Xin Leng^#, Jiakai Chen^#, Jianping Huang^*, Lei Zhang, Zongxuan Li

Phyton-International Journal of Experimental Botany, Vol.93, No.11, pp. 2963-2981, 2024, DOI:10.32604/phyton.2024.057166 - 30 November 2024

Abstract Tea, a globally cultivated crop renowned for its unique flavor profile and health-promoting properties, ranks among the most favored functional beverages worldwide. However, diseases severely jeopardize the production and quality of tea leaves, leading to significant economic losses. While early and accurate identification coupled with the removal of infected leaves can mitigate widespread infection, manual leaves removal remains time-consuming and expensive. Utilizing robots for pruning can significantly enhance efficiency and reduce costs. However, the accuracy of object detection directly impacts the overall efficiency of pruning robots. In complex tea plantation environments, complex image backgrounds, the… More >

Zhongyuan Zhang, Wenqiu Zhu^*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2547-2563, 2024, DOI:10.32604/cmc.2024.048755 - 15 May 2024

Abstract Remote sensing imagery, due to its high altitude, presents inherent challenges characterized by multiple scales, limited target areas, and intricate backgrounds. These inherent traits often lead to increased miss and false detection rates when applying object recognition algorithms tailored for remote sensing imagery. Additionally, these complexities contribute to inaccuracies in target localization and hinder precise target categorization. This paper addresses these challenges by proposing a solution: The YOLO-MFD model (YOLO-MFD: Remote Sensing Image Object Detection with Multi-scale Fusion Dynamic Head). Before presenting our method, we delve into the prevalent issues faced in remote sensing imagery… More >