Artificial Intelligence Integrated Rice Crop Disease Detection Using Drones for Smart Farming

This investigate addresses the common challenge farmers confront when physically distinguishing crop infections. We propose a viable arrangement utilizing drone-based crop discovery frameworks, which incorporate a high-resolution camera and a YOLOv8 picture classification demonstrate. This innovation makes disease discovery in broad agricultural regions both productive and cost-effective. The proposed framework points to diminish financial misfortunes for agriculturists and improve the generally maintainability of crop yields. By giving convenient and precise infection distinguishing proof, our arrangement makes a difference minimize the hazard of illness spreading to adjacent crops. Also, our research highlights the broader suggestions of integrating advanced technologies into farming, emphasizing potential financial benefits for ranchers. The mechanized usefulness of the framework facilitates the manual workload on agriculturists, permitting them to center on key decision-making and asset optimization. In rundown, this examination marks a critical step in utilizing cutting-edge innovation to address significant challenges in farming, advancing financial versatility and maintainable practices.

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Publication Details

Published
2024-08-08
DOI
https://doi.org/10.1109/iccpct61902.2024.10672693
Citations
4
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
2.40
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article

Artificial Intelligence Integrated Rice Crop Disease Detection Using Drones for Smart Farming

Diksha Diksha, Ayush Kumar Verma, Sachin Sharma
4 citations
Smart Agriculture and AI
2.40
article

Artificial Intelligence Integrated Rice Crop Disease Detection Using Drones for Smart Farming

Diksha Diksha, Ayush Kumar Verma, Sachin Sharma
article en
4 citations

Abstract

This investigate addresses the common challenge farmers confront when physically distinguishing crop infections. We propose a viable arrangement utilizing drone-based crop discovery frameworks, which incorporate a high-resolution camera and a YOLOv8 picture classification demonstrate. This innovation makes disease discovery in broad agricultural regions both productive and cost-effective. The proposed framework points to diminish financial misfortunes for agriculturists and improve the generally maintainability of crop yields. By giving convenient and precise infection distinguishing proof, our arrangement makes a difference minimize the hazard of illness spreading to adjacent crops. Also, our research highlights the broader suggestions of integrating advanced technologies into farming, emphasizing potential financial benefits for ranchers. The mechanized usefulness of the framework facilitates the manual workload on agriculturists, permitting them to center on key decision-making and asset optimization. In rundown, this examination marks a critical step in utilizing cutting-edge innovation to address significant challenges in farming, advancing financial versatility and maintainable practices.

Graphic Era University (IN)
Zero hunger
Openalex Percentile: Top 11%
Smart Agriculture and AI
2.40
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Artificial Intelligence Integrated Rice Crop Disease Detection Using Drones for Smart Farming — Diksha Diksha, Ayush Kumar Verma, et al. · (2024) | TGRS Research Map | TGRS