METHOD FOR DETECTING AND CLASSIFYING DISEASES IN FRUIT PLANTS

Abstract

ABSTRACT A method (100) for detecting and classifying diseases in fruit plants. Further, the method comprising collecting a dataset of images representing various fruit plants, both healthy and diseased. Further, the method (100) comprising the steps of pre-processing the images to standardize their size and normalize pixel values. Further, the method (100) comprising the steps of training a machine learning model using Convolutional Neural Networks (CNNs) on the pre-processed images to identify patterns associated with specific diseases.

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of agricultural technology and, in particular, to a method for detecting and classifying diseases in fruit plants using machine learning techniques, aimed at enhancing crop health and productivity while minimizing the reliance on chemical treatments and promoting sustainable farming practices.
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 cultivation of fruit plants is a vital component of global agriculture, contributing signifi , Claims:1. A method (100) for detecting and classifying diseases in fruit plants, the method comprising the steps of:
collecting a dataset of images representing various fruit plants, both healthy and diseased;
pre-processing the images to standardize their size and normalize pixel values; and
training a machine learning model using Convolutional Neural Networks (CNNs) on the pre-processed images to identify patterns associated with specific diseases.

2. The method (100) as claimed in claim 1, wherein the training of the machine learning model further includes utilizing data augmentation techniques to enhance the diversity of the training dataset by applying transformations such as rotation, flipping, and scaling to the images.

Documents