Research on Crop Detection Algorithm Based on Improved YOLOv7

• Published in: International journal of advanced network, monitoring, and controls Vol. 10; no. 2; pp. 10 - 19
• Main Authors: Shi, Xiaoqi, Ye, Xin
• Format: Journal Article
• Language: English
• Published: Xi'an Sciendo 16.06.2025; De Gruyter Poland
• Subjects: Accuracy; Agricultural production; Algorithms; Artificial intelligence; Attention Mechanism; Crop diseases; Crop Species Recognition; Crops; Datasets; Deep learning; Fruits; Labeling; Neural networks; Plant diseases; Target Detection; Vegetables; Vision systems; YOLOv7; China; India
• ISSN: 2470-8038; 2470-8038
• DOI: 10.2478/ijanmc-2025-0012

In the field of crop target detection, traditional target detection algorithms are often difficult to achieve satisfactory accuracy due to factors such as dense distribution of species and poor imaging quality, which brings many inconveniences and challenges in practical agricultural production applications. To address this situation, the study introduces an enhanced YOLOv7 algorithm, incorporating the attention mechanism, with the objective of substantially elevating the overall performance in crop target detection tasks. The improved algorithm can more accurately focus on the key features of crops by cleverly incorporating the attention mechanism, effectively filtering out the interference of complex background and noise, so as to achieve more accurate recognition of various crops. After a large amount of experimental data verification, the improved algorithm can achieve an average recognition accuracy of 80% for a variety of crops, with an average accuracy of 75%, and the highest recognition efficiency is as high as 91% in the detection of some specific crops. In contrast to other prominent crop target detection algorithms, the refined algorithm presented in this paper exhibits remarkable performance benefits. Notably, its target detection efficacy is highly significant, enabling swift and precise identification of crop species.