Crowd Sourcing Of Pests Information

Crowd Sourcing Of Pests Information

POLEBOINA RANGASAI, PUNITHA RANI S, PATHAKUMARI SUNIL KUMAR, DR JACOB AUGUSTINE

Abstract
This study introduces a transformative system that leverages Machine Learning (ML), Deep Learning (DL), and Internet of Things (IoT) technologies to address critical challenges in agriculture, including crop recommendation, fertilizer optimization, and pest and disease detection. The methodology involves collecting data from IoT sensors, weather APIs, and farmer-uploaded crop images, followed by preprocessing and analysis using ML and DL models. Results indicate a 90% accuracy in crop recommendations, a 25% reduction in fertilizer wastage, and an 85% success rate in early disease detection. Real-time monitoring through IoT integration provided actionable insights, significantly enhancing farm management. The discussion highlights challenges such as data quality, rural connectivity, and user accessibility, emphasizing the need for continuous improvements. In conclusion, this system demonstrates the potential to revolutionize agriculture by improving productivity, promoting sustainability, and empowering farmers with data-driven tools. Future enhancements will focus on expanding capabilities, refining user interfaces, and ensuring accessibility for diverse farming communities.

Index Terms—Agriculture, Machine Learning, Deep Learning, Internet of Things, Crop Recommendation, Fertilizer Optimization, Disease Detection, Real-time Monitoring, Sustainable Farming, Data-driven Decision Making.