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

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 wh...

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Bibliographic Details
Published in:Agriculture (Basel) Vol. 13; no. 4; p. 872
Main Authors: Shi, Lei, Sun, Jiayue, Dang, Yuanbo, Zhang, Shaoqi, Sun, Xiaoyun, Xi, Lei, Wang, Jian
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.04.2023
Subjects:
Agricultural production
Agriculture
Algorithms
Crop yield
Crop yields
data collection
Datasets
Fertility
Flowering
Internet of Things
lightweight object detection
memory
Methods
mobile telephones
Neural networks
Object recognition
reproduction
Root-mean-square errors
spikelets
Wheat
wheat spikelet counting
yield estimation
ISSN:2077-0472, 2077-0472
Online Access:Get full text
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Summary: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 (R2) 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 R2 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.
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ISSN:2077-0472
2077-0472
DOI:10.3390/agriculture13040872