APPLICATION OF INTELLIGENT RECOMMENDATION FOR AGRICULTURAL CULTIVATIONS
M. Asan nainar1, R. Bharath Kumar2, A.P. Thileepan3, S. Vetrivel4, R. Lokesh5
1M. Asan Nainar Information Technology & SRM Valliammai Engineering College
2R. Bharath Kumar Information Technology & SRM Valliammai Engineering College
3A.P Thileepan Information Technology & SRM Valliammai Engineering College
4S. Vetrivel Information Technology & SRM Valliammai Engineering College
5R. Lokesh Information Technology & SRM Valliammai Engineering College
-------------------------------------------------------***---------------------------------------------------------
ABSTRACT
Farmers face several challenges when growing crops like uncertain irrigation, poor soil quality, etc. Especially in India, a major fraction of farmers does not have the knowledge to select appropriate crops and fertilizers. Moreover, crop failure due to disease causes a significant loss to the farmers, as well as the consumers. While there have been recent developments in the automated detection of these diseases using Machine Learning techniques, the utilization of Deep Learning has not been fully explored. Additionally, such models are not easy to use because of the high-quality data used in their training, lack of computational power, and poor generalize ability of the models. To this end, we create an open-source easy-to-use web application to address some of these issues which may help improve crop production. In particular, we support crop recommendation, fertilizer recommendation and plant disease prediction. In addition, we also use interpretability techniques to explain the prediction made by our disease detection model. In Indian economy and employment agriculture plays major role. This problem can be addressed through precision agriculture. This method gives solutions like proposing a recommendation system through an ensemble model with majority voting techniques using machine learning algorithm as learner to recommend suitable crop based on soil parameters with high specific accuracy and efficiency.
KEYWORDS: Deep Learning, Machine Learning, Random Forest Algorithm, Conventional Neural Network, Supervised Learning.
