METHOD FOR PREDICTING PLANT DISEASES

Abstract

ABSTRACT A method (100) for predicting plant diseases. Further, the method comprising receiving an image of a plant leaf, flower, or fruit. Further, the method (100) comprising the steps of processing the image using a deep learning model. Further, the method (100) comprising the steps of classifying the plant as healthy or diseased based on the processed image. Further, the method (100) comprising the steps of providing a description and recommended treatment for the identified disease.

Specification

Description:
FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of agricultural technology and, in particular, to a method for predicting plant diseases using deep learning techniques. Specifically, it involves the development of an ensemble model that combines predictions from multiple pre-trained convolutional neural networks, aimed at enhancing the accuracy of disease classification in crops, thereby promoting sustainable farming practices and improving crop yield management.
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 th , Claims:1. A method (100) for predicting plant diseases, the method comprising the steps of:
receiving an image of a plant leaf, flower, or fruit;
processing the image using a deep learning model;
classifying the plant as healthy or diseased based on the processed image; and
providing a description and recommended treatment for the identified disease.

2. The method (100) as claimed in claim 1, wherein the deep learning model is an ensemble model that combines predictions from multiple pre-trained convolutional neural networks, enhancing the accuracy of the disease classification.

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