Deep Learning for Farmland Assessment and Developing an Automatic Crop Recommendation System Using GCN

Agricultural productivity is significantly influenced by soil health and crop suitability. Conventional techniques of soil analysis are physically taxing, taking up significant time, and typically involve specialized knowledge. This research proposes an innovative method employing deep learning techniques to classify soil images for farmland assessment. Furthermore, the study extends to developing an automatic crop recommendation system based on the classified soil types. By Utilizing deep convolutional networks for visual data classification and integrating data-driven recommendations, this system aims to enhance decision-making processes in agriculture, promoting sustainable farming practices and optimized crop yields. The GCN model, having achieved almost perfect score of 98% the performance set here is beyond any doubt one of the best that can be routinely which has been complemented by great results in several essential indicators starting with F1-score, recall, specificity, sensitivity and precision. Besides this comprehensive study also provides verifiable evidence of the fact that in each of the five categories, the GCN model stands out as the superior performer emphasizes its possibility to revolutionize the terrain and farming decision-making. The GCN mode distinctive approach is this ability to use topologies from the graph to deconstruct equations in agriculture provides a multiple perspective unmatched by its competitors.