METHOD FOR OPTIMIZING WEED CLASSIFICATION

Abstract

ABSTRACT A method (100) for optimizing weed classification. Further, the method comprising acquiring a dataset of weed images, wherein the dataset includes multiple weed species. Further, the method (100) comprising the steps of pre-processing the dataset to enhance image quality and augment the dataset through techniques including rotation, flipping, and noise reduction. Further, the method (100) comprising the steps of applying a YOLO (You Only Look Once) object detection model to identify weeds in the pre-processed images. Further, the method (100) comprising the steps of optimizing hyper parameters of the YOLO model using Particle Swarm Optimization (PSO) to improve classification accuracy.

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 optimizing weed classification using machine learning techniques, specifically through the application of a YOLO (You Only Look Once) object detection model enhanced by Particle Swarm Optimization (PSO) to improve accuracy and efficiency in identifying and managing weed populations in agricultural settings.
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 increas , Claims:1. A method (100) for optimizing weed classification, the method comprising the steps of:
acquiring a dataset of weed images, wherein the dataset includes multiple weed species;
pre-processing the dataset to enhance image quality and augment the dataset through techniques including rotation, flipping, and noise reduction;
applying a YOLO (You Only Look Once) object detection model to identify weeds in the pre-processed images; and
optimizing hyper parameters of the YOLO model using Particle Swarm Optimization (PSO) to improve classification accuracy.

2. The method (100) as claimed in claim 1, wherein the pre-processing step further includes segmenting the images to isolate weed features from the background, thereby enhancing the accuracy of the YOLO model in identifying and classifying the weeds.

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