METHOD FOR PREDICTING TOMATO LEAF DISEASES

Abstract

ABSTRACT A method (100) for predicting tomato leaf diseases. Further, the method comprising collecting and pre-processing a dataset of annotated tomato leaf images, applying data augmentation techniques to enhance the training data. Further, the method (100) comprising the steps of implementing a CNN architecture with transfer learning, utilizing a pre-trained Inception model to extract features from the pre-processed images. Further, the method (100) comprising the steps of training the CNN model on the augmented dataset to classify the images into predetermined disease categories and validating the model's performance using a separate validation dataset. Further, the method (100) comprising the steps of integrating the trained model into a user-friendly interface that allows users to upload images of tomato leaves for real-time disease prediction. Further, the method (100) comprising the steps of providing output to the user that includes the predicted disease cla

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 tomato leaf diseases using machine learning and transfer learning techniques. The method is designed to enhance crop management by providing early and accurate disease identification, thereby promoting sustainable agricultural practices and reducing the reliance on chemical treatments.
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 tomatoes is , Claims:1. A method (100) for predicting tomato leaf diseases, the method comprising the steps of:
collecting and pre-processing a dataset of annotated tomato leaf images, applying data augmentation techniques to enhance the training data;
implementing a Convolutional Neural Network (CNN) architecture with transfer learning, utilizing a pre-trained Inception model to extract features from the pre-processed images;
training the CNN model on the augmented dataset to classify the images into predetermined disease categories and validating the model's performance using a separate validation dataset;
integrating the trained model into a user-friendly interface that allows users to upload images of tomato leaves for real-time disease prediction; and
providing output to the user that includes the predicted disease classification along with recommended management strategies, and continuously updating the model based on new data and feedback.

2. The method (100) as claimed in claim 1, wherein the data augmentation techniques

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