A vital component of contemporary agriculture, weed control has a direct impact on crop quality and output. Conventional weed-finding techniques, which often depend on human examination or the use of broad-spectrum herbicides, are labor-intensive and may cause environmental damage. This work uses image processing methods to propose an optimal method for weed identification. The suggested method takes high-resolution photos of agricultural fields and applies sophisticated image processing algorithms to differentiate weeds and crops with high accuracy. To categorize plant species, important aspects like color, texture, and form are taken out of the photos and sent to machine learning algorithms for analysis. Enhancing detection accuracy and cutting down on processing time are two benefits of the enhanced algorithms, which enable real-time applications of the system. This research demonstrates how well an image-processing method works for weed detection in crops. Where the weeds in the unordered harvest may also be identified, not simply the ones present together. Furthermore, we can identify the weed and provide assurance that it is present in the harvest not only by identifying evidence of weed in crop with an image but also by identifying the weed in recorded video. Farming may be maintained up to date when weeds are managed by providing important and fundamental processes in future horticulture frameworks and by applying inputs exactly where they are required. It provides quick and easy opportunities for managing and identifying weeds.

Agriculture, image processing, weed identification, and weed detection.