METHOD FOR DETECTING PLANT DISEASES

Abstract

ABSTRACT A method (100) for detecting plant diseases. Further, the method comprising collecting a dataset of labeled images of healthy and diseased plants. Further, the method (100) comprising the steps of pre-processing the collected images, wherein the pre-processing step includes resizing, normalization, and data augmentation to standardize input dimensions and enhance image quality. Further, the method (100) comprising the steps of training a convolutional neural network (CNN) on the pre-processed dataset. The training step includes adjusting the CNN's parameters using a suitable optimizer and loss function to learn distinguishing features between healthy and diseased plant images. Further, the method (100) comprising the steps of evaluating the performance of the trained CNN model using a separate test dataset to measure accuracy, precision, and recall. Further, the method (100) comprising the steps of deploying the trained CNN model in a user-friendly application for

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of agricultural technology and, in particular, relates to a method for detecting plant diseases using convolutional neural networks (CNNs) that enhances the accuracy and efficiency of disease identification, thereby aiding farmers in timely intervention and improving crop yield.
BACKGROUND
[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] The agricultural industry faces significant challenges due to the prevalence of plant diseases, which can lead , Claims:1. A method (100) for detecting plant diseases, the method comprising the steps of:
collecting a dataset of labeled images of healthy and diseased plants;
pre-processing the collected images, wherein the pre-processing step includes resizing, normalization, and data augmentation to standardize input dimensions and enhance image quality;
training a convolutional neural network (CNN) on the pre-processed dataset, wherein the training step includes adjusting the CNN's parameters using a suitable optimizer and loss function to learn distinguishing features between healthy and diseased plant images;
evaluating the performance of the trained CNN model using a separate test dataset to measure accuracy, precision, and recall; and
deploying the trained CNN model in a user-friendly application for real-time plant disease detection, enabling farmers to receive timely alerts and recommendations based on the model's predictions.

2. The method (100) as claimed in claim 1, wherein the pre-processing step further includes a

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