Grapevine Leaf Disease Classification with Deep Learning and Feature Extraction Using IoT

Grapevine diseases may possess a substantial effect on crop yield and quality, but early identification and mitigation can help prevent losses. The manual detection and diagnosis of vineyard diseases can be time-consuming, subjective, and difficult for producers who lack the requisite knowledge. Therefore, automated systems may offer a more effective and accurate method for classifying grapevine diseases. This paper recommends an investigation on Grapevine Leaf Diseases Classification with Deep Learning Techniques and Feature Extraction Using the Internet of Things. The purpose of this research is to develop an IoT-based system for identifying and categorising different kinds of grapevine leaf diseases in real time. The system will capture photos of grapevine leaves using a network of IoT devices outfitted with high-resolution cameras. The images are going to be transmitted to a cloud-based infrastructure for analysis using techniques for deep learning and the extraction of features. The system that is suggested will enable cultivators to detect and categorise grapevine leaf illnesses in real time, enabling them to take swift action to prevent the disease’s spread and increase crop yield. The research will utilise the Grapevine foliage image dataset accessible via Kaggle and other datasets that are freely accessible to train and evaluate deep learning models. The models will be fine-tuned and optimised for the IoT platform in order to process and analyse images in real-time. The efficacy of the system will be evaluated using the metrics of precision, recall, precision, accuracy, and F1-score. The proposed research has the potential to revolutionise the grapevine field by providing producers with an affordable and effective instrument for detecting and classifying grapevine leaf diseases. In addition to being extensible to other crop diseases, the IoT-based system can contribute to developing sustainable agricultural practices.