Smart Farming with IoT and Machine Learning for Crop Recommendation and Disease Detection

Key challenges in traditional agriculture include subjective crop recommendation methods based on farmer experience, inefficient plant disease detection techniques reliant on visual inspection, and rudimentary environmental monitoring methods using manual observations. These limitations hinder optimal crop management and environmental control, leading to reduced productivity and increased vulnerability to pests and diseases. Smart agricultural system addresses the limitations imposed by outdated farming practices by incorporating IoT sensors and Machine Learning (ML) algorithms to facilitate datadriven decision-making and optimize farming processes. Key functionalities include crop recommendation, plant disease prediction, soil moisture monitoring, and humidity and temperature monitoring. Crop recommendation is facilitated by ML algorithms, specifically Random Forest, which analyses collected data to suggest suitable crops for specific geographic areas. Disease prediction employs TensorFlow models to accurately detect and diagnose plant diseases based on image data. Soil moisture monitoring is achieved through soil sensor, providing real-time data on soil water content, while humidity and temperature levels are monitored using DHT11 sensor. These environmental parameters are crucial for maintaining optimal growing conditions and mitigating risks associated with climate variability. Through the integration of IoT and ML technologies, our system offers a practical solution to enhance agricultural practices in resource-constrained settings. By providing farmers with actionable insights and decision support, we aim to improve crop yields, optimize resource utilization, and promote sustainable agriculture