AgriLeaf Pro: Implementation of an IoT and ML-Based System for Leaf Disease Detection and Soil Nutrient Monitoring

AgriLeaf Pro: Implementation of an IoT and ML-Based System for Leaf Disease Detection and Soil Nutrient Monitoring

Aghar Usman Kannathodi1, Gowtham S2, Guru Kiran S N3, Koushik G4

1 2 3 4 Students of Information Science and Engineering Department, Malnad College of Engineering, Hassan, Karnataka, India

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Abstract - AgriLeaf Pro is an integrated smart agriculture solution designed to enhance crop productivity and promote sustainable farming practices through the combined use of Internet of Things (IoT) technologies and Machine Learning (ML) models. The system comprises two core components: an Arduino-based IoT sensor module for real-time monitoring of soil nutrients (nitrogen, phosphorus, potassium, pH, and moisture), and a Convolutional Neural Network (CNN)-based model for leaf disease detection. The CNN model is capable of classifying 19 different plant leaf diseases with over 90% accuracy, offering early intervention strategies to prevent crop loss. A key innovation of AgriLeaf Pro lies in its responsive web-based dashboard, which visualizes sensor data, generates real-time alerts for nutrient deficiencies, and provides actionable insights for farmers. This system allows for efficient decision-making, optimized use of fertilizers, and timely disease management. The integration of these technologies empowers farmers to transition from traditional reactive farming methods to a data-driven, proactive approach. By reducing resource wastage and minimizing yield loss, AgriLeaf Pro contributes to the advancement of precision agriculture and environmental sustainability.

Key Words:         IoT, Machine Learning, Plant Disease Detection, Soil Monitoring, Precision Agriculture