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Abstract:-The user-friendly interface of the application allows farmers and agricultural professionals to easily capture and analyze leaf images using Smartphone, providing real-time feedback and actionable insights. this plant disease detection application represents a significant advancement in agricultural technology, offering a practical solution for enhancing crop health management and contributing to global food security. Future work will focus on expanding the disease database, improving model accuracy, and integrating additional features such as pest identification and weather impact analysis. The system is built on a Convolutional neural network (CNN) architecture trained on a diverse dataset comprising thousands of labeled images of healthy and diseased plants. The application can identify common diseases such as leaf spot, powdery mildew, and blight, providing users with accurate disease classifications and severity assessments. The application utilizes a combination of image processing and machine learning algorithms to accurately identify and classify variousKeywords: Image processing, Crop health monitoring, Agriculture technology, Machine learning, Convolutional neural networks (CNN). Introduction:-This study aims to develop a plant disease detection application utilizing machine learning algorithms, specifically Convolutional Neural Networks (CNNs), to analyze and classify plant leaf images. The application is designed to be user-friendly, allowing farmers and agricultural professionals to capture images of plant leaves using smartphones and receive instant diagnostic feedback. By leveraging technology, we can provide a more accurate, efficient, and accessible solution for farmers and agricultural workers, ultimately enhancing crop health, reducing losses, and promoting sustainable farming practices, So for identifying and preventing the spread of plant diseases we will adopt advanced technologies such as Machine Learning (ML) and Deep Learning (DL) that can help to overcome these challenges by enabling early identification of plant diseases.In India about 70% of the populace relies on agriculture. Identification of the plant diseases is important in order to prevent the losses within the yield. In the early ages, one cannot easily detect the disease of leaves before spreading them by using prior knowledge. Thus, the identification of leaf diseases is one of the challenging area of researches in image processing (IP), ML, as well as computer vision.In this project, we have described the technique for the detection of plant diseases with the help of their leaves pictures. Image processing is a branch of signal processing which can extract the image properties or useful information from the image. Machine learning is a sub part of artificial intelligence which works automatically or give instructions to do a particular task.The ability to analyze vast amounts of data and identify patterns that are not easily discernible to the human eye makes ML an ideal tool for this task. By utilizing image processing techniques, machine learning models can be trained to recognize disease symptoms from images of plant leaves, stems, or fruits, offering a scalable, accurate, and efficient method of detecting diseases.

Copyright © 2024 Prof. Abhijeet A Thakare. This is an open access article distributed under the Creative Commons Attribution License.