Plantex: Plant Recommendations and Leaf Disease Detection Using IOT and Machine Learning

In modern agriculture, optimizing crop yield and managing plant health are crucial for sustainable farming practices. This research explores the development of a robust system that leverages machine learning to provide intelligent crop recommendations and accurate leaf disease detection. The system integrates environmental factors, such as soil quality (Nitrogen, phosphorus, potassium, temperature, humidity and PH level) and historical crop dataset, to recommend the most suitable crops for a given region. Concurrently, it employs advances image processing techniques and machine learning algorithms to identify and classify leaf diseases from plant images. By automating these processes, the system aims to assist farmers in making informed decisions, reducing crop losses, and enhancing overall productivity. The results demonstrate the potential of machine learning in transforming traditional agriculture into a data-driven industry, improving food security, and promoting sustainable farming practices.