasizes the significant advancements brought about by Sentinel-2 in the field of 

agricultural monitoring and crop management. It has greatly enhanced the detection of abiotic and biotic stresses, 

improved the accuracy of crop yield estimation, facilitated more precise crop type classifications, and offered a 

range of other valuable applications in the agricultural sector. Various factors contribute to the augmentation of 

precision agriculture, resulting in enhanced agricultural management and environmental sustainability (Elahi et 

al., 2022; Raihan, 2024b). The utilization of satellite image-based solutions for the extraction of plantation rows 

plays a crucial role in various aspects of precision agriculture, including crop harvesting, pest management, and 

projections of plant growth rates. Fareed and Rehman (2020) conducted a study in which they employed GIS and 

RS techniques to develop an automated approach for extracting plantation rows from a digital surface model 

derived from drone-based picture point clouds. The technique of automatic extraction of plantation rows can be 

employed for the purpose of quantifying damage assessment in precision agriculture pertaining to plantation rows. 

Figure 6 presents the technologies involved in precision agriculture. 

 

 
Figure 6. Technologies involved in precision agriculture (Gonzalez-de-Santos et al., 2020).  



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Weed and fertilizer management 

 

The implementation of precise mapping techniques for weed distribution has the potential to significantly improve 

the effectiveness of weed management strategies. This can lead to a reduction in weed-related losses, as well as a 

decrease in the expenses associated with herbicide application and the optimization of fertilizer usage (Chaudhari 

et al., 2022; Raihan et al., 2022d). GIS technologies were employed to generate precise maps depicting the 

distribution of weeds inside rice farms, which enhanced input application efficiency, reducing the utilization of 

inputs such as herbicides, fungicides, and labor expenses associated with weeding (Dunaieva et al., 2018; Raihan 

et al., 2022e). Consequently, this led to a decrease in weed damage and a reduction in the overhead expenditures 

associated with crop production. The utilization of GIS in the creation of a GIS-based Fertilizer Decision Support 

System (FDSS) has been shown in the literature (Xie et al., 2012; Raihan et al., 2022f). This was achieved through 

the integration of RS data, field surveys, and expert knowledge. The researchers successfully developed a soil 

spatial database on the SuperMap platform, which proved to be valuable for effective crop management systems. 

The employment of FDSS in agricultural production has been found to yield several advantages, including 

enhanced efficiency in fertilizer utilization, thus leading to a reduction in production costs. 

 

Challenges in implementing GIS in agricultural policy and practice 

 

In general, the utilization of GIS and RS technology is not a universally effective solution for achieving evidence-

based policy and practice, and it is not without its drawbacks. The efficacy of geospatial technology application is 

contingent upon its appropriate utilization, the availability of high-quality data, and the allocation of substantial 

resources for its management (Raihan et al., 2022g). In nations experiencing limited resources, such as LMICs, 

the widespread utilization and acceptance of technology are hindered by the associated expenses and insufficient 

proficiency in relevant competencies (Raihan et al., 2022h). The task of simulating crop yield generation poses 

inherent challenges owing to the diverse array of cropping systems and the varying degrees of technological 

advancement employed (Abbasi et al., 2022; Raihan et al., 2022i). In order to achieve a precise evaluation of crop 

production gaps, it is imperative to enhance the quality of input data. This entails obtaining precise meteorological 

parameters, improving soil characterization, and acquiring geographically dispersed land use data (Schils et al., 

2022; Raihan, 2024c). Additionally, the implementation of instrumented geo-referenced validation sites would be 

necessary in order to provide full survey data that can be used to feed a continuous improvement cycle for assessing 

yield gaps (Tantalaki et al., 2019; Raihan, 2023j). Therefore, the advancement of remote sensing technologies and 

the refinement of integrated cropping systems models would result in enhanced precision for yield gap estimation 

in the future. 

In the field of drought vulnerability assessment and mapping, a majority of research documented in scholarly 

literature have demonstrated a preference for employing aggregated geographical data at broader spatial scales, 

such as national or regional levels. However, there has been a notable absence of use of finer-scale data, specifically 

at the local level (Raihan, 2023k). Given that the impact of drought hazards is predominantly experienced and 

demonstrated at the local level, conducting a comprehensive mapping of drought risks at a more precise scale 

necessitates the utilization of high-resolution remote sensing techniques and the incorporation of locally relevant 

indicators. This approach is crucial in order to obtain a comprehensive understanding of susceptibility in relation 

to drought (Raihan, 2023l). This would be of more significance to policymakers who are aiming to develop and 

execute mitigation initiatives at the local level. Given the anticipation of heightened and more frequent drought 

occurrences, as well as the escalating risks posed by climate change, the integration of all spatially explicit risk 

factors into drought risk mapping would constitute a valuable and effective addition to measures aimed at 



Global Sustainability Research 

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mitigating drought impacts (Raihan, 2023m). There is a need for further acquisition of skills and knowledge 

pertaining to the application of geospatial techniques in the context of agricultural drought risk. 

In the domain of agricultural disease identification, there persist issues pertaining to the accurate mapping of such 

diseases through the utilization of aerial or satellite imaging (Raihan, 2023n). While satellite imaging has proven 

effective in detecting and mapping several crop diseases, it is important to note that each disease possesses unique 

characteristics that necessitate distinct approaches for detection and management. Yang (2020) asserts that the 

identification of certain diseases poses challenges, particularly when there are several biotic and abiotic factors 

that have similar spectral features within a given field. The identification of recurring diseases necessitates the 

utilization of consistent historical images and spatial-temporal data, whereas the detection of developing diseases 

poses greater challenges. Yang (2020) asserts that there is a need for the development of more sophisticated 

RS imaging sensors and image-processing systems in order to effectively distinguish diseases from other factors 

that may cause confusion. In underdeveloped nations, there is a limited number of farmers possessing the requisite 

expertise for utilizing RS technologies in the generation of prescription maps, the execution of disease control 

strategies, and the administration of site-specific fungicides (Raihan, 2023o). Further investigation is warranted in 

the advancement of integrated geospatial analytical approaches and technologies to assist farmers in identifying 

various crop diseases. 

The utilization of precision agriculture technologies has the potential to enhance crop optimization and support 

decision-making in agricultural management, thereby addressing the issue of food insecurity in LMICs (Raihan, 

2023p). However, the successful implementation of precision farming necessitates the adoption of geospatial 

technology and the acquisition of substantial quantities of high-resolution spatiotemporal data (Raihan, 2023q). 

The deficiency in proficiency in using GIS and RS technologies in LMICs can be addressed by the widespread 

distribution and adoption of realistic geospatial technology from more industrialized nations (Roberts et al., 2021; 

Raihan, 2023r). Nevertheless, the successful implementation of precision agriculture methods in LMICs requires 

substantial investments in information and communication technology (ICT) infrastructure.  

The evaluation of soil fertility is widely recognized as a crucial parameter in precision farming and the sustainable 

management of agricultural lands based on their inherent capabilities (Raihan, 2023s). The establishment of a 

comprehensive soil information system is necessary. Nevertheless, as asserted by Abdelfattah and Kumar (2015), 

a significant portion of the global population lacks access to comprehensive soil quality data. In LMICs, there is a 

notable increase in the fragmentation of agricultural land into smaller plots that are economically unviable, 

accompanied by the adoption of unsustainable farming practices (Raihan, 2023t). Further investigation is 

warranted to explore the potential of active remote sensors in assessing soil quality within a dynamic and evolving 

context. 

One of the challenges hindering the utilization and acceptance of GIS and RS in the agricultural sector is to the 

absence of universally recognized standards for data interoperability (Raihan, 2023u). Despite the growing 

accessibility of spatial data utilization in LMICs, a significant challenge arises from the inherent susceptibility of 

these data to errors (Raihan, 2023v). Furthermore, the collection and storage of such data in LMICs may involve 

disparate spatial units, formats, metadata, as well as variations in time and space intervals (Raihan, 2023w). These 

factors render certain data impractical for use, impede the integration of spatial data, and impede the 

comprehensive analysis of data, particularly when it is acquired from several sensors and platforms (Raihan, 

2023x). There is a necessity to establish universally accepted protocols for the development of standardized rules 

pertaining to geographical data in the field of agriculture (Raihan, 2023y). It is of utmost importance to provide 

training to academics, practitioners, and farmers on the proper methods for collecting spatial data that is both high 

in quality and accuracy, ensuring its usability across several platforms. Enhancing the interoperability of spatial 

data repositories has the potential to facilitate data integration and enhance the efficacy of data analysis. 



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Crowdsourced data collecting has promise as a valuable contribution towards the development of cost-effective 

agri-spatial data repositories. 

 

Conclusions and Policy Implications 

 

This study has examined diverse methods in which GIS technology has been incorporated within the agricultural 

sector to enhance decision-making processes and inform policymaking efforts. GIS and RS technologies offer 

more effective methodologies for analyzing spatial elements that impact agricultural production in comparison to 

methods that lack geographically explicit data. When effectively utilized, the spatially integrated knowledge 

offered by GIS and RS can be utilized to augment agricultural policy and evidence-based interventions aimed at 

enhancing agricultural sustainability. Despite the potential of GIS technology to enhance agronomic practices, their 

utilization in many LMICs is limited. This is concerning, as many countries are in urgent need of upgrading their 

agriculture and food production techniques. In order to optimize the utilization of GIS and RS technologies, it is 

imperative for governments and farmers in LMICs to enhance their understanding and possible application of 

spatial data pertaining to agricultural practices. Advancements in geoinformatics methods and computing 

infrastructure have the potential to facilitate a more collaborative framework among various stakeholders, 

including scientists, researchers, policymakers, academics, crop consultants, extension employees, and 

agricultural machinery and chemical dealers. This framework aims to provide practical guidelines for the effective 

management of crop production estimations, fertility of the soil, cropping patterns and monitoring, drought risks, 

as well as fertilizer and weed management. 

To bolster evidence-based agricultural policy, government entities and policymakers necessitate robust empirical 

data that facilitates a comprehensive comprehension of the intricate and interrelated elements influencing 

agricultural productivity. Consequently, this would facilitate the development of specific intervention techniques. 

Moreover, it is imperative for a diverse range of stakeholders and professionals in the field of agriculture to have 

access to geographically relevant agricultural data in order to effectively implement a multitude of strategies aimed 

at enhancing agricultural productivity. Similarly, it is crucial for smallholder farmers to have access to synthesized 

information in order to empower them to make educated decisions based on evidence. This knowledge is also 

necessary for them to effectively undertake practical activities that can enhance agricultural productivity. The 

aforementioned statement highlights the growing need for the integration of GIS in the processes of formulating 

and implementing agricultural policies. 

GIS and RS technologies possess considerable potential in the evaluation, preservation, manipulation, and 

generation of agricultural data. The data holds potential value in several applications such as precision agriculture, 

site-specific farming, and disease detection, with the overarching goal of enhancing agricultural food production 

and addressing food security concerns. Regrettably, the absence of reliable spatial data in numerous local 

government entities persists as a hindrance to decision-making processes, policy development, and the subsequent 

execution thereof. In instances where such data is available, there is a prevailing deficiency in the proficiency of 

individuals in utilizing GIS and RS spatial analytical tools. The attainment of agriculturally integrated policies that 

encompass geographical dimensions necessitates the use of comprehensive and current spatial datasets, as well as 

improved methodologies that effectively amalgamate and analyze intricate data from diverse origins in order to 

generate valuable insights. This would require both national and local governments to implement methodologies, 

approaches, and methodologies that enable the gathering and examination of various agricultural datasets, thereby 

offering comprehensive insights to policymakers, planners, farmers, and a wide range of stakeholders involved in 

the agricultural industry. GIS offers a promising avenue for obtaining comprehensive and current spatial datasets, 

as well as improved spatial analytic techniques capable of effectively evaluating intricate data to generate valuable 



Global Sustainability Research 

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insights. If appropriately adopted and implemented, GIS has the potential to increase the spatial decision support 

system, hence boosting the efficiency and efficacy of agriculture policy development and planning. However, the 

implementation of policy change necessitates the presence of both public and political will in order to effectively 

lead and stimulate actions. Consequently, the implementation of GIS technology in the policymaking process 

necessitates the allocation of public monies by local government entities to establish the necessary software, 

hardware, supportive infrastructure, and staff training. Future research endeavors may prioritize the examination 

of how GIS and RS technologies might facilitate the establishment of a collaboration framework among scientists, 

policymakers, researchers, and extension agriculture officers. This collaborative framework aims to enhance the 

promotion of sustainable and climate-smart farming methods, with a particular emphasis on LMICs. 

 

Declaration  

 

Acknowledgment: N/A 

 

Funding: This research received no funding. 

 

Conflict of interest: The author declares no conflict of interest. 

 

Authors contribution: Asif Raihan contributed to conceptualization, visualization, methodology, reviewing 

literature, extracting information, synthesize, and manuscript writing. 

 

Data availability: The author confirms that the data supporting the findings of this study are available within the 

article. 

 

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16(3), 1-7.  

Raihan, A., Begum, R. A., Said, M. N. M., & Pereira, J. J. (2021b). Assessment of carbon stock in forest biomass 

and emission reduction potential in Malaysia. Forests, 12(10), 1294.  

Raihan, A., Begum, R. A., Said, M. N. M., & Pereira, J. J. (2022a). Dynamic impacts of energy use, agricultural 

land expansion, and deforestation on CO2 emissions in Malaysia. Environmental and Ecological Statistics, 

29, 477-507.  

Raihan, A., Begum, R. A., Said, M. N. M., & Pereira, J. J. (2022h). Relationship between economic growth, 

renewable energy use, technological innovation, and carbon emission toward achieving Malaysia’s Paris 

agreement. Environment Systems and Decisions, 42, 586-607.  

Raihan, A., & Bijoy, T. R. (2023). A review of the industrial use and global sustainability of Cannabis sativa. 

Global Sustainability Research, 2(4), 1-29.  

Raihan, A., Farhana, S., Muhtasim, D. A., Hasan, M. A. U., Paul, A., & Faruk, O. (2022e). The nexus between 

carbon emission, energy use, and health expenditure: empirical evidence from Bangladesh. Carbon Research, 

1(1), 30.  

Raihan, A., & Himu, H. A. (2023). Global impact of COVID-19 on the sustainability of livestock production. 

Global Sustainability Research, 2(2), 1-11.  

Raihan, A., Ibrahim, S., & Muhtasim, D. A. (2023a). Dynamic impacts of economic growth, energy use, tourism, 

and agricultural productivity on carbon dioxide emissions in Egypt. World Development Sustainability, 2, 

100059.  

Raihan, A., Muhtasim, D. A., Farhana, S., Hasan, M. A. U., Paul, A., & Faruk, O. (2022d). Toward environmental 

sustainability: Nexus between tourism, economic growth, energy use and carbon emissions in Singapore. 

Global Sustainability Research, 1(2), 53-65.  

Raihan, A., Muhtasim, D. A., Farhana, S., Hasan, M. A. U., Pavel, M. I., Faruk, O., Rahman, M., & Mahmood, A. 

(2022b). Nexus between economic growth, energy use, urbanization, agricultural productivity, and carbon 

dioxide emissions: New insights from Bangladesh. Energy Nexus, 8, 100144.  

Raihan, A., Muhtasim, D. A., Farhana, S., Hasan, M. A. U., Pavel, M. I., Faruk, O., Rahman, M., & Mahmood, A. 

(2023b). An econometric analysis of Greenhouse gas emissions from different agricultural factors in 

Bangladesh. Energy Nexus, 9, 100179.  

Raihan, A., Muhtasim, D. A., Farhana, S., Pavel, M. I., Faruk, O., & Mahmood, A. (2022f). Nexus between carbon 

emissions, economic growth, renewable energy use, urbanization, industrialization, technological innovation, 

and forest area towards achieving environmental sustainability in Bangladesh. Energy and Climate Change, 3, 

100080.  



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Raihan, A., Muhtasim, D. A., Farhana, S., Rahman, M., Hasan, M. A. U., Paul, A., & Faruk, O. (2023c). Dynamic 

linkages between environmental factors and carbon emissions in Thailand. Environmental Processes, 10, 5.  

Raihan, A., Muhtasim, D. A., Khan, M. N. A., Pavel, M. I., & Faruk, O. (2022g). Nexus between carbon emissions, 

economic growth, renewable energy use, and technological innovation towards achieving environmental 

sustainability in Bangladesh. Cleaner Energy Systems, 3, 100032.  

Raihan, A., Muhtasim, D. A., Pavel, M. I., Faruk, O., & Rahman, M. (2022c). An econometric analysis of the 

potential emission reduction components in Indonesia. Cleaner Production Letters, 3, 100008.  

Raihan, A., Pavel, M. I., Muhtasim, D. A., Farhana, S., Faruk, O., & Paul, A. (2023f). The role of renewable energy 

use, technological innovation, and forest cover toward green development: Evidence from Indonesia. 

Innovation and Green Development, 2(1), 100035.  

Raihan, A., Pereira, J. J., Begum, R. A., & Rasiah, R. (2023g). The economic impact of water supply disruption 

from the Selangor River, Malaysia. Blue-Green Systems, 5(2), 102-120.  

Raihan, A., Rashid, M., Voumik, L. C., Akter, S., & Esquivias, M. A. (2023h). The dynamic impacts of economic 

growth, financial globalization, fossil fuel energy, renewable energy, and urbanization on load capacity factor 

in Mexico. Sustainability, 15(18), 13462.  

Raihan, A., & Said, M. N. M. (2022). Cost–benefit analysis of climate change mitigation measures in the forestry 

sector of Peninsular Malaysia. Earth Systems and Environment, 6(2), 405-419.  

Raihan, A., & Tuspekova, A. (2022a). Towards sustainability: dynamic nexus between carbon emission and its 

determining factors in Mexico. Energy Nexus, 8, 100148.  

Raihan, A., & Tuspekova, A. (2022b). Dynamic impacts of economic growth, energy use, urbanization, tourism, 

agricultural value-added, and forested area on carbon dioxide emissions in Brazil. Journal of Environmental 

Studies and Sciences, 12(4), 794-814.  

Raihan, A., & Tuspekova, A. (2022c). Dynamic impacts of economic growth, energy use, urbanization, agricultural 

productivity, and forested area on carbon emissions: new insights from Kazakhstan. World Development 

Sustainability, 1, 100019.  

Raihan, A., & Tuspekova, A. (2022d). Nexus between emission reduction factors and anthropogenic carbon 

emissions in India. Anthropocene Science, 1(2), 295-310.  

Raihan, A., & Tuspekova, A. (2022e). Nexus between economic growth, energy use, agricultural productivity, and 

carbon dioxide emissions: new evidence from Nepal. Energy Nexus, 7, 100113.  

Raihan, A., & Tuspekova, A. (2022f). The nexus between economic growth, renewable energy use, agricultural 

land expansion, and carbon emissions: new insights from Peru. Energy Nexus, 6, 100067.  

Raihan, A., & Tuspekova, A. (2022g). Dynamic impacts of economic growth, renewable energy use, urbanization, 

industrialization, tourism, agriculture, and forests on carbon emissions in Turkey. Carbon Research, 1(1), 20.  

Raihan, A., & Tuspekova, A. (2022h). Nexus between energy use, industrialization, forest area, and carbon dioxide 

emissions: new insights from Russia. Journal of Environmental Science and Economics, 1(4), 1-11. 

Raihan, A., & Tuspekova, A. (2022i). Toward a sustainable environment: Nexus between economic growth, 

renewable energy use, forested area, and carbon emissions in Malaysia. Resources, Conservation & Recycling 

Advances, 15, 200096.  

Raihan, A., & Tuspekova, A. (2022j). Role of economic growth, renewable energy, and technological innovation 

to achieve environmental sustainability in Kazakhstan. Current Research in Environmental Sustainability, 4, 

100165.  

Raihan, A., & Tuspekova, A. (2022k). The nexus between economic growth, energy use, urbanization, tourism, 

and carbon dioxide emissions: New insights from Singapore. Sustainability Analytics and Modeling, 2, 

100009.  



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Raihan, A., & Tuspekova, A. (2023a). The role of renewable energy and technological innovations toward 

achieving Iceland’s goal of carbon neutrality by 2040. Journal of Technology Innovations and Energy, 2(1), 

22-37.  

Raihan, A., & Tuspekova, A. (2023b). Towards net zero emissions by 2050: the role of renewable energy, 

technological innovations, and forests in New Zealand. Journal of Environmental Science and Economics, 

2(1), 1-16.  

Raihan, A., & Voumik, L. C. (2022a). Carbon emission dynamics in India due to financial development, renewable 

energy utilization, technological innovation, economic growth, and urbanization. Journal of Environmental 

Science and Economics, 1(4), 36-50.  

Raihan, A., & Voumik, L. C. (2022b). Carbon emission reduction potential of renewable energy, remittance, and 

technological innovation: empirical evidence from China. Journal of Technology Innovations and Energy, 1(4), 

25-36.  

Raihan, A., Voumik, L.C., Esquivias, M.A., Ridzuan, A.R., Yusoff, N.Y.M., Fadzilah, A.H.H., & Malayaranjan, S. 

(2023i). Energy trails of tourism: analyzing the relationship between tourist arrivals and energy consumption 

in Malaysia. GeoJournal of Tourism and Geosites, 51, 1786–1795.  

Raihan, A., Voumik, L. C., Mohajan, B., Rahman, M. S., Zaman, M. R. (2023d). Economy-energy-environment 

nexus: the potential of agricultural value-added toward achieving China’s dream of carbon neutrality. Carbon 

Research, 2, 43.  

Raihan, A., Voumik, L. C., Nafi, S. M., & Kuri, B. C. (2022i). How Tourism Affects Women's Employment in 

Asian Countries: An Application of GMM and Quantile Regression. Journal of Social Sciences and 

Management Studies, 1(4), 57-72.  

Raihan, A., Voumik, L. C., Rahman, M. H., & Esquivias, M. A. (2023j). Unraveling the interplay between 

globalization, financial development, economic growth, greenhouse gases, human capital, and renewable 

energy uptake in Indonesia: multiple econometric approaches. Environmental Science and Pollution Research, 

30, 119117-119133.  

Raihan, A., Voumik, L. C., Ridwan, M., Ridzuan, A. R., Jaaffar, A. H., Yusof, N. Y. M. (2023k). From growth to 

green: navigating the complexities of economic    development, energy sources, health spending, and carbon 

emissions in Malaysia. Energy Reports, 10, 4318-4331.  

Raihan, A., Voumik, L. C., Yusma, N., & Ridzuan, A. R. (2023e). The nexus between international tourist arrivals 

and energy use towards sustainable tourism in Malaysia. Frontiers in Environmental Science, 11, 575. 

Rajak, P., Ganguly, A., Adhikary, S., & Bhattacharya, S. (2023). Internet of Things and smart sensors in agriculture: 

Scopes and challenges. Journal of Agriculture and Food Research, 14, 100776. 

Roberts, D. P., Short, N. M., Sill, J., Lakshman, D. K., Hu, X., & Buser, M. (2021). Precision agriculture and 

geospatial techniques for sustainable disease control. Indian Phytopathology, 74, 287-305. 

Roy, S., Singha, N., Bose, A., Basak, D., & Chowdhury, I. R. (2023). Multi-influencing factor (MIF) and RS–GIS-

based determination of agriculture site suitability for achieving sustainable development of Sub-Himalayan 

region, India. Environment, Development and Sustainability, 25(7), 7101-7133. 

Saha, S., Kundu, B., Saha, A., Mukherjee, K., & Pradhan, B. (2023). Manifesting deep learning algorithms for 

developing drought vulnerability index in monsoon climate dominant region of West Bengal, 

India. Theoretical and Applied Climatology, 151(1-2), 891-913. 

Saliu, F., & Deari, H. (2023). Precision Agriculture Improving Crop Production through Data-Driven Decision 

Making. International Journal of Research and Advances in Agricultural Sciences, 2(2), 14-33. 



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Santoso, H., Gunawan, T., Jatmiko, R. H., Darmosarkoro, W., & Minasny, B. (2011). Mapping and identifying 

basal stem rot disease in oil palms in North Sumatra with QuickBird imagery. Precision Agriculture, 12, 233-

248. 

Schils, R. L., van Voorn, G. A., Grassini, P., & van Ittersum, M. K. (2022). Uncertainty is more than a number or 

colour: Involving experts in uncertainty assessments of yield gaps. Agricultural Systems, 195, 103311. 

Segarra, J., Buchaillot, M. L., Araus, J. L., & Kefauver, S. C. (2020). Remote sensing for precision agriculture: 

Sentinel-2 improved features and applications. Agronomy, 10(5), 641. 

Sehgal, V. K., & Dhakar, R. (2016). Geospatial approach for assessment of biophysical vulnerability to agricultural 

drought and its intra-seasonal variations. Environmental monitoring and assessment, 188(3), 197. 

Shafi, U., Mumtaz, R., Iqbal, N., Zaidi, S. M. H., Zaidi, S. A. R., Hussain, I., & Mahmood, Z. (2020). A multi-

modal approach for crop health mapping using low altitude remote sensing, internet of things (IoT) and 

machine learning. IEEE Access, 8, 112708-112724. 

Sharma, R., Kamble, S. S., & Gunasekaran, A. (2018). Big GIS analytics framework for agriculture supply chains: 

A literature review identifying the current trends and future perspectives. Computers and Electronics in 

Agriculture, 155, 103-120. 

Shokr, M. S., Abdellatif, M. A., El Baroudy, A. A., Elnashar, A., Ali, E. F., Belal, A. A., ... & Kheir, A. M. (2021). 

Development of a spatial model for soil quality assessment under arid and semi-arid 

conditions. Sustainability, 13(5), 2893. 

Singha, C., Swain, K. C., & Swain, S. K. (2020). Best crop rotation selection with GIS-AHP technique using soil 

nutrient variability. Agriculture, 10(6), 213. 

Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A 

review. Remote Sensing, 12(19), 3136. 

Sultana, T., Hossain, M. S., Voumik, L. C., & Raihan, A. (2023a). Democracy, green energy, trade, and 

environmental progress in South Asia: Advanced quantile regression perspective. Heliyon, 9(10), e20488.  

Sultana, T., Hossain, M. S., Voumik, L. C., & Raihan, A. (2023b). Does globalization escalate the carbon 

emissions? Empirical evidence from selected next-11 countries. Energy Reports, 10, 86-98. 

Taiwo, B. E., Kafy, A. A., Samuel, A. A., Rahaman, Z. A., Ayowole, O. E., Shahrier, M., ... & Abosede, O. O. 

(2023). Monitoring and predicting the influences of land use/land cover change on cropland characteristics 

and drought severity using remote sensing techniques. Environmental and Sustainability Indicators, 18, 

100248. 

Tantalaki, N., Souravlas, S., & Roumeliotis, M. (2019). Data-driven decision making in precision agriculture: The 

rise of big data in agricultural systems. Journal of agricultural & food information, 20(4), 344-380. 

Tawfik, G. M., Dila, K. A. S., Mohamed, M. Y. F., Tam, D. N. H., Kien, N. D., Ahmed, A. M., & Huy, N. T. (2019). 

A step by step guide for conducting a systematic review and meta-analysis with simulation data. Tropical 

medicine and health, 47(1), 1-9. 

Toscano, P., Castrignanò, A., Di Gennaro, S. F., Vonella, A. V., Ventrella, D., & Matese, A. (2019). A precision 

agriculture approach for durum wheat yield assessment using remote sensing data and yield 

mapping. Agronomy, 9(8), 437. 

Trivedi, A., Rao, K. V. R., Rajwade, Y., Yadav, D., & Verma, N. S. (2022). Remote sensing and geographic 

information system applications for precision farming and natural resource management. Indian Journal of 

Ecology, 49(5), 1624-1633. 

Tsegaye, L., & Bharti, R. (2021). Soil erosion and sediment yield assessment using RUSLE and GIS-based 

approach in Anjeb watershed, Northwest Ethiopia. SN Applied Sciences, 3, 1-19. 



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Tunçay, T., Kılıç, Ş., Dedeoğlu, M., Dengiz, O., Başkan, O., & Bayramin, I. (2021). Assessing soil fertility index 

based on remote sensing and gis techniques with field validation in a semiarid agricultural ecosystem. Journal 

of Arid Environments, 190, 104525. 

Viana, C. M., Freire, D., Abrantes, P., Rocha, J., & Pereira, P. (2022). Agricultural land systems importance for 

supporting food security and sustainable development goals: A systematic review. Science of the total 

environment, 806, 150718. 

Voumik, L. C., Islam, M. J., & Raihan, A. (2022). Electricity production sources and CO2 emission in OECD 

countries: static and dynamic panel analysis. Global Sustainability Research, 1(2), 12-21.  

Voumik, L. C., Mimi, M. B., & Raihan, A. (2023a). Nexus between urbanization, industrialization, natural 

resources rent, and anthropogenic carbon emissions in South Asia: CS-ARDL approach. Anthropocene 

Science, 2(1), 48-61.  

Voumik, L. C., Rahman, M. H., Rahman, M. M., Ridwan, M., Akter, S., & Raihan, A. (2023). Toward a sustainable 

future: Examining the interconnectedness among Foreign Direct Investment (FDI), urbanization, trade 

openness, economic growth, and energy usage in Australia. Regional Sustainability, 4, 405-415.  

Voumik, L. C., Ridwan, M., Rahman, M. H., & Raihan, A. (2023). An Investigation into the Primary Causes of 

Carbon Dioxide Releases in Kenya: Does Renewable Energy Matter to Reduce Carbon Emission?. Renewable 

Energy Focus, 47, 100491. 

Wang, Y., Chen, Y., & Peng, S. (2011). A GIS framework for changing cropping pattern under different climate 

conditions and irrigation availability scenarios. Water resources management, 25, 3073-3090. 

Wang, Y., Min, D., Ye, W., Wu, K., & Yang, X. (2023). The Impact of Rural Location on Farmers’ Livelihood in 

the Loess Plateau: Local, Urban–Rural, and Interconnected Multi-Spatial Perspective Research. Land, 12(8), 

1624. 

Warren, S., Bampton, M., Cornick, L., & Patolo, N. (2023). Mapping the anthropogenic ocean: a critical GIS 

approach. Geographical Review, 113(4), 554-572. 

Weiss, M., Jacob, F., & Duveiller, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote 

sensing of environment, 236, 111402. 

Wijerathna-Yapa, A., & Pathirana, R. (2022). Sustainable Agro-Food Systems for Addressing Climate Change and 

Food Security. Agriculture, 12(10), 1554. 

Wu, B., Zhang, M., Zeng, H., Tian, F., Potgieter, A. B., Qin, X., ... & Loupian, E. (2023). Challenges and 

opportunities in remote sensing-based crop monitoring: a review. National Science Review, 10(4), nwac290. 

Xie, Y. W., Yang, J. Y., Du, S. L., Zhao, J., Li, Y., & Huffman, E. C. (2012). A GIS-based fertilizer decision support 

system for farmers in Northeast China: a case study at Tong-le village. Nutrient Cycling in Agroecosystems, 93, 

323-336. 

Yadav, N., Garg, V. K., Chhillar, A. K., & Rana, J. S. (2023). Recent advances in nanotechnology for the 

improvement of conventional agricultural systems: A Review. Plant Nano Biology, 4, 100032. 

Yanbo, Q., Shilei, W., Yaya, T., Guanghui, J., Tao, Z., & Liang, M. (2023). Territorial spatial planning for regional 

high-quality development–An analytical framework for the identification, mediation and transmission of 

potential land utilization conflicts in the Yellow River Delta. Land Use Policy, 125, 106462. 

Yang, C. (2020). Remote sensing and precision agriculture technologies for crop disease detection and 

management with a practical application example. Engineering, 6(5), 528-532. 

Zhang, L., Mohandes, S. R., Tong, J., Abadi, M., Banihashemi, S., & Deng, B. (2023). Sustainable Project 

Governance: Scientometric Analysis and Emerging Trends. Sustainability, 15(3), 2441.