Deep Learning Based Soil Classification and Crop Recommendation: A Real-Time Smart Agriculture Approach

Improving the efficiency and sustainability of agriculture requires precise soil categorization and suitable crop suggestions. However, the scalability and accessibility of traditional soil analysis methods are limited due to labor intensive, time consuming and specialized nature. This study uses cutting edge techniques to offer a reliable system for soil type classification in order to overcome these issues. Alluvial, black, clay and red soils are the four main types of soil that are the subject of this study. To improve the training dataset's quality and diversity and guarantee the models' resilience methods of data augmentation were used. With accuracies of 96%, 92% and 93% respectively, the system combines Convolutional Neural Networks (CNN), MobileNetV3 and VGG16 algorithms for type of soil classification. Enhancing data augmentation approaches, fine-tuning hyperparameters, improving model structures and employing advanced feature extraction techniques for improved soil disparities can all increase precision and reliability. Additionally, real-time soil type recognition and customized crop suggestions are made possible by a Flask-based user interface giving farmers practical knowledge for the best cultivation methods. By providing a scalable, precise and easy to-use answer to soil research and crop planning problems this creative method closes the gap between contemporary technology and conventional agriculture.