Optimization method for accurate positioning seeding based on sowing decision

Zhao, Xueguan; Chen, Liping; Gao, Yuanyuan; Yang, Simon X.; Zhai, Changyuan

doi:10.25165/j.ijabe.20211403.5758

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…The overall MAPE for every year did not exceed 10%, which indicated high accuracy of the results of the germination assessment using multispectral data from the UAV. The presence of an error of approximately 10% was possible due to many factors, for example, the use of a breeding seeder without an accurate seeding system [60], varietal characteristics, soil, and climatic conditions.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Field Germination of Soybean Breeding Crops Using Multispectral Data from UAV

et al. 2023

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The use of multispectral aerial photography data contributes to the study of soybean plants by obtaining objective data. The evaluation of field germination of soybean crops was carried out using multispectral data (MSD). The purpose of this study was to develop ranges of field germination of soybean plants according to multispectral survey data from an unmanned aerial vehicle (UAV) for three years (2020, 2021, and 2022). As part of the ground-based research, the number of plants that sprang up per unit area was calculated and expressed as a percentage of the seeds sown. A DJI Matrice 200 Series v2 unmanned aerial vehicle and a MicaSense Altum multispectral camera were used for multispectral aerial photography. The correlation between ground-based and multispectral data was 0.70–0.75. The ranges of field germination of soybean breeding crops, as well as the vegetation indices (VIs) normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and chlorophyll index green (ClGreen) were calculated according to Sturges’ rule. The accuracy of the obtained ranges was estimated using the mean absolute percentage error (MAPE). The MAPE values did not exceed 10% for the ranges of the NDVI and ClGreen vegetation indices, and were no more than 18% for the NDRE index. The final values of the MAPE for the three years did not exceed 10%. The developed software for the automatic evaluation of the germination of soybean crops contributed to the assessment of the germination level of soybean breeding crops using multispectral aerial photography data. The software considers data of the three vegetation indices and calculated ranges, and creates an overview layer to visualize the germination level of the breeding plots. The developed method contributes to the determination of field germination for numerous breeding plots and speeds up the process of breeding new varieties.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Field Germination of Soybean Breeding Crops Using Multispectral Data from UAV

et al. 2023

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“…With the RS232 serial communication protocol based receiver and TTL level single-chip microcomputer, the level conversion circuit has been designed to realize the communication between the BeiDou signal receiver and the main controller. The main controller can acquire the current tractor forward speed by receiving the BeiDou NMEA0183 format navigation telegram and analyzing the data in the GPRMC frame [27] .…”

Section: Overall System Design and Working Principlementioning

confidence: 99%

Development of the peanut precision fertilization control system based on threshold velocity algorithm

Yang

Shang

et al. 2022

International Journal of Agricultural and Biological Engineering

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In view of the fact that the current ground wheel velocimetry of the peanut precision fertilizer control system cannot solve the phenomenon of ground wheel slippage, and signal interference and delay loss cannot be excluded by BeiDou positioning velocimetry, a set of peanut precision fertilizer control system was designed based on the threshold speed algorithm. The system used STM32F103ZET6 microcontroller as the main controller, and touch screen for setting the operating parameters such as operating width, fertilizer type, and fertilizer application amount. The threshold speed algorithm combined with BeiDou and ground wheel velocimetry was adopted to obtain the forward speed of the tractor and adjust the speed of the DC drive motor of the fertilizer applicator in real time to achieve precise fertilizer application. First, through the threshold speed algorithm test, the optimal value of the length N of the ground wheel speed measurement queue was determined as 3, and the threshold of the speed variation coefficient was set to 4.6%. Then, the response performance of the threshold speed algorithm was verified by comparative test with different fertilization amounts (40 kg/hm 2 , 50 kg/hm 2 , 60 kg/hm 2 , 70 kg/hm 2 ) under two speed acquisition methods of ground wheel speed measurement and threshold speed algorithm (combination of Beidou single-point speed and ground wheel speed measurement) in different operation speeds (3 km/h, 4 km/h, 5 km/h). The response performance test results showed that the average value of the velocimetry delay distance of the BeiDou single-point positioning velocimetry method was 0.58 m, while the average value of that with the threshold velocity algorithm was 0.27 m, which decreased by 0.31 m and indicated more accurate with the threshold velocity algorithm. The field comparison test for fertilizer application performance turned out an over 96.08% accuracy rate of fertilizer discharge by applied with the threshold speed algorithm, which effectively avoided the inaccurate fertilizer application caused by wheel slippage and raised the accuracy of fertilizer discharge by at least 1.2% compared with that of using the ground wheel velocimetry alone. The results showed that the threshold speed algorithm can meet the requirements of precise fertilizer application.

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“…Cotton seed is the foundation of cotton production, and its quality directly impacts cotton yield and quality [1,2]. The quality of cotton seed is under increasing scrutiny as the mechanized one-hole, one-seed precision sowing technology becomes more prevalent in China [3][4][5]. Phenotypic defects are one of the criteria for evaluating the quality of cotton seed.…”

Section: Introductionmentioning

confidence: 99%

A method for detecting the quality of cotton seeds based on an improved ResNet50 model

et al. 2023

PLoS ONE

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The accurate and rapid detection of cotton seed quality is crucial for safeguarding cotton cultivation. To increase the accuracy and efficiency of cotton seed detection, a deep learning model, which was called the improved ResNet50 (Impro-ResNet50), was used to detect cotton seed quality. First, the convolutional block attention module (CBAM) was embedded into the ResNet50 model to allow the model to learn both the vital channel information and spatial location information of the image, thereby enhancing the model’s feature extraction capability and robustness. The model’s fully connected layer was then modified to accommodate the cotton seed quality detection task. An improved LRelu-Softplus activation function was implemented to facilitate the rapid and straightforward quantification of the model training procedure. Transfer learning and the Adam optimization algorithm were used to train the model to reduce the number of parameters and accelerate the model’s convergence. Finally, 4419 images of cotton seeds were collected for training models under controlled conditions. Experimental results demonstrated that the Impro-ResNet50 model could achieve an average detection accuracy of 97.23% and process a single image in 0.11s. Compared with Squeeze-and-Excitation Networks (SE) and Coordination Attention (CA), the model’s feature extraction capability was superior. At the same time, compared with classical models such as AlexNet, VGG16, GoogLeNet, EfficientNet, and ResNet18, this model had superior detection accuracy and complexity balances. The results indicate that the Impro-ResNet50 model has a high detection accuracy and a short recognition time, which meet the requirements for accurate and rapid detection of cotton seed quality.

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Optimization method for accurate positioning seeding based on sowing decision

Cited by 6 publications

References 6 publications

Evaluation of Field Germination of Soybean Breeding Crops Using Multispectral Data from UAV

Evaluation of Field Germination of Soybean Breeding Crops Using Multispectral Data from UAV

Development of the peanut precision fertilization control system based on threshold velocity algorithm

A method for detecting the quality of cotton seeds based on an improved ResNet50 model

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