Artificial Intelligence in Agrisystem

Artificial intelligence (AI) is a technology of 21st century that is limited to research in various fields of science and is lately a part of our daily life due to its rapid development. It has also gained importance in agricultural field and is achieving more applications. AI is applied in agriculture through two computer intelligence technologies: machine learning (ML) and deep learning (DL). These enable computer to learn and adapt processing input data pattern without human intervention by detecting hidden featuresof the subject under consideration. Generally, ML includes traditional methodologies that perform knowledge based learning by acquisition over features manually defined, whilst DL is a class of ML methods based on neural networks which learn to extract high-level hierarchies of features automatically from raw input. Moreover, there are diverse applications of AI in agriculture viz., crop management and health, crop husbandry etc. The wide-ranging growth of AI is mainly due to the power of high-performance computational devices, smart low-budget sensors for data acquisition, low-cost storage, wide availability of high-speed Internet for near real-time accessibility and sharing solution, abundant labeled data, great open-source software and algorithms used for knowledge extraction. AI systems presently help farming managers in agriculture more smartly to detect opportunities for crop productivity enhancement and guidelines for correct actions based on economic policy data trends. Unmanned Aerial Vehicles (UAVs), data collection services, satellite images, services providing data for input materials, pests and diseases analysis. In the near future, AI based services are likely to become a platform for agronomy, soil testing, crop health management and others where farmers may request AI to analyze raw data collected by low-budget robots, drones or smart phones by considering their special circumstances, cost and risk factors so as to take immediate action for further research and execution in various agricultural fields including soil microbiology and health with respect to macronutrients and micronutrients including trace elements.

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Publication Details

Journal
International Journal For Multidisciplinary Research
Published
2025-05-26
DOI
https://doi.org/10.36948/ijfmr.2025.v07i03.45862
Primary Topic
Evolutionary Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

Artificial Intelligence in Agrisystem

Aradhana Dohroo, Desh Raj Thakur
International Journal For Multidisciplinary Research
Evolutionary Algorithms and Applications
article

Artificial Intelligence in Agrisystem

Aradhana Dohroo, Desh Raj Thakur
article en

Abstract

Artificial intelligence (AI) is a technology of 21st century that is limited to research in various fields of science and is lately a part of our daily life due to its rapid development. It has also gained importance in agricultural field and is achieving more applications. AI is applied in agriculture through two computer intelligence technologies: machine learning (ML) and deep learning (DL). These enable computer to learn and adapt processing input data pattern without human intervention by detecting hidden featuresof the subject under consideration. Generally, ML includes traditional methodologies that perform knowledge based learning by acquisition over features manually defined, whilst DL is a class of ML methods based on neural networks which learn to extract high-level hierarchies of features automatically from raw input. Moreover, there are diverse applications of AI in agriculture viz., crop management and health, crop husbandry etc. The wide-ranging growth of AI is mainly due to the power of high-performance computational devices, smart low-budget sensors for data acquisition, low-cost storage, wide availability of high-speed Internet for near real-time accessibility and sharing solution, abundant labeled data, great open-source software and algorithms used for knowledge extraction. AI systems presently help farming managers in agriculture more smartly to detect opportunities for crop productivity enhancement and guidelines for correct actions based on economic policy data trends. Unmanned Aerial Vehicles (UAVs), data collection services, satellite images, services providing data for input materials, pests and diseases analysis. In the near future, AI based services are likely to become a platform for agronomy, soil testing, crop health management and others where farmers may request AI to analyze raw data collected by low-budget robots, drones or smart phones by considering their special circumstances, cost and risk factors so as to take immediate action for further research and execution in various agricultural fields including soil microbiology and health with respect to macronutrients and micronutrients including trace elements.

International Journal For Multidisciplinary ResearchVol. 7(3)
Openalex Percentile: Top 94%
Evolutionary Algorithms and Applications
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