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

Shi, Lei; Sun, Jiang; Dang, Yuanbo; Zhang, Shaoqi; Sun, Xiaoyun; Xi, Lei; Wang, Jian

doi:10.3390/agriculture13040872

Cited by 8 publications

(7 citation statements)

References 61 publications

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“…Hammers et al (2023) detected spikelets based on VGG16, the ResNet152V2, and the EfficientNetV2L deep learning models. Shi et al (2023) detected spikelets in wheat images captured in the field. The authors implemented YOLOv5s-T network model to count spikelets and obtained R 2 between manual and deep learning counts from 0.85 to 0.97 depending on the plant development stage.…”

Section: Related Workmentioning

confidence: 99%

Image-based classification of wheat spikes by glume pubescence using convolutional neural networks

Artemenko,

Genaev,

Epifanov

et al. 2024

Front. Plant Sci.

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IntroductionPubescence is an important phenotypic trait observed in both vegetative and generative plant organs. Pubescent plants demonstrate increased resistance to various environmental stresses such as drought, low temperatures, and pests. It serves as a significant morphological marker and aids in selecting stress-resistant cultivars, particularly in wheat. In wheat, pubescence is visible on leaves, leaf sheath, glumes and nodes. Regarding glumes, the presence of pubescence plays a pivotal role in its classification. It supplements other spike characteristics, aiding in distinguishing between different varieties within the wheat species. The determination of pubescence typically involves visual analysis by an expert. However, methods without the use of binocular loupe tend to be subjective, while employing additional equipment is labor-intensive. This paper proposes an integrated approach to determine glume pubescence presence in spike images captured under laboratory conditions using a digital camera and convolutional neural networks.MethodsInitially, image segmentation is conducted to extract the contour of the spike body, followed by cropping of the spike images to an equal size. These images are then classified based on glume pubescence (pubescent/glabrous) using various convolutional neural network architectures (Resnet-18, EfficientNet-B0, and EfficientNet-B1). The networks were trained and tested on a dataset comprising 9,719 spike images.ResultsFor segmentation, the U-Net model with EfficientNet-B1 encoder was chosen, achieving the segmentation accuracy IoU = 0.947 for the spike body and 0.777 for awns. The classification model for glume pubescence with the highest performance utilized the EfficientNet-B1 architecture. On the test sample, the model exhibited prediction accuracy parameters of F1 = 0.85 and AUC = 0.96, while on the holdout sample it showed F1 = 0.84 and AUC = 0.89. Additionally, the study investigated the relationship between image scale, artificial distortions, and model prediction performance, revealing that higher magnification and smaller distortions yielded a more accurate prediction of glume pubescence.

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Section: Related Workmentioning

confidence: 99%

Image-based classification of wheat spikes by glume pubescence using convolutional neural networks

Artemenko,

Genaev,

Epifanov

et al. 2024

Front. Plant Sci.

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show abstract

Section: Related Workmentioning

confidence: 99%

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Plant Breeding

Orlov

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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“…As a result, the weight of the YOLOv3 model was reduced from 54.2 MB to 8.2 MB, while achieving a mean Average Precision (mAP) of 91.3%. Shi et al. (2023) constructed a dataset covering three growth stages of wheat: flowering, grain filling, and maturity.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As a result, the weight of the YOLOv3 model was reduced from 54.2 MB to 8.2 MB, while achieving a mean Average Precision (mAP) of 91.3%. Shi et al (2023) constructed a dataset covering three growth stages of wheat: flowering, grain filling, and maturity. After performing pruning on the YOLOv5s algorithm, they proposed a new wheat ear detection algorithm named YOLOv5s-t by altering the convolution kernel sizes in the spatial pyramid to reduce the number of model convolutions, thereby decreasing the parameter count of the model.…”

Section: Literature Reviewmentioning

confidence: 99%

A lightweight network for improving wheat ears detection and counting based on YOLOv5s

Shen,

Zhang,

Liu

et al. 2023

Front. Plant Sci.

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IntroductionRecognizing wheat ears plays a crucial role in predicting wheat yield. Employing deep learning methods for wheat ears identification is the mainstream method in current research and applications. However, such methods still face challenges, such as high computational parameter volume, large model weights, and slow processing speeds, making it difficult to apply them for real-time identification tasks on limited hardware resources in the wheat field. Therefore, exploring lightweight wheat ears detection methods for real-time recognition holds significant importance.MethodsThis study proposes a lightweight method for detecting and counting wheat ears based on YOLOv5s. It utilizes the ShuffleNetV2 lightweight convolutional neural network to optimize the YOLOv5s model by reducing the number of parameters and simplifying the complexity of the calculation processes. In addition, a lightweight upsampling operator content-aware reassembly of features is introduced in the feature pyramid structure to eliminate the impact of the lightweight process on the model detection performance. This approach aims to improve the spatial resolution of the feature images, enhance the effectiveness of the perceptual field, and reduce information loss. Finally, by introducing the dynamic target detection head, the shape of the detection head and the feature extraction strategy can be dynamically adjusted, and the detection accuracy can be improved when encountering wheat ears with large-scale changes, diverse shapes, or significant orientation variations.Results and discussionThis study uses the global wheat head detection dataset and incorporates the local experimental dataset to improve the robustness and generalization of the proposed model. The weight, FLOPs and mAP of this model are 2.9 MB, 2.5 * 109 and 94.8%, respectively. The linear fitting determination coefficients R2 for the model test result and actual value of global wheat head detection dataset and local experimental Site are 0.94 and 0.97, respectively. The improved lightweight model can better meet the requirements of precision wheat ears counting and play an important role in embedded systems, mobile devices, or other hardware systems with limited computing resources.

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

Cited by 8 publications

References 61 publications

Image-based classification of wheat spikes by glume pubescence using convolutional neural networks

Image-based classification of wheat spikes by glume pubescence using convolutional neural networks

Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Plant Breeding

A lightweight network for improving wheat ears detection and counting based on YOLOv5s

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