Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions
This addresses crop failure and knowledge gaps for farmers, especially in India, but is incremental as it builds on existing machine learning methods for disease detection.
The paper tackles agricultural challenges like crop selection and disease detection by developing an open-source web application that provides crop and fertilizer recommendations, plant disease prediction, and an interactive news-feed, with results including the use of interpretability techniques to explain predictions.
Farmers face several challenges when growing crops like uncertain irrigation, poor soil quality, etc. Especially in India, a major fraction of farmers do 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 generalizability 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, plant disease prediction, and an interactive news-feed. In addition, we also use interpretability techniques in an attempt to explain the prediction made by our disease detection model.