Unsupervised Bayesian learning for rice panicle segmentation with UAV images

Hayat, Abul; Wu, Jingxian; Cao, Ye

doi:10.1186/s13007-020-00567-8

Cited by 20 publications

(15 citation statements)

References 23 publications

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“…We address the primary concern with image segmentation in eliminating the influence from soil background to solve these problems that greatly limit the reliability of the spectral imaging technology for crop nutrition diagnosis. The several alternative methods of spectral image segmentation include spectral segmentation methods using vegetation indices [21,22], threshold segmentation methods based on spatial grey value distribution [30,31], and learning-based segmentation methods [32]. The method based on the vegetation index mainly distinguishes green plants from the background according to the reflectance differences between the crop and the background special.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Influence Elimination and Chlorophyll Content Diagnosis of Maize Using UAV Spectral Imagery

et al. 2020

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In order to improve the diagnosis accuracy of chlorophyll content in maize canopy, the remote sensing image of maize canopy with multiple growth stages was acquired by using an unmanned aerial vehicle (UAV) equipped with a spectral camera. The dynamic influencing factors of the canopy multispectral images of maize were removed by using different image segmentation methods. The chlorophyll content of maize in the field was diagnosed. The crop canopy spectral reflectance, coverage, and texture information are combined to discuss the different segmentation methods. A full-grown maize canopy chlorophyll content diagnostic model was created on the basis of the different segmentation methods. Results showed that different segmentation methods have variations in the extraction of maize canopy parameters. The wavelet segmentation method demonstrated better advantages than threshold and ExG index segmentation methods. This method segments the soil background, reduces the texture complexity of the image, and achieves satisfactory results. The maize canopy multispectral band reflectance and vegetation index were extracted on the basis of the different segmentation methods. A partial least square regression algorithm was used to construct a full-grown maize canopy chlorophyll content diagnostic model. The result showed that the model accuracy was low when the image background was not removed (Rc2 (the determination coefficient of calibration set) = 0.5431, RMSEF (the root mean squared error of forecast) = 4.2184, MAE (the mean absolute error) = 3.24; Rv2 (the determination coefficient of validation set) = 0.5894, RMSEP (the root mean squared error of prediction) = 4.6947, and MAE = 3.36). The diagnostic accuracy of the chlorophyll content could be improved by extracting the maize canopy through the segmentation method, which was based on the wavelet segmentation method. The maize canopy chlorophyll content diagnostic model had the highest accuracy (Rc2 = 0.6638, RMSEF = 3.6211, MAE = 2.89; Rv2 = 0.6923, RMSEP = 3.9067, and MAE = 3.19). The research can provide a feasible method for crop growth and nutrition monitoring on the basis of the UAV platform and has a guiding significance for crop cultivation management.

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Section: Introductionmentioning

confidence: 99%

Dynamic Influence Elimination and Chlorophyll Content Diagnosis of Maize Using UAV Spectral Imagery

et al. 2020

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“…On rice panicle detection, Hayat et al (2020) applied the Bayesian learning method to perform rice panicle segmentation with UAV optical images. They used an unsupervised learning approach to detect the required features to replace the training phase, using the Markov chain Monte Carlo (MCMC) method with Gibbs sampling.…”

Section: Object Detection Application In Plant Sensingmentioning

confidence: 99%

Maize Tassel Detection From UAV Imagery Using Deep Learning

et al. 2021

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The timing of flowering plays a critical role in determining the productivity of agricultural crops. If the crops flower too early, the crop would mature before the end of the growing season, losing the opportunity to capture and use large amounts of light energy. If the crops flower too late, the crop may be killed by the change of seasons before it is ready to harvest. Maize flowering is one of the most important periods where even small amounts of stress can significantly alter yield. In this work, we developed and compared two methods for automatic tassel detection based on the imagery collected from an unmanned aerial vehicle, using deep learning models. The first approach was a customized framework for tassel detection based on convolutional neural network (TD-CNN). The other method was a state-of-the-art object detection technique of the faster region-based CNN (Faster R-CNN), serving as baseline detection accuracy. The evaluation criteria for tassel detection were customized to correctly reflect the needs of tassel detection in an agricultural setting. Although detecting thin tassels in the aerial imagery is challenging, our results showed promising accuracy: the TD-CNN had an F1 score of 95.9% and the Faster R-CNN had 97.9% F1 score. More CNN-based model structures can be investigated in the future for improved accuracy, speed, and generalizability on aerial-based tassel detection.

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“…Xiong et al (2017) proposed an algorithm to segment panicles based on superpixel regions generation, CNN and superpixel optimization and the F-measure was 76.73%. Hayat et al (2020) proposed an algorithm for rice panicle segmentation based on unsupervised Bayesian learning and the mean F1 score was 82.10%. Ma et al (2020) proposed EarSegNet based on semantic segmentation for winter wheat ears segmentation and the F1 score was 87.25%.…”

Section: Introductionmentioning

confidence: 99%

Field rice panicle detection and counting based on deep learning

Wang

Yang

et al. 2022

Front. Plant Sci.

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Panicle number is directly related to rice yield, so panicle detection and counting has always been one of the most important scientific research topics. Panicle counting is a challenging task due to many factors such as high density, high occlusion, and large variation in size, shape, posture et.al. Deep learning provides state-of-the-art performance in object detection and counting. Generally, the large images need to be resized to fit for the video memory. However, small panicles would be missed if the image size of the original field rice image is extremely large. In this paper, we proposed a rice panicle detection and counting method based on deep learning which was especially designed for detecting rice panicles in rice field images with large image size. Different object detectors were compared and YOLOv5 was selected with MAPE of 3.44% and accuracy of 92.77%. Specifically, we proposed a new method for removing repeated detections and proved that the method outperformed the existing NMS methods. The proposed method was proved to be robust and accurate for counting panicles in field rice images of different illumination, rice accessions, and image input size. Also, the proposed method performed well on UAV images. In addition, an open-access and user-friendly web portal was developed for rice researchers to use the proposed method conveniently.

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Unsupervised Bayesian learning for rice panicle segmentation with UAV images

Cited by 20 publications

References 23 publications

Dynamic Influence Elimination and Chlorophyll Content Diagnosis of Maize Using UAV Spectral Imagery

Dynamic Influence Elimination and Chlorophyll Content Diagnosis of Maize Using UAV Spectral Imagery

Maize Tassel Detection From UAV Imagery Using Deep Learning

Field rice panicle detection and counting based on deep learning

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