AI Driven Soil Monitoring and Crop Recommendation using Machine Learning Algorithm.

Farmers, notably in India, face challenges such as inadequate expertise in crop selection and crop failures due to disease. Deep Learning's untapped potential in agriculture, limited by data quality and processing constraints, presents an opportunity for enhancement. The objective is to give an open-source online application is developed using Flask to aid farmers in informed decision-making. Utilize Precision Agriculture principles to recommend crops based on soil types, features, and data-driven insights to boost productivity. The various methods based on a machine learning-based ensemble model is employed to suggest suitable crops based on soil data, focusing on accuracy and efficiency. The outcome of the developed system shows promise in enhancing input-output efficiency, improving decision-making, and reducing unsuitable crop choices, thereby boosting agricultural productivity and positively impacting India's economy. This approach demonstrates practical potential in resolving farming challenges by leveraging machine learning for crop recommendations, potentially revolutionizing agriculture in India and beyond. [ABSTRACT FROM AUTHOR]