Advanced Rice Yield and Soil Prediction using Deep Learning

Rice farming is the cornerstone of global food security, and crop yield and quality are highly reliant on soil suitability. Crop identification and soil assessment in the past to a large degree rely on time-consuming methods that not only take much time but are also prone to human mistakes. In order to address such challenges, the current paper proposes a solution based on deep learning in the integration of rice crop classification and soil categorization in order to effectively optimize rice farming by accurately identifying the best cultivation conditions. The suggested system takes advantage of the architecture of an already trained Convolutional Neural Network (CNN) on a large database of images of different types of rice crops and soils. The model identifies and perceives the inherent spectral and textural characteristics of the images in a way that it is able to classify effectively. The system by processing the real images taken from the rice fields is able to identify the rice type and assess compatibility of the production-supporting soil simultaneously. This twin-analysis approach guarantees better farm decision-making with prompt and precise prediction of soil suitability without exposing many to manual testing. High reliability under varying environmental conditions is guaranteed with rigorous assessments with predefined assessment metrics of accuracy, precision, recall, and F1-score by the model. Agricultural diagnosis with deep learning provides a cost-effective and scalable path towards the future of agriculture. Not only is this research beneficial to farmers because of its ability to provide useful information on enhancing crop yield, but it is also sustainable because it encourages data-driven agriculture. The model has achieved an accuracy of 99.7%. The dataset used in this study includes 75,000 Macroscopic Rice images, of which 15000 contain arborio, 15000 contain basmati, 15000 contain Ipsala,15000 contain jasmine and 15000 labeled as karacadag.