Article: An improved algorithm for sesame seedling and weed detection based on YOLOV7 Journal: International Journal of Wireless and Mobile Computing (IJWMC) 2024 Vol.26 No.3 pp.282 - 290 Abstract: Sesame is a widely cultivated oilseed and edible crop with significant economic and nutritional value. Weed infestation is a primary factor that hinders sesame growth, and effective weed control is crucial for optimal sesame yield. Accurate identification of sesame seedlings and weeds can guide intelligent devices to improve weed control. However, low recognition accuracy and high miss-detection rates persist as major issues. To address these challenges, we propose the YOLOV7-G algorithm, which builds upon the YOLOV7 baseline network. Our approach integrates the SimAM attention mechanism in the feature extraction structure to focus the model on the morphological features of weeds and incorporates the C3 module into the backbone network to increase the perceptual field range. We also employ the SPPFCSPC module in the Neck part to replace convolutional kernels of different sizes with stacked 5×5 convolutional kernels to reduce computational effort while maintaining the original perceptual field. Finally, we utilise the Focal-SIoU loss function to improve the regression accuracy of the prediction frame. Experimental results demonstrate that the YOLOV7-G algorithm out-performs YOLOV7, achieving a 17% increase in average precision a 5.1% increase in mAP@0.5. Inderscience Publishers - linking academia, business and industry through research

Title: An improved algorithm for sesame seedling and weed detection based on YOLOV7

Authors: Gan Yu; Huimin Sun; Zhuolei Xiao; Chaoyue Dai

Addresses: School of Computer and Information Engineering, Fuyang Normal University, Fuyang, Anhui, China ' School of Computer and Information Engineering, Fuyang Normal University, Fuyang, Anhui, China ' School of Computer and Information Engineering, Fuyang Normal University, Fuyang, Anhui, China ' School of Computer and Information Engineering, Fuyang Normal University, Fuyang, Anhui, China

Abstract: Sesame is a widely cultivated oilseed and edible crop with significant economic and nutritional value. Weed infestation is a primary factor that hinders sesame growth, and effective weed control is crucial for optimal sesame yield. Accurate identification of sesame seedlings and weeds can guide intelligent devices to improve weed control. However, low recognition accuracy and high miss-detection rates persist as major issues. To address these challenges, we propose the YOLOV7-G algorithm, which builds upon the YOLOV7 baseline network. Our approach integrates the SimAM attention mechanism in the feature extraction structure to focus the model on the morphological features of weeds and incorporates the C3 module into the backbone network to increase the perceptual field range. We also employ the SPPFCSPC module in the Neck part to replace convolutional kernels of different sizes with stacked 5×5 convolutional kernels to reduce computational effort while maintaining the original perceptual field. Finally, we utilise the Focal-SIoU loss function to improve the regression accuracy of the prediction frame. Experimental results demonstrate that the YOLOV7-G algorithm out-performs YOLOV7, achieving a 17% increase in average precision a 5.1% increase in mAP@0.5.

Keywords: SPPFCSPC; YOLOV7; SimAM attention mechanism; Focal-SIoU; C3.

DOI: 10.1504/IJWMC.2024.137859

International Journal of Wireless and Mobile Computing, 2024 Vol.26 No.3, pp.282 - 290

Received: 05 Jul 2023
Accepted: 23 Sep 2023
Published online: 05 Apr 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: An improved algorithm for sesame seedling and weed detection based on YOLOV7

Keep up-to-date