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ISSN 1004-9037
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Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
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      09 May 2023, Volume 38 Issue 3

    Dr. B. Chinna Rao1, A. Prasanth2, Bh. Lakshmi Gowri Prasanna3, B. Chenna Kesava Reddy4, D. Mahesh5
    Journal of Data Acquisition and Processing, 2023, 38 (3): 30-36 . 


    Agricultural productivity is a key component of the Indian economy. Therefore, the contribution of food crops and cash crops is highly important for both the environment and human beings. Every year crops succumb to several diseases. Due to inadequate diagnosis of such diseases and not knowing symptoms of the disease and its treatment, many plants die. This project aims to develop a system for the detection of leaf diseases using machine learning in MATLAB. The system involves collection a dataset of labeled images of leaves using machine learning and extracting relevant features from the images, selecting a suitable machine learning model, and training it on the extracted features. The performance of the model is evaluated on a test set of images, and the system can be deployed in a real-world application for detecting leaf diseases in new images. This project utilizes MATLAB’s rich set of tools and functions for each step of the process, making it an efficient and effective solution for leaf disease detection. The leaf disease detection system using machine learning in MATLAB aims to detect diseases in leaves using image processing and machine learning techniques. The system uses a dataset of labeled leaf images containing both healthy and diseased leaves. Relevant features are extracted from the images and used to train a machine learning model using algorithms such as SVM, KNN, or ANN. The model is tested and evaluated using metrics such as accuracy, precision, recall, and F1-score. Once the model is trained and evaluated, it can be deployed to detect leaf diseases in new images. The project has potential applications in agriculture and plant pathology, where early detection and identification of leaf diseases can lead to improved crop yields and reduced crop losses.


    Agriculture, Leaf Detection, Disease Identification, Accuracy, Disease Name, Support Vector Machine (SVM).

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ISSN 1004-9037


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