Monitoring diverse crop activities using Machine Learning Approach

Agriculture is a significant occupation for a large portion of the Indian population.Crop production plays a crucial role in our economy.The quality of crop production can suffer due to improper crop selection for specific soil types or lack of knowledge about different crops' growth requirements.The proposed system makes use of machine learning to recommend crops based on historical soil parameter data, reducing the risk of soil degradation and promoting crop health.Factors like Sulphur, potassium, calcium, temperature, humidity, rainfall and soil ph.levels are analyzed using neural networks to suggest suitable crops for cultivation.One of the main reasons for low crop yield is the presence of infections caused by microorganisms, viruses, and fungi.Plant disease analysis is a key task in agriculture and can be prevented by utilizing plant disease detection techniques.Manual monitoring and management of plant diseases are laborintensive and time-consuming, hence the use of image processing for disease identification.The objective of this study is to develop a model that can detect diseased crop leaves and predict plant diseases.This work is based on the convolution neural network(CNN).The detection of pests in agricultural field has attracted a lot of attention, which is helpful in achieving smart agriculture.In particular, the monitoring of crop pests is one of the key ways to manage and optimize agricultural resources.You Only Look Once (YOLO) based approaches have provided good results.Moreover, there is no large dataset for pest detection.In essence, this study puts forth a complete approach that tackle the ability of machine learning to revolutionize crop recommendation, disease detection, and pest detection in the agricultural sector.The primary objective is to optimize crop yield and foster sustainable practices.Through the integration of neural networks for crop recommendation, CNN for disease detection, and the challenges associated with pest detection, this research plays a crucial role in project the development of modern agricultural practices in India.