Weed Identification Using Deep Learning and Image Processing in Vegetable Plantation

Abstract

Weed identification in vegetable plantation is more challenging than crop weed identification due to their random plant spacing. So far, little work has been found on identifying weeds in vegetable plantation. Traditional methods of crop weed identification used to be mainly focused on identifying weed directly; however, there is a large variation in weed species. This paper proposes a new method in a contrary way, which combines deep learning and image processing technology. Firstly, a trained CenterNet model was used to detect vegetables and draw bounding boxes around them. Afterwards, the remaining green objects falling out of bounding boxes were considered as weeds. In this way, the model focuses on identifying only the vegetables and thus avoid handling various weed species. Furthermore, this strategy can largely reduce the size of training image dataset as well as the complexity of weed detection, thereby enhancing the weed identification performance and accuracy. To extract weeds from the background, a color index-based segmentation was performed utilizing image processing. The employed color index was determined and evaluated through Genetic Algorithms (GAs) according to Bayesian classification error. During the field test, the trained CenterNet model achieved a precision of 95.6%, a recall of 95.0%, and a " score of 0.953, respectively. The proposed index-19R + 24G-2B ≥ 862 yields high segmentation quality with a much lower computational cost compared to the wildly used ExG index. These experiment results demonstrate the feasibility of using the proposed method for the ground-based weed identification in vegetable plantation.

Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves

“…However, a small proportion of ground objects that are not classified as tea in the ground multispectral images can affect the final quality of the parameter monitoring results. In order to reduce the influence of soil and shadow noise and improve the accuracy of the final quality parameter monitoring results, this study used the EXG index to effectively distinguish the background of green vegetation and soil for image enhancement [31,[79][80][81][82]. The Ostu method was used for image segmentation [32,[83][84][85][86] to enable the effective extraction of the tea areas from the original image that contains other features.…”

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

“…On-the-ground or remote sensing technologies can be used to capture weed images or non-imaging data. Previous research has shown that ground-based approaches (also known as proximal sensing) can capture high-resolution images, allowing for the early detection of substantially lower weed densities, and the discrimination of primary plant species [16,17]. Alternatively, traditional remote sensing platforms such as piloted airborne and satellite may investigate wider areas but have lower image spatial resolution [18].…”

Design of and Experiment with Seedling Selection System for Automatic Transplanter for Vegetable Plug Seedlings

“…Vegetables are rich in nutrients, such as vitamins, minerals, and dietary fiber, which are essential for the human body. They are important food sources and economic crops all around the world [1,2]. Plug seedling cultivation and transplantation are important procedures for vegetable production.…”