Crop phenotyping studies with application to crop monitoring

Jin, Xiuliang; Yang, Wanneng; Doonan, John; Atzberger, Clement

doi:10.1016/j.cj.2022.09.001

Cited by 9 publications

(4 citation statements)

References 35 publications

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“…Wheat ear counting is a labor-intensive work. Timely, accurate, and high-throughput acquisition of wheat ear information is crucial to improve wheat productivity ( Jin et al., 2022 ; Zhao et al., 2022 ; Zhu et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

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Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming

Xu,

Zhou,

et al. 2024

Front. Plant Sci.

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Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD. The precision, recall, F1 score and AP of the model on the test set are 95.85%, 94.71%, 95.28%, and 94.99%, respectively. The AP value is 1.67% higher than that of YOLOv7x, and 10.41%, 39.32%, 2.96%, and 0.22% higher than that of Faster RCNN, SSD, YOLOv5s, and YOLOv7. YOLOv7xSPD is combined with the Kalman filter tracking and the Hungarian matching algorithm to establish a wheat ear counting model with the video flow, called YOLOv7xSPD Counter, which can realize real-time counting of wheat ears in the field. In the video with a resolution of 3840×2160, the detection frame rate of YOLOv7xSPD Counter is about 5.5FPS. The counting results are highly correlated with the ground truth number (R2 = 0.99), and can provide model basis for wheat yield prediction, genetic breeding and optimized planting management.

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Section: Introductionmentioning

confidence: 99%

“…high-throughput acquisition of wheat ear information is crucial to improve wheat productivity (Jin et al, 2022;Zhao et al, 2022;Zhu et al, 2022).…”

mentioning

confidence: 99%

Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming

Xu,

Zhou,

et al. 2024

Front. Plant Sci.

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“…Phenotype monitoring of field crops requires acquisition of multi-scale, multi-sequential, and multi-source phenotyping information in a non-invasive, high-throughput manner in the real growth environment of crops. In recent years, research on phenotyping traits of field crops has transformed from low-throughput and extensive monitoring in a single environment to high-throughput precision monitoring for group shapes in complex environments [5]. To meet the demand for high-throughput phenotype monitoring for field crops, researchers have made great efforts to develop diverse phenotype monitoring systems for field crops.…”

Section: Introductionmentioning

confidence: 99%

“…traits of field crops has transformed from low-throughput and extensive monitoring in a single environment to high-throughput precision monitoring for group shapes in complex environments [5]. To meet the demand for high-throughput phenotype monitoring for field crops, researchers have made great efforts to develop diverse phenotype monitoring systems for field crops.…”

Section: Introductionmentioning

confidence: 99%

Field Phenotyping Monitoring Systems for High-Throughput: A Survey of Enabling Technologies, Equipment, and Research Challenges

Yuan,

Song,

Liu

et al. 2023

Agronomy

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High-throughput phenotype monitoring systems for field crops can not only accelerate the breeding process but also provide important data support for precision agricultural monitoring. Traditional phenotype monitoring methods for field crops relying on artificial sampling and measurement have some disadvantages including low efficiency, strong subjectivity, and single characteristics. To solve these problems, the rapid monitoring, acquisition, and analysis of phenotyping information of field crops have become the focus of current research. The research explores the systematic framing of phenotype monitoring systems for field crops. Focusing on four aspects, namely phenotyping sensors, mobile platforms, control systems, and phenotyping data preprocessing algorithms, the application of the sensor technology, structural design technology of mobile carriers, intelligent control technology, and data processing algorithms to phenotype monitoring systems was assessed. The research status of multi-scale phenotype monitoring products was summarized, and the merits and demerits of various phenotype monitoring systems for field crops in application were discussed. In the meantime, development trends related to phenotype monitoring systems for field crops in aspects including sensor integration, platform optimization, standard unification, and algorithm improvement were proposed.

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