Agri-Botics (Seeding the Future)

Agricultural productivity and sustainability are blocked by multiple challenges. That's why small farmers realize the difficulty of growing food. Unpredictable weather patterns cause damage to crops and interfere with the planting schedule; this can reduce farming output a great deal compared with plans made in advance for exactly what to plant where. Often, these factors also increase fertilizing costs through more frequent application of expensive chemicals or fertilizers over labor-saving machinery, with every new production run costing about as much as its predecessor in materials. Dead soils mean another way in which pollution reduces fertility on the farm; this results not only in damaging crops. Soil degradation from overuse and excessive chemical application reduces the fertility of the earth, and pests and plant diseases multiply at great speed. The result is that farmers often lose a significant portion or all of their crops at harvest time. In addition, staffing these labor-intensive tasks in rural life is getting more difficult. Younger generations no longer remain on the farm but go to cities, and older agriculturists have to accomplish everything by themselves. Existing agricultural technologies are often inappropriate or merely offer lip service to the needs of small farmers. To change this state of affairs, Artificial Intelligence (AI) and Machine Learning (ML) provide an integrated technological solution tailored for farmers, incorporating sensors and artificial intelligence into one system that automates crucial farming jobs, such as detecting crop disease, choosing crops based on soil analysis for optimum growth, and forecasting future levels of harvest. The system assists farmers through decision factors, without their needing any technical expertise. Field sensors and APIs continuously monitor soil health and weather changes around the farm fields; this convenience makes for great water and fertilizer economies while providing a cost-saving benefit and minimizing pollution.

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Publication Details

Published
2025-06-17
DOI
https://doi.org/10.1109/icicv64824.2025.11086004
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
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article

Agri-Botics (Seeding the Future)

M. Sai Manikanta, T. Pavan Kumar, V. Baby Shalini, Nerella Venkata Siva Sai Sandeep
Smart Agriculture and AI
article

Agri-Botics (Seeding the Future)

M. Sai Manikanta, T. Pavan Kumar, V. Baby Shalini, Nerella Venkata Siva Sai Sandeep
article en

Abstract

Agricultural productivity and sustainability are blocked by multiple challenges. That's why small farmers realize the difficulty of growing food. Unpredictable weather patterns cause damage to crops and interfere with the planting schedule; this can reduce farming output a great deal compared with plans made in advance for exactly what to plant where. Often, these factors also increase fertilizing costs through more frequent application of expensive chemicals or fertilizers over labor-saving machinery, with every new production run costing about as much as its predecessor in materials. Dead soils mean another way in which pollution reduces fertility on the farm; this results not only in damaging crops. Soil degradation from overuse and excessive chemical application reduces the fertility of the earth, and pests and plant diseases multiply at great speed. The result is that farmers often lose a significant portion or all of their crops at harvest time. In addition, staffing these labor-intensive tasks in rural life is getting more difficult. Younger generations no longer remain on the farm but go to cities, and older agriculturists have to accomplish everything by themselves. Existing agricultural technologies are often inappropriate or merely offer lip service to the needs of small farmers. To change this state of affairs, Artificial Intelligence (AI) and Machine Learning (ML) provide an integrated technological solution tailored for farmers, incorporating sensors and artificial intelligence into one system that automates crucial farming jobs, such as detecting crop disease, choosing crops based on soil analysis for optimum growth, and forecasting future levels of harvest. The system assists farmers through decision factors, without their needing any technical expertise. Field sensors and APIs continuously monitor soil health and weather changes around the farm fields; this convenience makes for great water and fertilizer economies while providing a cost-saving benefit and minimizing pollution.

Kalasalingam Academy of Research and Education (IN)
Openalex Percentile: Top 86%
Smart Agriculture and AI
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Agri-Botics (Seeding the Future) — M. Sai Manikanta, T. Pavan Kumar, et al. · (2025) | TGRS Research Map | TGRS