Agronomy

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4 pages, 158 KiB

Open AccessEditorial

Agricultural Unmanned Systems: Empowering Agriculture with Automation

by Mengke Zhang and Shubo Wang

Agronomy 2024, 14(6), 1203; https://doi.org/10.3390/agronomy14061203 - 2 Jun 2024

Viewed by 1448

Abstract

Automation is crucial for the advancement of modern agriculture [...] Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

Research

Jump to: Editorial, Review

27 pages, 3941 KiB

Open AccessArticle

Precision Inter-Row Relative Positioning Method by Using 3D LiDAR in Planted Forests and Orchards

by Limin Liu, Dong Ji, Fandi Zeng, Zhihuan Zhao and Shubo Wang

Agronomy 2024, 14(6), 1279; https://doi.org/10.3390/agronomy14061279 - 13 Jun 2024

Cited by 1 | Viewed by 873

Abstract

Accurate positioning at the inter-row canopy can provide data support for precision variable-rate spraying. Therefore, there is an urgent need to design a reliable positioning method for the inter-row canopy of closed orchards (planted forests). In the study, the Extended Kalman Filter (EKF) [...] Read more.

Accurate positioning at the inter-row canopy can provide data support for precision variable-rate spraying. Therefore, there is an urgent need to design a reliable positioning method for the inter-row canopy of closed orchards (planted forests). In the study, the Extended Kalman Filter (EKF) fusion positioning method (method C) was first constructed by calibrating the IMU and encoder with errors. Meanwhile, 3D Light Detection and Ranging (LiDAR) observations were introduced to be fused into Method C. An EKF fusion positioning method (method D) based on 3D LiDAR corrected detection was designed. The method starts or closes method C by the presence or absence of the canopy. The vertically installed 3D LiDAR detected the canopy body center, providing the vehicle with inter-row vertical distance and heading. They were obtained through the distance between the center of the body and fixed row spacing. This can provide an accurate initial position for method C and correct the positioning trajectory. Finally, the positioning and canopy length measurement experiments were designed using a GPS positioning system. The results show that the method proposed in this study can significantly improve the accuracy of length measurement and positioning at the inter-row canopy, which does not significantly change with the distance traveled. In the orchard experiment, the average positioning deviations of the lateral and vertical distances at the inter-row canopy are 0.1 m and 0.2 m, respectively, with an average heading deviation of 6.75°, and the average relative error of canopy length measurement was 4.35%. The method can provide a simple and reliable inter-row positioning method for current remote-controlled and manned agricultural machinery when working in standardized 3D crops. This can modify the above-mentioned machinery to improve its automation level. Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

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13 pages, 12166 KiB

Open AccessEditor’s ChoiceArticle

Enhanced Estimation of Rice Leaf Nitrogen Content via the Integration of Hybrid Preferred Features and Deep Learning Methodologies

by Yiping Peng, Wenliang Zhong, Zhiping Peng, Yuting Tu, Yanggui Xu, Zhuxian Li, Jianyi Liang, Jichuan Huang, Xu Liu and Youqiang Fu

Agronomy 2024, 14(6), 1248; https://doi.org/10.3390/agronomy14061248 - 9 Jun 2024

Cited by 3 | Viewed by 1102

Abstract

Efficiently obtaining leaf nitrogen content (LNC) in rice to monitor the nutritional health status is crucial in achieving precision fertilization on demand. Unmanned aerial vehicle (UAV)-based hyperspectral technology is an important tool for determining LNC. However, the intricate coupling between spectral information and [...] Read more.

Efficiently obtaining leaf nitrogen content (LNC) in rice to monitor the nutritional health status is crucial in achieving precision fertilization on demand. Unmanned aerial vehicle (UAV)-based hyperspectral technology is an important tool for determining LNC. However, the intricate coupling between spectral information and nitrogen remains elusive. To address this, this study proposed an estimation method for LNC that integrates hybrid preferred features with deep learning modeling algorithms based on UAV hyperspectral imagery. The proposed approach leverages XGBoost, Pearson correlation coefficient (PCC), and a synergistic combination of both to identify the characteristic variables for LNC estimation. We then construct estimation models of LNC using statistical regression methods (partial least-squares regression (PLSR)) and machine learning algorithms (random forest (RF); deep neural networks (DNN)). The optimal model is utilized to map the spatial distribution of LNC at the field scale. The study was conducted at the National Agricultural Science and Technology Park, Guangzhou, located in Baiyun District of Guangdong, China. The results reveal that the combined PCC-XGBoost algorithm significantly enhances the accuracy of rice nitrogen inversion compared to the standalone screening approach. Notably, the model built with the DNN algorithm exhibits the highest predictive performance and demonstrates great potential in mapping the spatial distribution of LNC. This indicates the potential role of the proposed model in precision fertilization and the enhancement of nitrogen utilization efficiency in rice cultivation. The outcomes of this study offer a valuable reference for enhancing agricultural practices and sustainable crop management. Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

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38 pages, 20974 KiB

Open AccessEditor’s ChoiceArticle

A Path Planning System for Orchard Mower Based on Improved A* Algorithm

by Mengke Zhang, Xiaoguang Li, Ling Wang, Liujian Jin and Shubo Wang

Agronomy 2024, 14(2), 391; https://doi.org/10.3390/agronomy14020391 - 18 Feb 2024

Cited by 5 | Viewed by 1697

Abstract

The application of intelligent mobile robots in agriculture has emerged as a new research frontier, with the integration of autonomous navigation technology and intelligent agricultural robots being the key to the widespread adoption of smart agricultural machinery. This paper investigates comprehensive coverage path [...] Read more.

The application of intelligent mobile robots in agriculture has emerged as a new research frontier, with the integration of autonomous navigation technology and intelligent agricultural robots being the key to the widespread adoption of smart agricultural machinery. This paper investigates comprehensive coverage path planning for tracked lawnmowers within orchard environments and addresses challenges related to task allocation and battery life. Firstly, in this study, the motion model of the tracked lawnmower was initially simplified based on assumptions about the orchard environment. Force analyses were conducted on each of its motion mechanisms. For the known orchard environment, a grid-based mapping technique was employed to model the orchard environment. Then, in order to improve the algorithm speed and reduce the number of turns during the lawnmower’s traversal, the A* search algorithm was enhanced by combining the method of robot cluster traversal in the orchard environment. Finally, the improved method was simulated and verified in the MATLAB platform to investigate the influence of the number of lawnmower clusters on the path planning in the connected and non-connected orchards. Furthermore, two sets of on-site field trials were meticulously designed to validate the reliability, practicality, and efficacy of the simulation experiments. Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

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30 pages, 9499 KiB

Open AccessArticle

Construction and Optimization of a Collaborative Harvesting System for Multiple Robotic Arms and an End-Picker in a Trellised Pear Orchard Environment

by Hewen Zhang, Xiaoguang Li, Ling Wang, Dian Liu and Shubo Wang

Agronomy 2024, 14(1), 80; https://doi.org/10.3390/agronomy14010080 - 28 Dec 2023

Cited by 7 | Viewed by 1399

Abstract

In order to meet the needs of intensive mechanized picking in trellised pear orchards, this paper designed a pick-place integrated end-picker based on the analysis of agronomic characteristics of trellised pear gardens and fruit. In order to realize the accurate positioning of pears [...] Read more.

In order to meet the needs of intensive mechanized picking in trellised pear orchards, this paper designed a pick-place integrated end-picker based on the analysis of agronomic characteristics of trellised pear gardens and fruit. In order to realize the accurate positioning of pears in picking, based on the kinematic analysis of robot arms and the construction of a private dataset, the YOLOv5s object detection algorithm was used in conjunction with a depth camera to achieve fruit positioning. The hand–eye system calibration was carried out. Aiming at solving the problems of redundancy, inefficiency, and uneven distribution of task volume in the conventional multiple robot arms algorithm, a simulated annealing algorithm was introduced to optimize the picking sequence, and a task allocation method was proposed. On the basis of studying several key parameters affecting the performance of the algorithm, the picking efficiency was greatly optimized. And the effectiveness of the proposed multi-robot collaborative picking method in a trellised pear orchard environment was demonstrated through experiments and simulation verification. The experiments showed that the picking efficiency of the integrated end-picker was increased by about 30%, and the success rate was significantly higher than that of the flexible grippers. The results of this study can be utilized to advance robotic pear-picking research and development. Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

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16 pages, 5360 KiB

Open AccessArticle

Enhanced Neural Network for Rapid Identification of Crop Water and Nitrogen Content Using Multispectral Imaging

by Yaoqi Peng, Mengzhu He, Zengwei Zheng and Yong He

Agronomy 2023, 13(10), 2464; https://doi.org/10.3390/agronomy13102464 - 23 Sep 2023

Cited by 2 | Viewed by 1122

Abstract

Precision irrigation and fertilization in agriculture are vital for sustainable crop production, relying on accurate determination of the crop’s nutritional status. However, there are challenges in optimizing traditional neural networks to achieve this accurately. This paper aims to propose a rapid identification method [...] Read more.

Precision irrigation and fertilization in agriculture are vital for sustainable crop production, relying on accurate determination of the crop’s nutritional status. However, there are challenges in optimizing traditional neural networks to achieve this accurately. This paper aims to propose a rapid identification method for crop water and nitrogen content using optimized neural networks. This method addresses the difficulty in optimizing the traditional backpropagation neural network (BPNN) structure. It uses 179 multi−spectral images of crops (such as maize) as samples for the neural network model. Particle swarm optimization (PSO) is applied to optimize the hidden layer nodes. Additionally, this paper proposes a double−hidden−layer network structure to improve the model’s prediction accuracy. The proposed double−hidden−layer PSO−BPNN model showed a 9.87% improvement in prediction accuracy compared with the traditional BPNN model. The correlation coefficient R² for predicted crop nitrogen and water content was 0.9045 and 0.8734, respectively. The experimental results demonstrate high training efficiency and accuracy. This method lays a strong foundation for developing precision irrigation and fertilization plans for modern agriculture and holds promising prospects. Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

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15 pages, 3542 KiB

Open AccessArticle

The Medium-Blocking Discharge Vibration-Uniform Material Plasma Seed Treatment Device Based on EDEM

by Yunting Hui, Chen Huang, Yangyang Liao, Decheng Wang, Yong You and Xu Bai

Agronomy 2023, 13(8), 2055; https://doi.org/10.3390/agronomy13082055 - 3 Aug 2023

Cited by 3 | Viewed by 1187

Abstract

Pre-sowing treatment of seeds by plasma can improve seed vigor and promote seed germination and growth. To solve the problems of low processing volume and uneven treatment in plasma seed treatment devices, according to the process scheme of medium-blocking discharge plasma seed treatment, [...] Read more.

Pre-sowing treatment of seeds by plasma can improve seed vigor and promote seed germination and growth. To solve the problems of low processing volume and uneven treatment in plasma seed treatment devices, according to the process scheme of medium-blocking discharge plasma seed treatment, a medium-blocking discharge vibration-uniform material plasma seed treatment device was designed, the structure and working principle of the vibration-uniform material device were systematically analyzed, and the mathematical model of seed force was established. According to electromagnetic vibration theory, the seed sorting and conveying principles were analyzed in the lower trough, and the relevant parameters were selected and calculated. Using EDEM discrete element simulation software, a numerical simulation of alfalfa seed feeding and vibration-uniform material process was carried out. A three-factor, three-level orthogonal test was established. The results showed that the vibration amplitude and groove shape significantly affected the coefficient of variation of seed uniformity on the groove during the seed feeding and vibration-uniform material processes, and the groove wheel speed had a certain effect on the coefficient of variation of uniformity. The main order of factors affecting the uniformity of seed spreading was vibration amplitude B > notch shape C > speed A. The optimal speed was 35 r/min, the optimal notch shape was circular, and the optimal vibration amplitude was 0.55 mm. Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

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15 pages, 4357 KiB

Open AccessArticle

Transfer Learning-Based Lightweight SSD Model for Detection of Pests in Citrus

by Linhui Wang, Wangpeng Shi, Yonghong Tang, Zhizhuang Liu, Xiongkui He, Hongyan Xiao and Yu Yang

Agronomy 2023, 13(7), 1710; https://doi.org/10.3390/agronomy13071710 - 26 Jun 2023

Cited by 9 | Viewed by 1960

Abstract

In citrus cultivation, it is a difficult task for farmers to classify different pests correctly and make proper decisions to prevent citrus damage. This work proposes an efficient modified lightweight transfer learning model which combines the effectiveness and accuracy of citrus pest characterization [...] Read more.

In citrus cultivation, it is a difficult task for farmers to classify different pests correctly and make proper decisions to prevent citrus damage. This work proposes an efficient modified lightweight transfer learning model which combines the effectiveness and accuracy of citrus pest characterization with mobile terminal counting. Firstly, we utilized typical transfer learning feature extraction networks such as ResNet50, InceptionV3, VGG16, and MobileNetV3, and pre-trained the single-shot multibox detector (SSD) network to compare and analyze the classification accuracy and efficiency of each model. Then, to further reduce the amount of calculations needed, we miniaturized the prediction convolution kernel at the end of the model and added a residual block of a 1 × 1 convolution kernel to predict category scores and frame offsets. Finally, we transplanted the preferred lightweight SSD model into the mobile terminals developed by us to verify its usability. Compared to other transfer learning models, the modified MobileNetV3+RPBM can enable the SSD network to achieve accurate detection of Panonychus Citri Mcgregor and Aphids, with a mean average precision (mAP) up to 86.10% and the counting accuracy reaching 91.0% and 89.0%, respectively. In terms of speed, the mean latency of MobileNetV3+RPBM is as low as 185 ms. It was concluded that this novel and efficient modified MobileNetV3+RPBM+SSD model is effective at classifying citrus pests, and can be integrated into devices that are embedded for mobile rapid detection as well as for counting pests in citrus orchards. The work presented herein can help encourage farm managers to judge the degree of pest damage and make correct decisions regarding pesticide application in orchard management. Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

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15 pages, 4022 KiB

Open AccessArticle

Analysis of Near-Infrared Spectral Properties and Quantitative Detection of Rose Oxide in Wine

by Xuebing Bai, Yaqiang Xu, Xinlong Chen, Binxiu Dai, Yongsheng Tao and Xiaolin Xiong

Agronomy 2023, 13(4), 1123; https://doi.org/10.3390/agronomy13041123 - 14 Apr 2023

Cited by 3 | Viewed by 1795

Abstract

This study aims to investigate the near-infrared spectral properties of Rose Oxide (4-Methyl-2-(2-methyl-1-propenyl) tetrahydropyran) in wine, establish a quantitative detection, and build relationships between the chemical groups of Rose Oxide and near-infrared characteristic bands, so as to provide ideas and references for the [...] Read more.

This study aims to investigate the near-infrared spectral properties of Rose Oxide (4-Methyl-2-(2-methyl-1-propenyl) tetrahydropyran) in wine, establish a quantitative detection, and build relationships between the chemical groups of Rose Oxide and near-infrared characteristic bands, so as to provide ideas and references for the near-infrared detection of a low-content aroma substance in wine. In total, 133 samples with different wine matrices were analyzed using Fourier transform–near-infrared (FT-NIR) spectroscopy. Min–max normalization (MMN), principal component analysis (PCA), and synergy interval partial least squares regression (Si-PLSR) were used for pre-processing, outlier rejection, analysis of spectral properties, and modeling. Finally, the quantitative detection model was established using the PLSR method and the wine sample containing Rose Oxide was verified externally. Eight subintervals (4000–4400 cm⁻¹, 4400–4800 cm⁻¹, 5600–6000 cm⁻¹, 6000–6400 cm⁻¹, 6400–6800 cm⁻¹, 6800–7200 cm⁻¹, 7200–7600 cm⁻¹, 8400–8800 cm⁻¹) were determined as the characteristic band intervals of Rose Oxide in the NIR region. Among them, 5600–6000 cm⁻¹ was assigned to the first overtone C–H stretching in tetrahydropyran ring and methyl as well as the combination C–H stretching of the CH₃ function groups, 6000–6400 cm⁻¹ was assigned to the first overtone C–H stretching of the C–H=group and the combination C=C stretching in isobutyl, and 8400–8800 cm⁻¹ was assigned to the second overtone C–H stretching and C–O stretching in tetrahydropyran ring as well as the C–H stretching vibration in methyl. In addition, 4000–4800 cm⁻¹, 6400–6800 cm⁻¹, and 7200–7600 cm⁻¹ were assigned to the C–H stretching vibration, while 6400–7600 cm⁻¹ was assigned to the C–O stretching vibration. The training result showed that the calibration model (

r_{c v}^{2}

of 0.96 and

R M S E_{C V}

of 2.33) and external validation model (

r_{c v}^{2}

of 0.84 and

R M S E_{C V}

of 2.72) of Rose Oxide in wine were acceptable, indicating a good predictive ability. The spectral assignment of Rose Oxide provides a new way for the NIR study of other terpenes in wine, and the use of the established Si-PLSR model for the rapid determination of Rose Oxide content in wine is feasible. Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

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13 pages, 4525 KiB

Open AccessArticle

Construction and Test of Baler Feed Rate Detection Model Based on Power Monitoring

by Huaiyu Liu, Ning Gao, Zhijun Meng, Anqi Zhang, Changkai Wen, Hanqing Li and Jing Zhang

Agronomy 2023, 13(2), 425; https://doi.org/10.3390/agronomy13020425 - 31 Jan 2023

Cited by 3 | Viewed by 1586

Abstract

The existing methods of measuring the baler feed rate seldom consider the influence of machine vibration on the sensor signal during field operation, which leads to the low detection accuracy and poor stability of feeding quantity detection. We established a feed rate detection [...] Read more.

The existing methods of measuring the baler feed rate seldom consider the influence of machine vibration on the sensor signal during field operation, which leads to the low detection accuracy and poor stability of feeding quantity detection. We established a feed rate detection model of a baler based on power monitoring of the pickup platform. Through the dynamic analysis of the pickup platform, the functional relationship between the working power of the pickup platform and the feed rate was constructed. A power monitoring system of the pickup platform was developed, and the model construction experiment of the working power and the feed rate was performed. The influence mechanism of different running speeds on the torque noise signal of the power input shaft of the pickup platform was explored. The frequency of the noise signal was mainly concentrated at 0.5–6 Hz and 9–13 Hz employing a fast Fourier transform, and the noise signal was eliminated by the frequency-domain-filtering method. The function model of working power and feed rate of the pickup platform was established based on signal processing, and the determination coefficient R² of the model was 0.9796. The field experiment results show that when the feed rate of the baler is between 1.6 and 4.88 kg/s, the determination coefficient R² and RMSE between the actual and predicted feed rate are 0.989 and 0.2, respectively. The relative error range of feed-rate prediction is −9.37–8.77%, which indicates that the model has high detection accuracy and good stability and meets the requirements of feed-rate monitoring of a baler in field operation. Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

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16 pages, 6027 KiB

Open AccessArticle

Design and Test of Obstacle Detection and Harvester Pre-Collision System Based on 2D Lidar

by Yehua Shang, Hao Wang, Wuchang Qin, Qian Wang, Huaiyu Liu, Yanxin Yin, Zhenghe Song and Zhijun Meng

Agronomy 2023, 13(2), 388; https://doi.org/10.3390/agronomy13020388 - 28 Jan 2023

Cited by 7 | Viewed by 1783

Abstract

Aiming at the need to prevent agricultural machinery from colliding with obstacles in the operation of unmanned agricultural machinery, an obstacle detection algorithm using 2D lidar was proposed, and a pre-collision system was designed using this algorithm, which was tested on a harvester. [...] Read more.

Aiming at the need to prevent agricultural machinery from colliding with obstacles in the operation of unmanned agricultural machinery, an obstacle detection algorithm using 2D lidar was proposed, and a pre-collision system was designed using this algorithm, which was tested on a harvester. The method uses the differences between lidar data frames to calculate the collision times between the farm machinery and the obstacles. The algorithm consists of the following steps: pre-processing to determine the region of interest, median filtering, and DBSCAN (density-based spatial clustering of applications with noise) to identify the obstacle and calculate of the collision time according to the 6σ principle. Based on this algorithm, a pre-collision system was developed and integrated with agricultural machinery navigation software. The harvester was refitted electronically, and the system was tested on a harvester. The results showed that the system had an average accuracy rate of 96.67% and an average recall rate of 97.14% for being able to stop safely for obstacles in the area of interest, with a summed average of 97% for both the accuracy and recall rates. The system can be used for an emergency stop when encountering obstacles in the automatic driving of agricultural machinery and provides a basis for the unmanned driving of agricultural machinery in more complex scenarios. Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

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20 pages, 10076 KiB

Open AccessArticle

Factors Affecting Droplet Loss behind Canopies with Air-Assisted Sprayers Used for Fruit Trees

by Shijie Jiang, Wenwei Li, Shenghui Yang, Yongjun Zheng, Yu Tan and Jiawei Xu

Agronomy 2023, 13(2), 375; https://doi.org/10.3390/agronomy13020375 - 27 Jan 2023

Cited by 2 | Viewed by 1455

Abstract

Air-assisted sprayers are widely employed in orchards, but inappropriate spray parameters can lead to large droplet losses, pesticide waste, and environmental pollution. To investigate the factors affecting the droplet loss of an air-assisted sprayer behind canopies, a two-factor, five-level full experiment was conducted [...] Read more.

Air-assisted sprayers are widely employed in orchards, but inappropriate spray parameters can lead to large droplet losses, pesticide waste, and environmental pollution. To investigate the factors affecting the droplet loss of an air-assisted sprayer behind canopies, a two-factor, five-level full experiment was conducted in an actual orchard, where the two factors were the power gradient and foliage area volume density (FAVD). In addition, the location of the sampling point was also considered in the data analysis, including horizontal distance, forward distance, and height. The results show that all factors significantly affected droplet coverage (p-value < 0.01). The droplet coverage showed an increase and then a decrease with an increasing power gradient, and the maximum coverage was measured at power gradient P3 (forward speed: 0.49 m/s, spray pressure: 0.30 MPa, and spray flow rate: 7.13 L/min) or P4 (forward speed: 0.58 m/s, spray pressure: 0.35 MPa, and spray flow rate: 8.44 L/min). The effect of FAVD on droplet coverage had obvious regularity, and this regularity did not change with the power gradient. At different positions behind canopies, the droplet coverage had great differences. The droplet coverage gradually decreases with increasing horizontal distance and height, while increasing with forward distance. This study provides a reference for the air-assisted sprayers to reduce droplet loss, and data support for subsequent research on precision spraying based on FAVD. Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

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22 pages, 8292 KiB

Open AccessArticle

Research on the Slip Rate Control of a Power Shift Tractor Based on Wheel Speed and Tillage Depth Adjustment

by Changhai Luo, Changkai Wen, Zhijun Meng, Huaiyu Liu, Guoqiang Li, Weiqiang Fu and Chunjiang Zhao

Agronomy 2023, 13(2), 281; https://doi.org/10.3390/agronomy13020281 - 17 Jan 2023

Cited by 6 | Viewed by 1960

Abstract

The existing control methods for the slip rate of the driving wheel of a test prototype have limitations that cause low-quality tillage and finishing operations. We propose a slip rate control method based on the dual factor adjustment of wheel speed and tillage [...] Read more.

The existing control methods for the slip rate of the driving wheel of a test prototype have limitations that cause low-quality tillage and finishing operations. We propose a slip rate control method based on the dual factor adjustment of wheel speed and tillage depth, taking the power shift tractor New Holland T1404 as an example to verify the algorithm. This method employs the wheel speed control principle based on the power transmission ratio calculation, throttle adjustment, and wheel speed control methods, as well as the slip rate control method, with wheel speed–slip rate control as the main factor and tillage depth–slip rate control as the secondary factor. A tractor test prototype was built to validate the method. The wheel speed control method enabled the tractor to accurately control the wheel speed under three working conditions: no load on a cemented ground, no load in a field, and subsoiling operation. For the subsoiling operation, the slip rate control method gradually reduced the tractor wheel speed when the slip rate of the tractor’s drive wheel was too high until it met the requirements. When the wheel speed was adjusted to the lower limit, suspension control was performed to reduce the tillage depth and improve vehicle trafficability. In the 130 s validation test, it took 14.1 s for the tractor with the slip rate control function to have a wheel slip rate exceeding 20%, which was 25.4% lower than that of the tractor without this function. The proposed method controls the slip rate within the optimal range while ensuring maximum operation quality (tillage depth). Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

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17 pages, 7343 KiB

Open AccessFeature PaperArticle

Dual-Manipulator Optimal Design for Apple Robotic Harvesting

by Zicong Xiong, Qingchun Feng, Tao Li, Feng Xie, Cheng Liu, Le Liu, Xin Guo and Chunjiang Zhao

Agronomy 2022, 12(12), 3128; https://doi.org/10.3390/agronomy12123128 - 9 Dec 2022

Cited by 14 | Viewed by 2859

Abstract

In order to ensure canopy area coverage with the most compact mechanical configuration possible, this paper proposes a configuration optimization design method of dual-manipulator to meet the research and development needs of an apple-efficient harvesting robot using the typical tree shape of a [...] Read more.

In order to ensure canopy area coverage with the most compact mechanical configuration possible, this paper proposes a configuration optimization design method of dual-manipulator to meet the research and development needs of an apple-efficient harvesting robot using the typical tree shape of a “high spindle” in China as the object. A Cartesian coordinate dual-manipulator with two groups of vertically synchronous operations and a three-degree range of motion based on the features of the spatial distribution of fruits under a typical canopy of dwarf and close planting was designed. Two-stage telescoping components that can be driven by both gas and electricity are employed to ensure the picking robotic arm’s quick response and accessibility to the tree crown. Based on the quantitative description of the working space and configuration parameters of the dual-manipulator, a multi-objective optimization model of the major configuration parameters is constructed. A comprehensive evaluation method of the dual-manipulator configuration based on the CRITIC–TOPSIS combined method is proposed. The optimal solutions of the lengths and elevations of upper and lower telescopic parts of the dual-manipulator and the distance from the mounting base of the outer frame of the dual-manipulator to the center of the tree trunk are determined, which are 1119.3 mm and 39.4°, 898.7 mm and 26°, 755.3 mm, respectively. The interaction between the configuration parameters of the dual-manipulator and its working area is then simulated and examined in order to verify the rationality of the optimum configuration settings. The results show that the optimal configuration of the dual-manipulator can fully cover the target working space, and the redundancy rate is 16.62%. The results of this study can be utilized to advance robotic fruit-picking research and development. Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

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Review

Jump to: Editorial, Research

49 pages, 22203 KiB

Open AccessEditor’s ChoiceReview

A Comprehensive Review of the Research of the “Eye–Brain–Hand” Harvesting System in Smart Agriculture

by Wanteng Ji, Xianhao Huang, Shubo Wang and Xiongkui He

Agronomy 2023, 13(9), 2237; https://doi.org/10.3390/agronomy13092237 - 26 Aug 2023

Cited by 5 | Viewed by 3524

Abstract

Smart agricultural harvesting robots’ vision recognition, control decision, and mechanical hand modules all resemble the human eye, brain, and hand, respectively. To enable automatic and precise picking of target fruits and vegetables, the system makes use of cutting-edge sensor technology, machine vision algorithms, [...] Read more.

Smart agricultural harvesting robots’ vision recognition, control decision, and mechanical hand modules all resemble the human eye, brain, and hand, respectively. To enable automatic and precise picking of target fruits and vegetables, the system makes use of cutting-edge sensor technology, machine vision algorithms, and intelligent control and decision methods. This paper provides a comprehensive review of international research advancements in the “eye–brain–hand” harvesting systems within the context of smart agriculture, encompassing aspects of mechanical hand devices, visual recognition systems, and intelligent decision systems. Then, the key technologies used in the current research are reviewed, including image processing, object detection and tracking, machine learning, deep learning, etc. In addition, this paper explores the application of the system to different crops and environmental conditions and analyzes its advantages and challenges. Finally, the challenges and prospects for the research on picking robots in the future are presented, including further optimization of the algorithm and improvement of flexibility and reliability of mechanical devices. To sum up, the “eye–brain–hand” picking system in intelligent agriculture has great potential to improve the efficiency and quality of crop picking and reduce labor pressure, and it is expected to be widely used in agricultural production. Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

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32 pages, 7008 KiB

Open AccessEditor’s ChoiceReview

Fruit Detection and Recognition Based on Deep Learning for Automatic Harvesting: An Overview and Review

by Feng Xiao, Haibin Wang, Yueqin Xu and Ruiqing Zhang

Agronomy 2023, 13(6), 1625; https://doi.org/10.3390/agronomy13061625 - 16 Jun 2023

Cited by 45 | Viewed by 15466

Abstract

Continuing progress in machine learning (ML) has led to significant advancements in agricultural tasks. Due to its strong ability to extract high-dimensional features from fruit images, deep learning (DL) is widely used in fruit detection and automatic harvesting. Convolutional neural networks (CNN) in [...] Read more.

Continuing progress in machine learning (ML) has led to significant advancements in agricultural tasks. Due to its strong ability to extract high-dimensional features from fruit images, deep learning (DL) is widely used in fruit detection and automatic harvesting. Convolutional neural networks (CNN) in particular have demonstrated the ability to attain accuracy and speed levels comparable to those of humans in some fruit detection and automatic harvesting fields. This paper presents a comprehensive overview and review of fruit detection and recognition based on DL for automatic harvesting from 2018 up to now. We focus on the current challenges affecting fruit detection performance for automatic harvesting: the scarcity of high-quality fruit datasets, fruit detection of small targets, fruit detection in occluded and dense scenarios, fruit detection of multiple scales and multiple species, and lightweight fruit detection models. In response to these challenges, we propose feasible solutions and prospective future development trends. Future research should prioritize addressing these current challenges and improving the accuracy, speed, robustness, and generalization of fruit vision detection systems, while reducing the overall complexity and cost. This paper hopes to provide a reference for follow-up research in the field of fruit detection and recognition based on DL for automatic harvesting. Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

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29 pages, 5782 KiB

Open AccessReview

Object Detection and Recognition Techniques Based on Digital Image Processing and Traditional Machine Learning for Fruit and Vegetable Harvesting Robots: An Overview and Review

by Feng Xiao, Haibin Wang, Yaoxiang Li, Ying Cao, Xiaomeng Lv and Guangfei Xu

Agronomy 2023, 13(3), 639; https://doi.org/10.3390/agronomy13030639 - 23 Feb 2023

Cited by 19 | Viewed by 6468

Abstract

The accuracy, speed, and robustness of object detection and recognition are directly related to the harvesting efficiency, quality, and speed of fruit and vegetable harvesting robots. In order to explore the development status of object detection and recognition techniques for fruit and vegetable [...] Read more.

The accuracy, speed, and robustness of object detection and recognition are directly related to the harvesting efficiency, quality, and speed of fruit and vegetable harvesting robots. In order to explore the development status of object detection and recognition techniques for fruit and vegetable harvesting robots based on digital image processing and traditional machine learning, this article summarizes and analyzes some representative methods. This article also demonstrates the current challenges and future potential developments. This work aims to provide a reference for future research on object detection and recognition techniques for fruit and vegetable harvesting robots based on digital image processing and traditional machine learning. Full article

(This article belongs to the Special Issue Agricultural Unmanned Systems: Empowering Agriculture with Automation)

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