Soil Health Analysis Using Few-Shot Learning and Deep Learning

This paper presents an innovative approach to soil health analysis specifically tailored for farmers in a Pune division. We addressing the unique agricultural needs of this region by leveraging advanced machine learning techniques and data-driven methodologies. Our research focused on the development of machine learning models for crop recommendation and soil classification, utilizing diverse datasets including soil nutrient information, soil card details, weather records, and soil image visuals. The first aspect of our investigation involves the implementation of a few-shot learning model for personalized crop suggestions. This model considering the unique characteristics of the soil in the Pune division, incorporating data from soil card filings, weather reports, and soil images specific to the region. By providing tailor recommendations for optimal crop selection, our model aiming to maximize agricultural productivity and sustainability for Pune division farmers. In the second phase, we employing convolutional neural networks (CNNs) for soil picture classification. This model analyzing soil visuals to accurately categorize soil types, assisting in soil classification procedures crucial for various agricultural and environmental applications within the Pune division. Our emphasis throughout this research is on developing robust and accurate machine learning models that cater to the agricultural needs of Pune division farmers. By combining cutting-edge machine learning techniques with region-specific agricultural data, including soil image data, this research aims to empower Pune division farmers with actionable insights. Our goal is to promote sustainable farming practices and contribute to the agricultural development of the region.