Leaf Analytica Through AI

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Authors : Rohan Raut , Anshul Suryawashi , Dhiray Tongse ,Shubhanshu singh, Prof.Pooja Wagh

DOI : 10.46335/IJIES.2024.9.9.8

Abstract- The Proposed system aims to develop a robust and reliable system for automated plant disease detection using machine learning algorithms. The proposed system utilizes image processing techniques to extract relevant features from images of plant leaves exhibiting symptoms of diseases. These features are then fed into various machine learning models, including convolutional neural networks (CNNs), support vector machines (SVMs), and decision trees, to classify the presence and type of disease accurately. Key components of the Proposed system include dataset collection, preprocessing, feature extraction, model training, and performance evaluation. A diverse dataset comprising images of healthy and diseased plantleaves from multiple plant species will be curated and annotated. Preprocessing techniques such as normalization, augmentation, and noise reduction will be applied to enhance the quality of the input images. Feature extraction methods, including handcrafted features and deep learning- based feature representations, will be explored to capture discriminative information from the images effectively. Various machine learning algorithms will be trained and optimizedusing the extracted features to achieve high classification accuracy. The performance of the developed system will be evaluated using metrics such as accuracy, precision, recall, and F1- score through cross-validation and testing on unseen data. The Proposed system aims to provide farmers and agricultural stakeholders with an efficient tool for early detection and management of plant diseases, ultimately contributing to improved crop health and increased agricultural productivity.