Research

26 pages, 1654 KiB

Open AccessArticle

Optimal Coverage Path Planning for Agricultural Vehicles with Curvature Constraints

by Maria Höffmann, Shruti Patel and Christof Büskens

Agriculture 2023, 13(11), 2112; https://doi.org/10.3390/agriculture13112112 - 07 Nov 2023

Cited by 2 | Viewed by 1247

Complete coverage path planning (CCPP) is vital in mobile robot applications. Optimizing CCPP is particularly significant in precision agriculture, where it enhances resource utilization, reduces soil compaction, and boosts crop yields. This work offers a comprehensive approach to CCPP for agricultural vehicles with [...] Read more.

Complete coverage path planning (CCPP) is vital in mobile robot applications. Optimizing CCPP is particularly significant in precision agriculture, where it enhances resource utilization, reduces soil compaction, and boosts crop yields. This work offers a comprehensive approach to CCPP for agricultural vehicles with curvature constraints. Our methodology comprises four key stages. First, it decomposes complex agricultural areas into simpler cells, each equipped with guidance tracks, forming a fixed track system. The subsequent route planning and smooth path planning stages compute a path that adheres to path constraints, optimally traverses the cells, and aligns with the track system. We use the generalized traveling salesman problem (GTSP) to determine the optimal traversing sequence. Additionally, we introduce an algorithm for calculating paths that are both smooth and curvature-constrained within individual cells, as well as paths that enable seamless transitions between cells, resulting in a smooth, curvature-constraint coverage path. Our modular approach allows method flexibility at each step. We evaluate our method on real agricultural fields, demonstrating its effectiveness in minimizing path length, ensuring efficient coverage, and adhering to curvature constraints. This work establishes a strong foundation for precise and efficient agricultural coverage path planning, with potential for further real-world applications and enhancements. Full article

(This article belongs to the Special Issue Agricultural Automation in Smart Farming)

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21 pages, 8439 KiB

Open AccessArticle

A New Remote Sensing Service Mode for Agricultural Production and Management Based on Satellite–Air–Ground Spatiotemporal Monitoring

by Wenjie Li, Wen Dong, Xin Zhang and Jinzhong Zhang

Agriculture 2023, 13(11), 2063; https://doi.org/10.3390/agriculture13112063 - 27 Oct 2023

Viewed by 1231

Abstract

Remote sensing, the Internet, the Internet of Things (IoT), artificial intelligence, and other technologies have become the core elements of modern agriculture and smart farming. Agricultural production and management modes guided by data and services have become a cutting-edge carrier of agricultural information [...] Read more.

Remote sensing, the Internet, the Internet of Things (IoT), artificial intelligence, and other technologies have become the core elements of modern agriculture and smart farming. Agricultural production and management modes guided by data and services have become a cutting-edge carrier of agricultural information monitoring, which promotes the transformation of the intelligent computing of remote sensing big data and agricultural intensive management from theory to practical applications. In this paper, the main research objective is to construct a new high-frequency agricultural production monitoring and intensive sharing service and management mode, based on the three dimensions of space, time, and attributes, that includes crop recognition, growth monitoring, yield estimation, crop disease or pest monitoring, variable-rate prescription, agricultural machinery operation, and other automatic agricultural intelligent computing applications. The platforms supported by this mode include a data management and agricultural information production subsystem, an agricultural monitoring and macro-management subsystem (province and county scales), and two mobile terminal applications (APPs). Taking Shandong as the study area of the application case, the technical framework of the system and its mobile terminals were systematically elaborated at the province and county levels, which represented macro-management and precise control of agricultural production, respectively. The automatic intelligent computing mode of satellite–air–ground spatiotemporal collaboration that we proposed fully couples data obtained from satellites, unmanned aerial vehicles (UAVs), and IoT technologies, which can provide the accurate and timely monitoring of agricultural conditions and real-time guidance for agricultural machinery scheduling throughout the entire process of agricultural cultivation, planting, management, and harvest; the area accuracy of all obtained agricultural information products is above 90%. This paper demonstrates the necessity of customizable product and service research in agricultural intelligent computing, and the proposed practical mode can provide support for governments to participate in agricultural macro-management and decision making, which is of great significance for smart farming development and food security. Full article

(This article belongs to the Special Issue Agricultural Automation in Smart Farming)

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14 pages, 3720 KiB

Open AccessArticle

Research on Hand–Eye Calibration Accuracy Improvement Method Based on Iterative Closest Point Algorithm

by Tingwu Yan, Peijuan Li, Yiting Liu, Tong Jia, Hanqi Yu and Guangming Chen

Agriculture 2023, 13(10), 2026; https://doi.org/10.3390/agriculture13102026 - 19 Oct 2023

Viewed by 973

Abstract

In the functioning of the hand–eye collaboration of an apple picking robot, the accuracy of the hand–eye relationship is a key factor affecting the efficiency and accuracy of the robot’s operation. In order to enhance the low accuracy of traditional hand–eye calibration methods, [...] Read more.

In the functioning of the hand–eye collaboration of an apple picking robot, the accuracy of the hand–eye relationship is a key factor affecting the efficiency and accuracy of the robot’s operation. In order to enhance the low accuracy of traditional hand–eye calibration methods, linear and nonlinear solving methods based on mathematical tools such as quaternions are commonly adopted. To solve the loss of accuracy in decoupling during the linearization solution and to reduce the cumulative error that occurs during nonlinear solutions, a hand–eye calibration method, based on the ICP algorithm, is proposed in this paper. The method initializes the ICP matching algorithm with a solution derived from Tsai–Lenz, and substitutes it for iterative computation, thereby ascertaining a precise hand–eye conversion relationship by optimizing the error threshold and iteration count in the ICP matching process. Experimental results demonstrate that the ICP-based hand–eye calibration optimization algorithm not only circumvents the issues pertaining to accuracy loss and significant errors during solving, but also enhances the rotation accuracy by 13.6% and the translation accuracy by 2.47% compared with the work presented by Tsai–Lenz. Full article

(This article belongs to the Special Issue Agricultural Automation in Smart Farming)

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17 pages, 4022 KiB

Open AccessArticle

Research on the Agricultural Pest Identification Mechanism Based on an Intelligent Algorithm

by Qixun Xiao, Wenying Zheng, Yifan He, Zijie Chen, Fanxin Meng and Liyan Wu

Agriculture 2023, 13(10), 1878; https://doi.org/10.3390/agriculture13101878 - 26 Sep 2023

Viewed by 1137

Abstract

The use of Internet of Things (IoT) technology for real-time monitoring of agricultural pests is an unavoidable trend in the future of intelligent agriculture. This paper aims to address the difficulties in deploying models at the edge of the pest monitoring visual system [...] Read more.

The use of Internet of Things (IoT) technology for real-time monitoring of agricultural pests is an unavoidable trend in the future of intelligent agriculture. This paper aims to address the difficulties in deploying models at the edge of the pest monitoring visual system and the low recognition accuracy. In order to achieve that, a lightweight GCSS-YOLOv5s algorithm is proposed. Firstly, we introduce the lightweight network GhostNet, use the Ghostconv module to replace the traditional convolution, and construct the C3Ghost module based on the CSP structure, drastically reducing the number of model parameters. Secondly, during the feature fusion process, we introduce the content-aware reassembly of features (CARAFE) lightweight up-sampling operator to enhance the feature integration capability of the pests by reducing the impact of redundant features after fusion. Then, we adopt SIoU as the bounding box regression loss function, which enhances the convergence speed and detection accuracy of the model. Finally, the traditional non-maximum suppression (NMS) was improved to Soft-NMS to improve the model’s ability to recognize overlapping pests. According to the experimental results, the mean average precision (mAP) of the GCSS-YOLOv5s model reaches 90.5%. This is achieved with a 44% reduction in the number of parameters and a 7.4 G reduction in computation volume compared to the original model. The method significantly reduces the model’s resource requirements while maintaining accuracy, which offers a specific theoretical foundation and technological reference for the future field of intelligent monitoring. Full article

(This article belongs to the Special Issue Agricultural Automation in Smart Farming)

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18 pages, 5639 KiB

Open AccessArticle

Study on Vibration Characteristics of Paddy Power Chassis under Different Driving Conditions

by Dongyang Yu, Jianfei He, Feihu Peng, Cheng Qian, Ying Zang, Minghua Zhang, Wenwu Yang, Guoxiang Zeng, Jianpeng Chen, Wei Qin and Zaiman Wang

Agriculture 2023, 13(9), 1842; https://doi.org/10.3390/agriculture13091842 - 20 Sep 2023

Cited by 1 | Viewed by 776

Abstract

To elucidate the vibrational characteristics of power chassis in paddy fields, we examined the Yanmar VPG6G rice transplanter across diverse terrains, including paddy fields, dry land, and concrete roads. Vibrational acceleration measurements, taken in longitudinal, transverse, and vertical orientations at key chassis locations, [...] Read more.

To elucidate the vibrational characteristics of power chassis in paddy fields, we examined the Yanmar VPG6G rice transplanter across diverse terrains, including paddy fields, dry land, and concrete roads. Vibrational acceleration measurements, taken in longitudinal, transverse, and vertical orientations at key chassis locations, revealed noteworthy findings. The Mizuta power chassis exhibited its lowest root-mean-square (RMS) vibrational acceleration on concrete, while the highest was observed on paddy fields. The acceleration power spectra predominantly peaked between 1~14 Hz, with peak values amplifying as speed increased. Additionally, pendant orientation frequencies exceeded those of longitudinal and lateral directions. Both front and rear wheels mirrored the vibrational accelerations of the rear axle, but dynamic load coefficients for the front wheels consistently surpassed the rear, particularly at elevated speeds. This research not only enhances our understanding of terrain-induced vibrations and the intricate dynamics between terrain and tires but also lays the groundwork for designing optimized vibration-damping solutions tailored to prevalent road conditions. Full article

(This article belongs to the Special Issue Agricultural Automation in Smart Farming)

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19 pages, 11030 KiB

Open AccessArticle

Designing, Optimizing, and Validating a Low-Cost, Multi-Purpose, Automatic System-Based RGB Color Sensor for Sorting Fruits

by Abdallah E. Elwakeel, Yasser S. A. Mazrou, Aml A. Tantawy, Abdelaziz M. Okasha, Adel H. Elmetwalli, Salah Elsayed and Abeer H. Makhlouf

Agriculture 2023, 13(9), 1824; https://doi.org/10.3390/agriculture13091824 - 18 Sep 2023

Cited by 3 | Viewed by 2054

Abstract

The use of automatic systems in the agriculture sector enhances product quality and the country’s economy. The method used to sort fruits and vegetables has a remarkable impact on the export market and quality assessment. Although manual sorting and grading can be performed [...] Read more.

The use of automatic systems in the agriculture sector enhances product quality and the country’s economy. The method used to sort fruits and vegetables has a remarkable impact on the export market and quality assessment. Although manual sorting and grading can be performed easily, it is inconsistent, time-consuming, expensive, and highly influenced by the surrounding environment. In this regard, this study aimed to design and optimize the performance of a low-cost, multi-purpose, automatic RGB color-based sensor for sorting fruits. The proposed automatic color sorting system consists of hardware components including a machine frame, belt and pulleys, conveyor belt, scanning zone, plastic boxes, electric components (stepper motors, RGB color sensors, Arduino Mega, motor drivers), and software components (Arduino IDE version 2.2.1 and C++). Calibration was performed for the light intensity sensor to measure the light intensity inside the scanning zone, the conveyor speed sensor, and the RGB color sensors by testing the RGB color channels. The sensor, the height, conveyor belt color, and light intensity should be carefully adjusted to ensure a high performance of the color-based sorting system. The results showed that the appropriate sensor height ranged from 15 to 30 mm, the optimum color of the conveyor belt was black, and scanning the objects at a light intensity of 25 lux achieved the best output signals. The RGB color sensors achieved an analytical performance similar to that obtained with manual sorting without requiring the use of computers for image processing like other automatic sorting systems do in order to gather RGB data. Full article

(This article belongs to the Special Issue Agricultural Automation in Smart Farming)

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16 pages, 25510 KiB

Open AccessArticle

Quantification of Biophysical Parameters and Economic Yield in Cotton and Rice Using Drone Technology

by Sellaperumal Pazhanivelan, Ramalingam Kumaraperumal, P. Shanmugapriya, N. S. Sudarmanian, A. P. Sivamurugan and S. Satheesh

Agriculture 2023, 13(9), 1668; https://doi.org/10.3390/agriculture13091668 - 24 Aug 2023

Cited by 2 | Viewed by 1150

Abstract

New agronomic opportunities for more informed agricultural decisions and enhanced crop management have been made possible by drone-based near-ground remote sensing. Obtaining precise non-destructive information regarding crop biophysical characteristics at spatial and temporal scales is now possible. Drone-mounted multispectral and thermal sensors were [...] Read more.

New agronomic opportunities for more informed agricultural decisions and enhanced crop management have been made possible by drone-based near-ground remote sensing. Obtaining precise non-destructive information regarding crop biophysical characteristics at spatial and temporal scales is now possible. Drone-mounted multispectral and thermal sensors were used to assess crop phenology, condition, and stress by profiling spectral vegetation indices in crop fields. In this study, vegetation indices, viz., Atmospherically Resistant Vegetation Index (ARVI), Modified Chlorophyll Absorption Ratio Index (MCARI), Wide Dynamic Range Vegetation Index (WDRVI), Normalized Red–Green Difference Index (NGRDI), Excess Green Index (ExG), Red–Green Blue Vegetation Index (RGBVI), and Visible Atmospherically Resistant Index (VARI) were generated. Furthermore, Pearson correlation analysis showed a better correlation between WDRVI and VARI with LAI (R = 0.955 and R = 0.982) ground truth data. In contrast, a strong correlation (R = 0.931 and R = 0.844) was recorded with MCARI and NGRDI with SPAD chlorophyll ground truth data. Then, the best-performing indices, WDRVI and MCARI in cotton, and VARI and NGRDI in rice, were further used to generate the yield model. This study for determining LAI and chlorophyll shows that high spatial resolution drone imageries are accurate and fast. As a result, finding out the LAI and chlorophyll and how they affect crop yield at a regional scale is helpful. The widespread use of unmanned aerial vehicles (UAV) and yield prediction were technical components of large-scale precision agriculture. Full article

(This article belongs to the Special Issue Agricultural Automation in Smart Farming)

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18 pages, 6455 KiB

Open AccessArticle

Development of a Control System for Double-Pendulum Active Spray Boom Suspension Based on PSO and Fuzzy PID

by Fang Li, Xiaohu Bai, Zhanxiang Su, Shoushan Tang, Zexu Wang, Feng Li and Hualong Yu

Agriculture 2023, 13(9), 1660; https://doi.org/10.3390/agriculture13091660 - 23 Aug 2023

Viewed by 826

Abstract

During the operation of boom sprayers in the field, it is crucial to ensure that the entire boom is maintained at an optimal height relative to the ground or crop canopy. Active suspension is usually used to adjust the height. A control system [...] Read more.

During the operation of boom sprayers in the field, it is crucial to ensure that the entire boom is maintained at an optimal height relative to the ground or crop canopy. Active suspension is usually used to adjust the height. A control system for double-pendulum active suspension was developed in this paper. The control system consisted of a main control node, two distance measurement nodes, a vehicle inclination detection node, and an execution node. Communication between nodes was carried out using a CAN bus. The hardware was selected, and the interface circuits of the sensors and the actuator were designed. The transfer functions of the active suspension and electric linear actuator were established. In order to enhance the efficiency of the control system, the particle swarm optimization (PSO) algorithm was employed to optimize the initial parameters of the fuzzy PID controller. The simulation results demonstrated that the PSO-based fuzzy PID controller exhibited improvements in terms of reduced overshoot and decreased settling time when compared to conventional PID and fuzzy PID controllers. The experimental results showed that the active suspension system equipped with the control system could effectively isolate high-frequency disturbances and follow low-frequency ground undulations, meeting the operational requirements. Full article

(This article belongs to the Special Issue Agricultural Automation in Smart Farming)

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21 pages, 5836 KiB

Open AccessArticle

Design and Experimental Study of Ball-Head Cone-Tail Injection Mixer Based on Computational Fluid Dynamics

by Yixin Shi, Siliang Xiang, Minzi Xu, Defan Huang, Jianfei Liu, Xiaocong Zhang and Ping Jiang

Agriculture 2023, 13(7), 1377; https://doi.org/10.3390/agriculture13071377 - 11 Jul 2023

Cited by 1 | Viewed by 786

Abstract

The uniform and accurate mixing of pesticides in water is a necessary prerequisite for plant protection, especially for enabling precise variable spraying, and is also an important method to achieve a precise reduction in pesticide spraying. In order to ensure the uniform mixing [...] Read more.

The uniform and accurate mixing of pesticides in water is a necessary prerequisite for plant protection, especially for enabling precise variable spraying, and is also an important method to achieve a precise reduction in pesticide spraying. In order to ensure the uniform mixing of pesticides and water and solve the problems of traditional injection mixers, such as the limited range in the mixing ratio and unadjustable proportion, an active injection liquid mixer is designed in this paper. The mixer can be matched with an online mixing and spraying device to achieve accuracy in mixing and spraying. In this paper, a computational fluid dynamics (CFD) method is used to optimize the structure of the mixer. Through comparative analysis, the optimal structure of the mixer was found. It has a spherical head and conical tail, the number of guide plates is seven, and the shape is semicircular. By calculating the volume fraction of pesticide distribution under different cross-sections, the coefficient of variation in the process of mixing is obtained. The analysis shows that the maximum coefficient of variation of the ball-head cone-tail active injection mixer was 2.88% (lower than the allowable 5%) with a mixing ratio ranging from 300:1 to 3000:1. At the same time, image analysis methods of high-definition photography and ultraviolet spectrophotometry were used to analyze the mixing effect of the mixer. The test results show that, when the pressure of the pesticide injection is 1 MPa, the distribution of the pesticide and water in the ball-head cone-tail injection mixer is more uniform under different mixing ratios, and it has a better spatio-temporal distribution uniformity with the concentration changing a little at different times and different spatial locations. The mixer can provide a theoretical reference and technical support for the subsequent realization of an accurate online variable spray. Full article

(This article belongs to the Special Issue Agricultural Automation in Smart Farming)

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18 pages, 10627 KiB

Open AccessArticle

Energy Management of Sowing Unit for Extended-Range Electric Tractor Based on Improved CD-CS Fuzzy Rules

by Zhengkai Wu, Jiazhong Wang, Yazhou Xing, Shanshan Li, Jinggang Yi and Chunming Zhao

Agriculture 2023, 13(7), 1303; https://doi.org/10.3390/agriculture13071303 - 26 Jun 2023

Cited by 1 | Viewed by 871

Abstract

In order to ensure the continuity and endurance mileage requirements during sowing operations, it is necessary to establish accurate modeling for the working condition of the electric tractor sowing unit by adopting a reasonable energy management strategy and realizing accurate energy prediction. The [...] Read more.

In order to ensure the continuity and endurance mileage requirements during sowing operations, it is necessary to establish accurate modeling for the working condition of the electric tractor sowing unit by adopting a reasonable energy management strategy and realizing accurate energy prediction. The existing electric tractor sowing unit battery energy management strategy is not optimal since it is mostly based on extensive rules. In this paper, according to the requirements of the sowing conditions, a precise model of electric energy consumption in the sowing cycle was established and an energy management strategy of sowing unit of extended-range electric tractor with power CD-CS was proposed. Fuzzy control rules of the dynamic SOC correction factor were established in the battery maintenance stage, and the NSGA-II algorithm was used to optimize the fuzzy control rules to optimize the battery charging and discharging efficiency. A hardware-in-the-loop simulation test platform was built, and the proposed CD-CS strategy was compared with the fuzzy improvement strategy. The simulation results show that the proposed fuzzy improvement strategy extended the battery life of the power consumption stage by 2131.9 s, which is a significant improvement. The field practical results showed that the SOC decreased by 7.21% and the simulation by 4.94% in terms of power consumption in a cycle. The power consumption variance was within a reasonable range, which further verifies the feasibility of the strategy. Full article

(This article belongs to the Special Issue Agricultural Automation in Smart Farming)

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18 pages, 5928 KiB

Open AccessArticle

Beyond Trade-Off: An Optimized Binocular Stereo Vision Based Depth Estimation Algorithm for Designing Harvesting Robot in Orchards

by Li Zhang, Qun Hao, Yefei Mao, Jianbin Su and Jie Cao

Agriculture 2023, 13(6), 1117; https://doi.org/10.3390/agriculture13061117 - 25 May 2023

Viewed by 1367

Abstract

Depth estimation is one of the bottleneck parts for harvesting robots to determine whether the operation of grasping or picking succeeds or not directly. This paper proposed a novel disparity completion method combined with bilateral filtering and pyramid fusion to improve the issues [...] Read more.

Depth estimation is one of the bottleneck parts for harvesting robots to determine whether the operation of grasping or picking succeeds or not directly. This paper proposed a novel disparity completion method combined with bilateral filtering and pyramid fusion to improve the issues of incorrect outputs due to the missed or wrong matching when achieving 3D position from 2D images in open-world environments. Briefly, our proposed method has two significant advantages in general. Firstly, occlusion between leaves, branches, and fruits is a universal phenomenon in unstructured orchard environments, which results in the most depth estimation algorithms facing great challenges to obtain accurate outputs in these occluded regions. To alleviate these issues, unlike other research efforts that already exist, we optimized the semi-global matching algorithm to obtain high accuracy sparse values as an initial disparity map; then, an improved bilateral filtering algorithm is proposed to eliminate holes and discontinuous regions caused by occlusion to obtain precise and density disparity outputs. Secondly, due to taking the practical high-efficiency requirements of the automated harvesting robot in its working status into consideration, we attempted to merge multiple low-resolution bilateral filtering results through the pyramid fusion model which goes beyond the trade-off mechanism to improve the performance of both accuracy and time cost. Finally, a prototype harvesting robot was designed to conduct experiments at three kinds of different distances (0.6~0.75 m, 1~1.2 m, and 1.6~1.9 m). Experiment results showed that our proposed method achieved density disparity maps and eliminated holes and discontinuous defects in the disparity map effectively. The average absolute error of our proposed method is 3.2 mm, and the average relative error is 1.79%. In addition, the time cost is greatly reduced more than 90%. Comprehensive experimental results demonstrate that our proposed algorithm provides a potential possibility for designing harvesting. Full article

(This article belongs to the Special Issue Agricultural Automation in Smart Farming)

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