A Smart Intelligent Agriculture Technique for Leaf Disease Identification and Monitoring Using Deep Learning

Plant diseases continue to increase threats that demand immediate attention to the stability of worldwide food systems and farming product output. The identification technologies currently used in disease diagnosis take too much time while requiring large manual effort and succumb to human error. Research development has created a smart agriculture system based on deep learning and feature extraction so plant leaf diseases can be effectively detected and identified. The combined application of transfer learning with CNNs enables the system to extract significant features from leaf images thus achieving precise plant disease classification. Specific image preprocessing methods that do both enhancement and segmentation work together to improve model precision specifically through the elimination of noise and utterance of disease-related patterns. A diverse collection of leaf images between diseased and healthy states serves to validate the system across different plant types. The deep learning-based extraction process outperforms traditional machine learning through tests that prove precise disease recognition throughout all experiments. Exact agriculture receives substantial progress through artificial disease diagnosis systems that decrease agricultural losses while enabling data-driven plant health monitoring. Future investigations plan to build real-time mobile solutions that unite edge computing abilities for making field-based disease diagnosis decisions.