Advancements in Precision Agriculture: An Integrated System for Crop Detection Using Image Analysis and Environmental Data

Abstract- -A revolutionary strategy to improve agricultural productivity, sustainability, and efficiency is precision agriculture. This research proposes an integrated, complete system that uses environmental data and image analysis techniques to precisely detect crops. The suggested method makes use of developments in data analytics, computer vision, and remote sensing to enable precise and fast crop identification in agricultural fields. Farmers can now make more informed decisions about irrigation, fertilization, and pest control because to the increased accuracy and dependability of crop recognition resulting from the integration of image analysis and environmental data. Because of the system’s real-time capabilities, resource consumption is optimized and crop disease risk is decreased through proactive monitoring and management. The use of machine learning models for predictive analysis is also covered in the article, which enables the system to estimate future crop yields and further improve resource allocation. This capacity for prediction helps farmers plan and make decisions, which eventually supports agricultural practices that are both commercially and sustainably viable. To sum up, the comprehensive integrated system that has been suggested is a noteworthy development in precision agriculture, providing farmers with an effective instrument to improve their crop management techniques. In addition to increasing crop detection accuracy, the combination of image analysis and environmental data lays the groundwork for data-driven insights and decision-making in contemporary agriculture.

DOI: /10.61463/ijset.vol.12.issue1.602