Real-time crop analysis with enhanced image classification using deep learning and fuzzy logic techniques

The proposed research presents a smart irrigation system is designed to optimize agricultural water management using a robotic framework that integrates deep learning and fuzzy logic for real-time crop classification and decision-making. It employs a combination of VGG-19 and a fusion model RTCropNet (Real-time crop network), leveraging the strengths of each architecture: VGG-19 for robust feature extraction and the RTCropNet fusion for enhanced spatial localization and fine-grained crop classification. Pre-processing is performed through advanced fuzzy logic techniques to improve image quality, enhancing classification accuracy across diverse crop types. Evaluation metrics indicate that the RTCropNet model achieves a training accuracy of 80.15% and a validation accuracy of 81.77%, with precision, recall, and F1 scores of 0.83, 0.82, and 0.82, respectively, outperforming traditional methods on complex agricultural imagery. Comparative analysis shows the model's superiority over EfficientNet-B3, DenseNet-121, and MobileNetV2, particularly in handling high-resolution spatial details, while maintaining efficiency in real-time conditions. This integration of deep learning with fuzzy logic and sensor data represents a novel approach in precision irrigation, offering a dynamic, data-driven response system that adjusts irrigation based on real-time environmental conditions, distinguishing it from conventional SOTA approaches.