YOLOv5s-T: A Lightweight Small Object Detection Method for Wheat Spikelet Counting

Lei Shi, Jiayue Sun, Yuanbo Dang, Shaoqi Zhang, Xiaoyun Sun, Lei Xi and Jian Wang ()
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Lei Shi: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Jiayue Sun: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Yuanbo Dang: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Shaoqi Zhang: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Xiaoyun Sun: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Lei Xi: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Jian Wang: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China

Agriculture, 2023, vol. 13, issue 4, 1-15

Abstract: Utilizing image data for yield estimation is a key topic in modern agriculture. This paper addresses the difficulty of counting wheat spikelets using images, to improve yield estimation in wheat fields. A wheat spikelet image dataset was constructed with images obtained by a smartphone, including wheat ears in the flowering, filling, and mature stages of reproduction. Furthermore, a modified lightweight object detection method, YOLOv5s-T, was incorporated. The experimental results show that the coefficient of determination ( R 2 ) between the predicted and true values of wheat spikelets was 0.97 for the flowering stage, 0.85 for the grain filling stage, and 0.78 for the mature stage. The R 2 in all three fertility stages was 0.87, and the root mean square error (RMSE) was 0.70. Compared with the original YOLOv5s algorithm, the spikelet detection counting effect of YOLOv5s-T was not reduced. Meanwhile, the model size was reduced by 36.8% (only 9.1 M), the GPU memory usage during the training process was reduced by 0.82 GB, the inference time was reduced by 2.3 ms, the processing time was reduced by 10 ms, and the calculation amount was also reduced. The proposed YOLOv5s-T algorithm significantly reduces the model size and hardware resource requirements while guaranteeing high detection and counting accuracy, which indicates the potential for wheat spikelet counting in highly responsive wheat yield estimation.

Keywords: yield estimation; lightweight object detection; wheat spikelet counting (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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