Farmer's Assistant: A Machine Learning Based Application for Agricultural Solutions

Abstract

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.

Advancing toward Personalized and Precise Phosphorus Prescription Models for Soybean (Glycine max (L.) Merr.) through Machine Learning

“…Several studies, including those by Adams et al [5], and Ferguson et al [75], have confirmed that soybeans exhibit optimal performance in soils with pH values within this range. The order of importance of the last two predictors aligns with the observation made by Gupta et al [95] that precipitation holds greater significance than soil pH in influencing crop yield within the pH range of 5.5-7.0. This study's RF (Random Forest) model, supported by extensive data collection and comprehensive documentation of experimental P dosage sites, exhibits exceptional capabilities in generating highly personalized dose-by-dose response curves for individual diagnosed soybean fields.…”