A Recommendation System for Integrated Agriculture Using Neural Networks with Random Forest Algorithm

Agriculture and its affiliated sectors are significant sources of income in rural India. However, the agricultural yield per hectare is poor compared to worldwide norms. A difficulty experienced by the farmers is that the suitable crop is not selected for their soil conditions, resulting in a diminished yield. This study solves this difficulty by proposing a recommendation system with an accuracy of 96% that efficiently uses a random forest algorithm to select a crop based on site-specific variables. Plant diseases affect their unique species' growth; consequently, early detection is vital. Therefore to categorize and detect symptoms of plant illnesses, Convolutional Neural Network (CNN) architecture with an accuracy of 75% is utilized with numerous visualization techniques. Early diagnosis would aid in minimizing pesticide usage; further CNN model for the guiding of pesticides is also adopted with an overall accuracy of 81%. In addition, these strategies are evaluated using a range of performance metrics. Agriculture is an underdevelopment field lacking a solid trade mechanism to sell the harvest, resulting in farmers' benefit. An E-commerce platform has been built where farmers can sell daily generated commodities straight from their field location or their residences without adding the market—incorporating Artificial intelligence into agriculture methods has proven helpful in modernizing farming in a suggested system comprising crop, fertilizer, pesticide recommendation, and plant disease detection.