Exploring Artificial Intelligence Applications in the Agricultural Sector

This study conducts a comprehensive review and critical analysis of scholarly articles pertaining to the use of Artificial Intelligence (AI) within the agricultural industry. Subsequently, it delves into an examination of the possible utilization of AI in various agricultural contexts. Farmers are now able to use advanced data and analytics solutions driven by AI, enabling them to optimize crop yields and reduce inefficiencies in the cultivation of biofuels and food. The sectors of agriculture are now seeing a transformative impact from the integration of AI and Machine Learning (ML), which have already shown significant advancements in several domains. Several emerging technologies are now being developed to facilitate the process of crop and soil monitoring for farmers, hence enhancing its simplicity. The cutting-edge techniques used in the monitoring of crop health include 3D laser scanning and hyperspectral imaging, which are based on AI. The use of AI-enhanced advancements enables the collection of more comprehensive data pertaining to the health of crops, surpassing previous levels of detail. The research focused on the significance of AI inside the agricultural sector. This article provides a brief overview of the functioning of AI in the agricultural sector, as well as the many elements that may be monitored via the use of AI technology. Ultimately, we have successfully examined the primary applications of artificial intelligence within the agricultural sector.

Authors

Institutions

Publication Details

Published
2025-04-08
DOI
https://doi.org/10.64026/jssf/2025005
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring Artificial Intelligence Applications in the Agricultural Sector

Arulmurugan Ramu
1 links
Smart Agriculture and AI
article

Exploring Artificial Intelligence Applications in the Agricultural Sector

Arulmurugan Ramu
article en

Abstract

This study conducts a comprehensive review and critical analysis of scholarly articles pertaining to the use of Artificial Intelligence (AI) within the agricultural industry. Subsequently, it delves into an examination of the possible utilization of AI in various agricultural contexts. Farmers are now able to use advanced data and analytics solutions driven by AI, enabling them to optimize crop yields and reduce inefficiencies in the cultivation of biofuels and food. The sectors of agriculture are now seeing a transformative impact from the integration of AI and Machine Learning (ML), which have already shown significant advancements in several domains. Several emerging technologies are now being developed to facilitate the process of crop and soil monitoring for farmers, hence enhancing its simplicity. The cutting-edge techniques used in the monitoring of crop health include 3D laser scanning and hyperspectral imaging, which are based on AI. The use of AI-enhanced advancements enables the collection of more comprehensive data pertaining to the health of crops, surpassing previous levels of detail. The research focused on the significance of AI inside the agricultural sector. This article provides a brief overview of the functioning of AI in the agricultural sector, as well as the many elements that may be monitored via the use of AI technology. Ultimately, we have successfully examined the primary applications of artificial intelligence within the agricultural sector.

Aktobe Regional State University named after K.Zhubanov (KZ)
Zero hunger
Openalex Percentile: Top 90%
Smart Agriculture and AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.