In-field rice panicles detection and growth stages recognition based on RiceRes2Net

Tan, Suiyan; Lu, Henghui; Yu, Jie; Lan, Maoyang; Hu, Xihong; Peng, Yingtong; Wang, Yuwei; Li, Zehua; Qi, Long; Xu, Ma

doi:10.1016/j.compag.2023.107704

Cited by 20 publications

(6 citation statements)

References 32 publications

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“…However, it is difficult to mark the same number of wheat spikes from canopy images at the filling and maturity stages due to the field environment and wheat growth [ 21 ]. During wheat growth stages, wheat changes significantly in color, size, and morphological features, allowing us to develop key technologies to accurately detect wheat spikes at several critical growth stages [ 47 ]. Therefore, it is necessary to develop a deep neural network for wheat spike detection suitable for multiple growth stages to enable accurate yield prediction.…”

Section: Discussionmentioning

confidence: 99%

Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet

Zhao,

Cai,

Wang

et al. 2023

Plant Phenomics

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Accurate wheat spike detection is crucial in wheat field phenotyping for precision farming. Advances in artificial intelligence have enabled deep learning models to improve the accuracy of detecting wheat spikes. However, wheat growth is a dynamic process characterized by important changes in the color feature of wheat spikes and the background. Existing models for wheat spike detection are typically designed for a specific growth stage. Their adaptability to other growth stages or field scenes is limited. Such models cannot detect wheat spikes accurately caused by the difference in color, size, and morphological features between growth stages. This paper proposes WheatNet to detect small and oriented wheat spikes from the filling to the maturity stage. WheatNet constructs a Transform Network to reduce the effect of differences in the color features of spikes at the filling and maturity stages on detection accuracy. Moreover, a Detection Network is designed to improve wheat spike detection capability. A Circle Smooth Label is proposed to classify wheat spike angles in drone imagery. A new micro-scale detection layer is added to the network to extract the features of small spikes. Localization loss is improved by Complete Intersection over Union to reduce the impact of the background. The results show that WheatNet can achieve greater accuracy than classical detection methods. The detection accuracy with average precision of spike detection at the filling stage is 90.1%, while it is 88.6% at the maturity stage. It suggests that WheatNet is a promising tool for detection of wheat spikes.

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Section: Discussionmentioning

confidence: 99%

Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet

Zhao,

Cai,

Wang

et al. 2023

Plant Phenomics

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“…proposed a pipeline using YOLOv5, DeepSORT for tracking identical panicles over time-series images and quantifying the effects of nitrogen on flowering duration and timing ( Zhou et al., 2023 ). The improved Cascade R-CNN is used to detect rice panicles and recognize growth stages from smartphone images under complex field conditions ( Tan et al., 2023 ). The estimated heading dates by counting flowering panicle regions in ground images under an indirectly image classification manner is also performed ( Desai et al., 2019 ).…”

Section: Introductionmentioning

confidence: 99%

High-throughput UAV-based rice panicle detection and genetic mapping of heading-date-related traits

Chen,

Lu,

Wang

et al. 2024

Front. Plant Sci.

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IntroductionRice (Oryza sativa) serves as a vital staple crop that feeds over half the world's population. Optimizing rice breeding for increasing grain yield is critical for global food security. Heading-date-related or Flowering-time-related traits, is a key factor determining yield potential. However, traditional manual phenotyping methods for these traits are time-consuming and labor-intensive.MethodHere we show that aerial imagery from unmanned aerial vehicles (UAVs), when combined with deep learning-based panicle detection, enables high-throughput phenotyping of heading-date-related traits. We systematically evaluated various state-of-the-art object detectors on rice panicle counting and identified YOLOv8-X as the optimal detector.ResultsApplying YOLOv8-X to UAV time-series images of 294 rice recombinant inbred lines (RILs) allowed accurate quantification of six heading-date-related traits. Utilizing these phenotypes, we identified quantitative trait loci (QTL), including verified loci and novel loci, associated with heading date.DiscussionOur optimized UAV phenotyping and computer vision pipeline may facilitate scalable molecular identification of heading-date-related genes and guide enhancements in rice yield and adaptation.

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“…By generating a series of bounding boxes containing the detected rice spikes and comparing the number of spikes in each box with manually labeled results, they demonstrated the accuracy of their system in rice detection and counting in the field. Tan et al [23] proposed a new architecture, RiceRes2Net, based on an enhanced Cascade RCNN, to enhance the efficiency of rice spike detection and to classify rice spike images captured by smart phones in the field. The utilization of deep learning models for spike detection in the above study showcases its effectiveness.…”

Section: Introductionmentioning

confidence: 99%

RICE-YOLO: In-Field Rice Spike Detection Based on Improved YOLOv5 and Drone Images

Lan,

Liu,

Zheng

et al. 2024

Agronomy

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The rice spike, a crucial part of rice plants, plays a vital role in yield estimation, pest detection, and growth stage management in rice cultivation. When using drones to capture photos of rice fields, the high shooting angle and wide coverage area can cause rice spikes to appear small in the captured images and can cause angular distortion of objects at the edges of images, resulting in significant occlusions and dense arrangements of rice spikes. These factors are unique challenges during drone image acquisition that may affect the accuracy of rice spike detection. This study proposes a rice spike detection method that combines deep learning algorithms with drone perspectives. Initially, based on an enhanced version of YOLOv5, the EMA (efficient multiscale attention) attention mechanism is introduced, a novel neck network structure is designed, and SIoU (SCYLLA intersection over union) is integrated. Experimental results demonstrate that RICE-YOLO achieves a mAP@0.5 of 94.8% and a recall of 87.6% on the rice spike dataset. During different growth stages, it attains an AP@0.5 of 96.1% and a recall rate of 93.1% during the heading stage, and a AP@0.5 of 86.2% with a recall rate of 82.6% during the filling stage. Overall, the results indicate that the proposed method enables real-time, efficient, and accurate detection and counting of rice spikes in field environments, offering a theoretical foundation and technical support for real-time and efficient spike detection in the management of rice growth processes.

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In-field rice panicles detection and growth stages recognition based on RiceRes2Net

Cited by 20 publications

References 32 publications

Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet

Small and Oriented Wheat Spike Detection at the Filling and Maturity Stages Based on WheatNet

High-throughput UAV-based rice panicle detection and genetic mapping of heading-date-related traits

RICE-YOLO: In-Field Rice Spike Detection Based on Improved YOLOv5 and Drone Images

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