Research on Crop Detection Algorithm Based on Improved YOLOv7

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 appl...

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Bibliographic Details
Published inInternational journal of advanced network, monitoring, and controls Vol. 10; no. 2; pp. 10 - 19
Main Authors Shi, Xiaoqi, Ye, Xin
Format Journal Article
LanguageEnglish
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
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Summary: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.
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ISSN:2470-8038
2470-8038
DOI:10.2478/ijanmc-2025-0012