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Agronomy
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Advances in Intelligent Agricultural Development: From Technology to Field Management
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Advances in Intelligent Agricultural Development: From Technology to Field Management

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 18621

Special Issue Editors

Prof. Dr. Jinfeng Wang

E-Mail Website
Guest Editor
College of Engineering, Northeast Agricultural University, Harbin 150030, China
Interests: unmanned farm information perception and equipment; crop remote sensing and non-destructive testing; intelligent paddy field agricultural equipment
Special Issues, Collections and Topics in MDPI journals
Dr. Han Tang

E-Mail Website
Guest Editor
College of Engineering, Northeast Agricultural University, Harbin 150030, China
Interests: agricultural machinery; seeding system; CFD–DEM; soil properties
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoping Jiang

E-Mail Website
Guest Editor
College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Interests: precision seeding; smart irrigation; variable rate fertilization; variable spraying; intelligent production measurement; phenotypic monitoring

Special Issue Information

Dear Colleagues,

Intelligent agricultural technology plays an important role in the sustainable development of agriculture, and its development reflects the degree and level of agricultural modernization. In the last ten years, advanced agricultural equipment and technology have been used in agricultural management and production to achieve the modernization of agricultural production tools, constantly improve the level of agricultural production technology and obtain economic and ecological benefits. Topics of interest for this Special Issue include, but are not limited to, the following: field management key technologies and equipment, precision seeding/transplanting technology and equipment, field information perception technology and equipment, pest control technology and equipment, harvest loss reduction technology and equipment, and field intelligent equipment technology. In this Special Issue, we aim to focus on the weak links of field production and management and the needs of intelligent agricultural development, and to carry out basic theoretical research and field operation equipment research to promote sustainable agricultural development. Manuscripts are limited to research manuscripts and reviews.

Prof. Dr. Jinfeng Wang
Dr. Han Tang
Dr. Xiaoping Jiang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • field management
  • field information perception
  • harvest loss reduction technology
  • intelligent equipment
  • precision seeding
  • pest control
  • machine vision
  • hyperspectral

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Published Papers (7 papers)

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Research

19 pages, 8129 KiB  
Article
A Method for Determining the Nitrogen Content of Wheat Leaves Using Multi-Source Spectral Data and a Convolution Neural Network
by Jinyan Ju, Zhenyang Lv, Wuxiong Weng, Zongfeng Zou, Tenghui Lin, Yingying Liu, Zhentao Wang and Jinfeng Wang
Agronomy 2023, 13(9), 2387; https://doi.org/10.3390/agronomy13092387 - 14 Sep 2023
Cited by 6 | Viewed by 1697
Abstract
Accurate estimation of wheat leaf nitrogen concentration (LNC) is critical for characterizing ecosystem and plant physiological processes; it can further guide fertilization and other field management operations, and promote the sustainable development of agriculture. In this study, a wheat LNC test method based [...] Read more.
Accurate estimation of wheat leaf nitrogen concentration (LNC) is critical for characterizing ecosystem and plant physiological processes; it can further guide fertilization and other field management operations, and promote the sustainable development of agriculture. In this study, a wheat LNC test method based on multi-source spectral data and a convolutional neural network is proposed. First, interpolation reconstruction was performed on the wheat spectra data collected by different spectral instruments to ensure that the number of spectral channels and spectral range were consistent, and multi-source spectral data were constructed using interpolated, reconstructed imaging spectral data and non-imaging spectral data. Afterwards, the convolutional neural network DshNet and machine learning methods (PLSR, SVR, and RFR) were compared under various scenarios (non-imaging spectral data, imaging spectral data, and multi-source spectral data). Finally, the competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to optimize the LNC detection model. The results show that the model based on DshNet has the highest test accuracy. The CARS method is more suitable for DshNet model optimization than SPA. In the modeling scenario with non-imaging spectral, imaging spectral, and multi-source spectral, the optimized R2 is 0.86, 0.82, and 0.82, and the RMSE is 0.29, 0.31, and 0.31, respectively. The LNC visualization results show that DshNet modeling using multi-source spectral data is conducive to the visualization expansion of non-imaging spectral data. Therefore, the method presented in this paper provides new considerations for spectral data from different sources and is helpful for related research on the chemometric task of multi-source spectral data. Full article
(This article belongs to the Special Issue Advances in Intelligent Agricultural Development: From Technology to Field Management)
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19 pages, 6406 KiB  
Article
Tomato Recognition and Localization Method Based on Improved YOLOv5n-seg Model and Binocular Stereo Vision
by Shuhe Zheng, Yang Liu, Wuxiong Weng, Xuexin Jia, Shilong Yu and Zuoxun Wu
Agronomy 2023, 13(9), 2339; https://doi.org/10.3390/agronomy13092339 - 8 Sep 2023
Cited by 19 | Viewed by 3965
Abstract
Recognition and localization of fruits are key components to achieve automated fruit picking. However, current neural-network-based fruit recognition algorithms have disadvantages such as high complexity. Traditional stereo matching algorithms also have low accuracy. To solve these problems, this study targeting greenhouse tomatoes proposed [...] Read more.
Recognition and localization of fruits are key components to achieve automated fruit picking. However, current neural-network-based fruit recognition algorithms have disadvantages such as high complexity. Traditional stereo matching algorithms also have low accuracy. To solve these problems, this study targeting greenhouse tomatoes proposed an algorithm framework based on YOLO-TomatoSeg, a lightweight tomato instance segmentation model improved from YOLOv5n-seg, and an accurate tomato localization approach using RAFT-Stereo disparity estimation and least squares point cloud fitting. First, binocular tomato images were captured using a binocular camera system. The left image was processed by YOLO-TomatoSeg to segment tomato instances and generate masks. Concurrently, RAFT-Stereo estimated image disparity for computing the original depth point cloud. Then, the point cloud was clipped by tomato masks to isolate tomato point clouds, which were further preprocessed. Finally, a least squares sphere fitting method estimated the 3D centroid co-ordinates and radii of tomatoes by fitting the tomato point clouds to spherical models. The experimental results showed that, in the tomato instance segmentation stage, the YOLO-TomatoSeg model replaced the Backbone network of YOLOv5n-seg with the building blocks of ShuffleNetV2 and incorporated an SE attention module, which reduced model complexity while improving model segmentation accuracy. Ultimately, the YOLO-TomatoSeg model achieved an AP of 99.01% with a size of only 2.52 MB, significantly outperforming mainstream instance segmentation models such as Mask R-CNN (98.30% AP) and YOLACT (96.49% AP). The model size was reduced by 68.3% compared to the original YOLOv5n-seg model. In the tomato localization stage, at the range of 280 mm to 480 mm, the average error of the tomato centroid localization was affected by occlusion and sunlight conditions. The maximum average localization error was ±5.0 mm, meeting the localization accuracy requirements of the tomato-picking robots. This study developed a lightweight tomato instance segmentation model and achieved accurate localization of tomato, which can facilitate research, development, and application of fruit-picking robots. Full article
(This article belongs to the Special Issue Advances in Intelligent Agricultural Development: From Technology to Field Management)
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14 pages, 6675 KiB  
Article
Performance Analysis and Testing of a Multi-Duct Orchard Sprayer
by Zhanbiao Li, Xingyu Wang, Cui Li, Haipeng Lan, Yichuan He, Zhihui Tang and Yurong Tang
Agronomy 2023, 13(7), 1815; https://doi.org/10.3390/agronomy13071815 - 8 Jul 2023
Viewed by 1627
Abstract
A multi-duct orchard sprayer is designed in this paper to address the problem of low droplet coverage during plant protection in walnut orchards in South Xinjiang, China. This spray comprises a base frame, a pesticide tank, an air delivery system, a copying base [...] Read more.
A multi-duct orchard sprayer is designed in this paper to address the problem of low droplet coverage during plant protection in walnut orchards in South Xinjiang, China. This spray comprises a base frame, a pesticide tank, an air delivery system, a copying base frame and a drive system. Effects of the sprayer structure on the flow field were simulated using computational fluid dynamics. Operation parameters were optimised using the response surface analysis and validated using a field test. The results demonstrated that five jet tubes in the simulation design could fulfil the design requirements of GB/T 32250.3-2022. The upper, middle and lower droplet coverages were 74.92%, 90.01% and 69.9%, respectively, when the sprayer’s advancing speed, the angle of jet tubes and the nozzle diameter were 0.53 m/s, 70.27° and 0.81 mm, respectively. The machine can effectively enhance the spraying efficiency and droplet coverage of each canopy during plant protection operations in the new walnut orchards of South Xinjiang, China, and provide references for designing and optimising fruit tree spraying machinery. Full article
(This article belongs to the Special Issue Advances in Intelligent Agricultural Development: From Technology to Field Management)
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25 pages, 19606 KiB  
Article
Design and Experiment of a Targeted Variable Fertilization Control System for Deep Application of Liquid Fertilizer
by Wenqi Zhou, Tianhao An, Jinwu Wang, Qiang Fu, Nuan Wen, Xiaobo Sun, Qi Wang and Ziming Liu
Agronomy 2023, 13(7), 1687; https://doi.org/10.3390/agronomy13071687 - 23 Jun 2023
Cited by 10 | Viewed by 3430
Abstract
Given the problems of targeted variable deep application of liquid fertilizer in the field, such as low precision, inaccurate fertilization amount, and poor fertilization effect, a targeted variable fertilization control system of liquid fertilizer based on a fuzzy PID algorithm was designed in [...] Read more.
Given the problems of targeted variable deep application of liquid fertilizer in the field, such as low precision, inaccurate fertilization amount, and poor fertilization effect, a targeted variable fertilization control system of liquid fertilizer based on a fuzzy PID algorithm was designed in this study to realize the combination of precise variable fertilization technology and targeted deep-fertilization technology. Specifically, the fertilization equipment and adaptive fuzzy PID control strategy of targeted variable fertilization were designed first. Then, the mathematical model of the targeted variable fertilization control system of liquid fertilizer was established following the requirements of intertillage and fertilization of corn crops. Afterward, the response time and overshoot of the control system were simulated through the Simulink tool of MATLAB software, in which the fuzzy PID control and traditional PID control were compared. Then, the control effect of the targeted variable fertilization control system was verified through field experiments. The test results demonstrated that in the process of simulation analysis, the response time of the variable fertilization control system based on fuzzy PID control was shortened by nearly 5 s on average compared to the system based on traditional PID control, and the error was controlled within 10%. In the field test, the target rate of targeted variable fertilization equipment for liquid fertilizer reached more than 80%, and the control accuracy of the liquid fertilizer application amount also remained above 90%. Finally, the tracking experiment to check the fertilization effect proved that the targeted variable deep-fertilization method of liquid fertilizer could further improve the yield of maize crops under the premise of reducing the fertilization cost. The study provides a feasible solution for the method of precise variable fertilization combined with targeted fertilization. Full article
(This article belongs to the Special Issue Advances in Intelligent Agricultural Development: From Technology to Field Management)
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14 pages, 3688 KiB  
Article
Comparison of Prediction Models for Determining the Degree of Damage to Korla Fragrant Pears
by Shiyuan Li, Yang Liu, Xiyue Niu, Yurong Tang, Haipeng Lan and Yong Zeng
Agronomy 2023, 13(7), 1670; https://doi.org/10.3390/agronomy13071670 - 21 Jun 2023
Cited by 1 | Viewed by 1259
Abstract
For a fast and accurate evaluation of the values of damaged fragrant pears, a prediction method of the damage degree of Korla fragrant pears was proposed. To study variation laws of damages of fragrant pears under different volumes of squeezing deformation, the partial [...] Read more.
For a fast and accurate evaluation of the values of damaged fragrant pears, a prediction method of the damage degree of Korla fragrant pears was proposed. To study variation laws of damages of fragrant pears under different volumes of squeezing deformation, the partial least squares regression (PLSR), the generalised regression neural network (GRNN) and the adaptive neural fuzzy inference system (ANFIS) were chosen to predict the damage degree of fragrant pears and establish the optimal prediction model. The results demonstrated that with the increase of ripeness or deformation value, the damage degree of fragrant pears increases gradually. For performance comparison of prediction models based on PLSR, GRNN and ANFIS, it was found that the trained PLSR, GRNN and ANFIS can all predict the damage degree of Korla fragrant pears. The ANFIS, which inputs the membership function of dsigmf (R2 = 0.9979, RMSE = 46.6) and psigmf (R2 = 0.9979, RMSE = 46.6), achieves the best performance. Research results can provide theoretical references to the evaluation of the commodity value of damaged fragrant pears, quality grading of fragrant pears and design of the picking machine. Full article
(This article belongs to the Special Issue Advances in Intelligent Agricultural Development: From Technology to Field Management)
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13 pages, 4073 KiB  
Article
Effects of Threshing Devices, Maize Varieties and Moisture Content of Grains on the Percentage of Maize Grains Broken in Harvesting
by Xin Feng, Lijun Wang, Shengying Bi, Bo Wang, Zhao Ma and Yunpeng Gao
Agronomy 2023, 13(6), 1615; https://doi.org/10.3390/agronomy13061615 - 15 Jun 2023
Cited by 4 | Viewed by 2287
Abstract
Maize is one of the most important economic crops in the world. The integrity of maize grains directly affects the economic value of the maize as a main commodity that depends on the percentage of grains broken (POGB) in mechanized harvesting. Decreasing grain [...] Read more.
Maize is one of the most important economic crops in the world. The integrity of maize grains directly affects the economic value of the maize as a main commodity that depends on the percentage of grains broken (POGB) in mechanized harvesting. Decreasing grain processing breakage is key to achieving mechanical harvesting with high quality. It is difficult to ensure a low POGB, because it depends on different maize varieties, their moisture contents, threshing devices and harvester working speeds. In this paper, the effects of these factors on the POGB are investigated when the working speed of the harvester is 1.0 m·s−1, 1.5 m·s−1 and 2.0 m·s−1, respectively. The different threshing forms, including tangential-axial-flow (TAF), axial-flow with nail-tooth (AFN), axial-flow with rasp bar in big space (AFRBBS) and axial-flow with rasp bar in small space (AFRBSS), are summarized. The POGB of TAF was 5.4% and it was the lowest of four threshing devices when the working speed of the harvester was 1.0 m·s−1, which was suitable to thresh maize at a low working speed. Maize Demeiya No. 1 (DMYN1), XianDa205 (XD205), Demeiya No. 3 (DMYN3) and Hayu189 (HY189) were harvested at different harvester working speeds, and the POGB of maize DMYN1 was the lowest among the four maize varieties. The POGB increased with increases in the working speed of the harvester and the moisture content of the maize grains. The POGB reached a minimum value of 3.5% when maize XD205 with the lowest moisture content was harvested. The results can provide a reference for choosing the maize variety, its moisture content, the threshing device and the working speed of the harvester for decreasing grain processing breakage. Full article
(This article belongs to the Special Issue Advances in Intelligent Agricultural Development: From Technology to Field Management)
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21 pages, 6507 KiB  
Article
Design Optimization and Mechanism Analysis of Water Jet-Type Inter-Plant Weeding Device for Water Fields
by Wenqi Zhou, Kai Song, Xiaobo Sun, Qiang Fu, Yijia Wang, Qi Wang and Dongwei Yan
Agronomy 2023, 13(5), 1305; https://doi.org/10.3390/agronomy13051305 - 6 May 2023
Cited by 3 | Viewed by 2506
Abstract
Existing rice inter-plant weed control devices have difficulty achieving inter-plant weed control in one pass. Due to the complex environment of paddy fields, these devices have a low weed removal rate and high seedling damage rate, making it difficult to ensure high-quality operation. [...] Read more.
Existing rice inter-plant weed control devices have difficulty achieving inter-plant weed control in one pass. Due to the complex environment of paddy fields, these devices have a low weed removal rate and high seedling damage rate, making it difficult to ensure high-quality operation. This study innovatively designed a water jet-based rice inter-plant weed control device. Based on the mechanism of water jet erosion of soil, it can erode and excavate the soil layer on which weeds depend, achieving inter-plant weed control in paddy fields. The optimal range of structural parameters of the water jet angle and nozzle opening diameter was analyzed. The results showed that the optimal structural parameters of the device were a jet angle of 31° and a nozzle opening diameter of 4 mm, which can achieve the best operational performance. Based on virtual simulation experiments, single-factor and multi-factor orthogonal rotation combination experiments were carried out with weed removal rate as the test index and different operating speeds and nozzle outlet pressures as the test factors to optimize the water jet-based inter-plant weed control device. The experimental results showed that when the working parameters of the water jet-based inter-plant weed control device were a forward speed of 0.30 m∙s−1 and a nozzle outlet pressure of 1.50 MPa, the weed removal rate was the highest at 92.78%. Field validation experiments showed that the weed removal rate was 90.16% and the seedling damage rate was 1.80% under this operation condition, and the quality of the operation met the requirements of inter-plant weed control technology. This study provides a technical reference for promoting the development of inter-plant weed control technology in paddy fields. Full article
(This article belongs to the Special Issue Advances in Intelligent Agricultural Development: From Technology to Field Management)
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