METHOD FOR DETECTING PLANT HEALTH USING DEEP LEARNING

Abstract

ABSTRACT A method (100) for detecting plant health issues using deep learning. Further, the method comprising collecting image data of plant leaves from a crop field. Further, the method (100) comprising the steps of pre-processing the collected images by performing operations including rotating, resizing, and rescaling to prepare them for model training. Further, the method (100) comprising the steps of augmenting the pre-processed images to create a more comprehensive dataset suitable for training. Further, the method (100) comprising the steps of training a convolutional neural network (CNN) model on the augmented dataset to recognize patterns indicative of plant diseases. Further, the method (100) comprising the steps of applying the trained model to classify new images of plant leaves to detect health issues with an accuracy of at least 93.84%. <>

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of agricultural technology and plant health monitoring, and in particular to a method for detecting plant health issues using deep learning techniques, enabling early identification of plant diseases and stress factors to improve crop productivity and sustainability in agricultural 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] Agricultural productivity and food security are closely linked to the health of plants. Any signi , Claims:1. A method (100) for detecting plant health issues using deep learning, the method comprising the steps of:
collecting image data of plant leaves from a crop field;
pre-processing the collected images by performing operations including rotating, resizing, and rescaling to prepare them for model training;
augmenting the pre-processed images to create a more comprehensive dataset suitable for training;
training a convolutional neural network (CNN) model on the augmented dataset to recognize patterns indicative of plant diseases; and
applying the trained model to classify new images of plant leaves to detect health issues with an accuracy of at least 93.84%.

2. The method (100) as claimed in claim 1, wherein the convolutional neural network (CNN) model is further trained to distinguish between multiple types of plant diseases by classifying specific patterns unique to each disease type.

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