Smart Farming Solutions for Crop Disease Prediction and Control using IoT and ML

The agricultural domain stands as a pivotal sector, underpinning not only economic stability but also fundamental human sustenance. Nowadays, it's challenging to get the right output from agriculture because crops are afflicted with multiple diseases during growth, which frequently require the farmer's notice, in the poor production of crops. Over a period of thousands of years, agriculture has changed by including new technologies and techniques that increase production, sustainability, and efficiency. This study focuses on new approaches to smart farming that combine machine learning (ML) and the Internet of Things (IoT) to predict and handle crop diseases and correction. The objective is to collect real-time data on crop conditions by utilizing IoT devices that relate to sensors for temperature, moisture, humidity, NPK, PIR and combine ML output with these readings. The input information is analyzed using machine learning (ML) methods to compare the effectiveness of ResNet50, VGG16, and Convolutional Neural Networks (CNN) in the management and prediction of disease. As per the analysis, ResNet50 performs more effectively compared to the other algorithms due to its lower loss, which indicates a more precise detection of diseases. shown in this work how IoT and ML can transform the farming process by giving farmers a more precise and efficient way of managing and monitoring crops.