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A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: closed (25 March 2024) | Viewed by 13812

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Prof. Dr. Dongyan Huang

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Guest Editor

College of Biological and Agricultural Engineering, Jilin University, Changchun 130025, China
Interests: agricultural machinery; sensors; automation; intelligence; conservation tillage; tillage technology; seeding technology

Dr. Gang Wang

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Guest Editor

College of Biological and Agricultural Engineering, Jilin University, 5988 Renmin Street, Changchun 130025, China
Interests: agricultural machinery; conservation tillage; sensors; automation; intelligence; plant protection
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Dear Colleagues,

Agricultural machinery is an important part of agricultural modernization. Agricultural machinery automation and intelligence is a comprehensive application of agricultural mechanization technology, computer technology, sensor technology and control technology. It can accurately and intelligently control and manage agricultural production, thereby improving crop quality and agricultural production efficiency, reducing costs and environmental damage. Automatic and intelligent agricultural machinery has a wide range of applications in field planting, including tillage, sowing, fertilization, irrigation, plant protection, harvesting and so on. It also includes the collection and processing of agricultural production information so as to provide decisions and prescriptions for agricultural production.

This Special Issue aims to deepen our understanding of the modernization of agricultural machinery, focusing on the application of automation and intelligent technologies in agricultural machinery, and stimulate further technological developments for sustainability and intelligence of agricultural production. For these reasons, it welcomes highly interdisciplinary quality studies from disparate research fields. Topics of interest include but are not limited to:

the design and performance evaluation of agricultural machinery;
mechatronics and control system for agricultural machinery;
AI-based decision support systems for agricultural production;
sensor technology for agricultural machinery and agricultural production information collection.

Prof. Dr. Dongyan Huang
Dr. Gang Wang
Guest Editors

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Keywords

agricultural machinery
sensors
automation
intelligence
conservation tillage
tillage technology
seeding technology

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Published Papers (10 papers)

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Research

17 pages, 4523 KiB

Open AccessArticle

Optimization of Operation Parameters and Performance Prediction of Paddy Field Grader Based on a GA-BP Neural Network

by Min Liu, Xuejie Ma, Weizhi Feng, Haiyang Jing, Qian Shi, Yang Wang, Dongyan Huang and Jingli Wang

Agriculture 2024, 14(8), 1283; https://doi.org/10.3390/agriculture14081283 - 3 Aug 2024

Viewed by 679

Abstract

Paddy field leveling is an essential step before rice transplanting. During the operation of a paddy field grader, a common issue is the wrapping of rice straw around the blades, resulting in a low rice straw burial rate. This study focused on analyzing the operating parameters of a disc spring–tooth-combined paddy field grader. A soil–straw mechanism simulation model was created using EDEM 2021 software to simulate the field operation status. Firstly, the single-factor test was carried out, with the working speed, the working depth of the disc cutter roller, and the rotation speed of the cutter roller as the factors and the straw-buried rate (SBR) and the machine forward resistance (MFR) as the test indexes, and the parameter range was optimized. The parameters were optimized by the response surface method (RSM) and machine learning algorithms. The results indicated that the genetic algorithm–back propagation (GA-BP) neural network outperformed other optimization models in terms of prediction accuracy and stability. By utilizing the GA-BP regression model and RSM model for regression fitting, two sets of optimal parameter combinations were obtained. Verification experiments were carried out using two sets of parameter combinations. Taking the average of the experimental results, the simulation results showed that the straw burial rate was 93.47% and the forward resistance was 6487 N for the parameter combinations of RSM, and the straw burial rate was 94.86% and the forward resistance was 6352 N for the parameter combinations of GA-BP; the field experiments showed that the straw burial rate was 92.86% and the forward resistance was 6518 N for the parameter combinations of RSM, and the straw burial rate was 95.17% and the forward resistance was 6249 N for the parameter combinations of GA-BP. The results demonstrated that the GA-BP prediction model exhibited better predictive capabilities compared to the traditional RSM, providing more accurate predictions of the paddy field grader’s field operation performance. Full article

(This article belongs to the Special Issue Smart Mechanization and Automation in Agriculture)

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25 pages, 3873 KiB

Open AccessArticle

Sliding Mode Speed Control in Synchronous Motors for Agriculture Machinery: A Chattering Suppression Approach

by David Marcos-Andrade, Francisco Beltran-Carbajal, Ivan Rivas-Cambero, Hugo Yañez-Badillo, Antonio Favela-Contreras and Julio C. Rosas-Caro

Agriculture 2024, 14(5), 737; https://doi.org/10.3390/agriculture14050737 - 9 May 2024

Cited by 2 | Viewed by 996

Abstract

Synchronous motors have extended their presence in different applications, specifically in high-demand environments such as agronomy. These uses need advanced and better control strategies to improve energy efficiency. Within this context, sliding mode control has demonstrated effectiveness in electric machine control due to its advantages in robustness and quick adaptation to uncertain dynamic system disturbances. Nevertheless, this control technique presents the undesirable chattering phenomenon due to the discontinuous control action. This paper introduces a novel speed integral control scheme based on sliding modes for synchronous motors. This approach is designed to track smooth speed profiles and is evaluated through several numeric simulations to verify its robustness against variable torque loads. This approach addresses using electric motors for different applications such as irrigation systems, greenhouses, pumps, and others. Moreover, to address the chattering problem, different sign function approximations are evaluated in the control scheme. Then, the most effective functions for suppressing the chattering phenomenon through extensive comparative analysis are identified. Integral compensation in this technique demonstrates improvement in motor performance, while sign function approximations show a chattering reduction. Different study cases prove the robustness of this control scheme for large-scale synchronous motors. The simulation results validate the proposed control scheme based on sliding modes with integral compensation, by achieving chattering reduction and obtaining an efficient control scheme against uncertain disturbances in synchronous motors for agronomy applications. Full article

(This article belongs to the Special Issue Smart Mechanization and Automation in Agriculture)

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16 pages, 5596 KiB

Open AccessArticle

Optimizing Efficiency of Tea Harvester Leaf-Collection Pipeline: Numerical Simulation and Experimental Validation

by Zhe Du, Liyuan Zhang, Xinping Li, Xin Jin and Fan Yu

Agriculture 2024, 14(5), 653; https://doi.org/10.3390/agriculture14050653 - 23 Apr 2024

Viewed by 1005

Abstract

To address the challenges of missed and disorderly picking in tea harvesters, this study focused on the leaf-collection pipeline and utilized Fluent simulation 19.0 software. A single-factor test identified key parameters affecting airflow velocity. An orthogonal test evaluated the main pipe taper, number of branch pipes, and branch pipe outlet diameter, with average outlet wind speed and wind speed non-uniformity as indicators. The optimal parameters were a main pipe taper of 25.5 mm, 10 branch pipes, and an inner diameter of 17.10 mm for the outlet, resulting in 10.73 m/s average wind speed and 8.24% non-uniformity. Validation tests showed errors under 1%. Further optimization on the internal structure’s extension length led to 11.02 m/s average wind speed and 8.04% non-uniformity. Field experiments demonstrated a 3.40% stalk leakage rate and 90.36% bud leaf integrity rate; the optimized structure of the leaf-collecting pipeline significantly improved the uniformity of airflow and the picking efficiency. These findings offer valuable insights and practical benefits for enhancing the efficiency of tea harvesters. Full article

(This article belongs to the Special Issue Smart Mechanization and Automation in Agriculture)

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31 pages, 9837 KiB

Open AccessArticle

Design and Test of Disturbed Fertilizer Strip-Ejection Device with Vertical Pendulum Bar Based on Discrete Element Method

by Lintao Chen, Xiangwu Deng, Zhaoxiang Liu, Xiangwei Mou, Xu Ma and Rui Chen

Agriculture 2024, 14(4), 635; https://doi.org/10.3390/agriculture14040635 - 20 Apr 2024

Cited by 1 | Viewed by 1302

Abstract

Fertilizer can improve the yield of crops per unit area, and uniform fertilizer discharge can improve the fertilizer utilization rate. Therefore, it is meaningful to improve the performance of fertilizer-discharge devices in order to improve the modernization level of crop field fertilizer management. To address the problems of operational smoothness, stability and poor uniformity of fertilizer discharge, and other difficult problems encountered with strip fertilizer-discharge devices, this study designs a disturbed fertilizer strip-discharge device with a vertical pendulum. The main factors affecting the performance of fertilizer discharge were the wedge angle of the push-disturbing main pendulum bar (PMPB), the inclination angle of the aided-stirring pendulum pick (APP), the flow gap of the pendulum bar (FGPB), and the operation frequency of the swing-rod combination (SRC). The discrete element method (DEM) was used to establish a simulation model of the fertilizer device to explore the influence of the main factors on the performance of fertilizer discharge, with the coefficient of variation (CV) of fertilizer discharge uniformity and fertilizer discharge accuracy (FDA) used as the evaluation indices. The results show that the factors affecting the CV of fertilizer discharge uniformity and FDA were, in order of priority, the operation frequency of the SRC, the FGPB, the wedge angle of the PMPB, and the inclination angle of the APP. The optimal parameters after rounding were as follows: the wedge angle of the PMPB was 45°, the inclination angle of the APP was 46°, the operation frequency of the SRC was 188 times/min, and the FGPB was 4.5 mm. At this point, the model predicted that the CV of fertilizer discharge uniformity would be 10.53%, and that the FDA would be 3.19%. Using the optimal parameters for bench test verification, it was found that the wedge angle of the PMPB was 45°, the inclination angle of the APP was 46°, the operation frequency of the SRC was 188 times/min, the FGPB was 4.5 mm, the CV of the uniformity of the fertilizer discharge was 11.06%, and the FDA was 3.51%. In the test, the fertilizer-discharge device was stable and had good adaptability to different fertilizers. The results of this study can provide a theoretical reference for the development of precision strip-fertilizer application devices. Full article

(This article belongs to the Special Issue Smart Mechanization and Automation in Agriculture)

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20 pages, 4397 KiB

Open AccessArticle

Sh-DeepLabv3+: An Improved Semantic Segmentation Lightweight Network for Corn Straw Cover Form Plot Classification

by Yueyong Wang, Xuebing Gao, Yu Sun, Yuanyuan Liu, Libin Wang and Mengqi Liu

Agriculture 2024, 14(4), 628; https://doi.org/10.3390/agriculture14040628 - 18 Apr 2024

Cited by 2 | Viewed by 1173

Abstract

Straw return is one of the main methods for protecting black soil. Efficient and accurate straw return detection is important for the sustainability of conservation tillage. In this study, a rapid straw return detection method is proposed for large areas. An optimized Sh-DeepLabv3+ model based on the aforementioned detection method and the characteristics of straw return in Jilin Province was then used to classify plots into different straw return cover types. The model used Mobilenetv2 as the backbone network to reduce the number of model parameters, and the channel-wise feature pyramid module based on channel attention (CA-CFP) and a low-level feature fusion module (LLFF) were used to enhance the segmentation of the plot details. In addition, a composite loss function was used to solve the problem of class imbalance in the dataset. The results show that the extraction accuracy is optimal when a 2048 × 2048-pixel scale image is used as the model input. The total parameters of the improved model are 3.79 M, and the mean intersection over union (MIoU) is 96.22%, which is better than other comparative models. After conducting a calculation of the form–grade mapping relationship, the error value of the area prediction was found to be less than 8%. The results show that the proposed rapid straw return detection method based on Sh-DeepLabv3+ can provide greater support for straw return detection. Full article

(This article belongs to the Special Issue Smart Mechanization and Automation in Agriculture)

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23 pages, 5508 KiB

Open AccessArticle

A Combined Paddy Field Inter-Row Weeding Wheel Based on Display Dynamics Simulation Increasing Weed Mortality

by Jinwu Wang, Zhe Liu, Mao Yang, Wenqi Zhou, Han Tang, Long Qi, Qi Wang and Yi-Jia Wang

Agriculture 2024, 14(3), 444; https://doi.org/10.3390/agriculture14030444 - 8 Mar 2024

Cited by 1 | Viewed by 1219

Abstract

Weeds compete with rice for sunlight and nutrients and are prone to harboring pathogens, leading to reduced rice yields. Addressing the issues of low weeding efficiency and weed mortality rates in existing inter-row weeding devices, the study proposes the design of a combination paddy field inter-row weeding wheel. The device’s operation process is theoretically analyzed based on the weed control requirements in the northeastern region of China, leading to the determination of specific structural parameters. This research conducted experiments on the mechanical properties of weed cutting to obtain geometric parameters for paddy field weeds. It was found that the range for the cutting gap of the dynamic–fixed blade is between 0.6 mm to 1.4 mm and the cutting angle is between 5° to 15°, resulting in the lowest peak cutting force for weeds. Using LS-DYNA R12.0.0 dynamic simulation software, a fluid–structure interaction (FSI) model of the weeding wheel–water–soil system was established. By employing the central composite experimental design principle and considering the soil stir rate and coupling stress as indicators, the optimal structural parameter combination for the device is obtained: a dynamic–fixed blade cutting gap of 1.4 mm, a cutting angle of 10.95°, and a dynamic blade install angle of −3.44°. Field experiments demonstrated that the device achieved an average weeding rate of 89.7% and an average seedling damage rate of 1.9%, indicating excellent performance. This study contributes to improving weed mortality rates and provides valuable guidance for inter-row mechanical weeding technology. Full article

(This article belongs to the Special Issue Smart Mechanization and Automation in Agriculture)

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21 pages, 4622 KiB

Open AccessArticle

A Two-Stage Approach to the Study of Potato Disease Severity Classification

by Yanlei Xu, Zhiyuan Gao, Jingli Wang, Yang Zhou, Jian Li and Xianzhang Meng

Agriculture 2024, 14(3), 386; https://doi.org/10.3390/agriculture14030386 - 28 Feb 2024

Cited by 3 | Viewed by 1637

Abstract

Early blight and late blight are two of the most prevalent and severe diseases affecting potato crops. Efficient and accurate grading of their severity is crucial for effective disease management. However, existing grading methods are limited to assessing the severity of each disease independently, often resulting in low recognition accuracy and slow grading processes. To address these challenges, this study proposes a novel two-stage approach for the rapid severity grading of both early blight and late blight in potato plants. In this research, two lightweight models were developed: Coformer and SegCoformer. In the initial stage, Coformer efficiently categorizes potato leaves into three classes: those afflicted by early blight, those afflicted by late blight, and healthy leaves. In the subsequent stage, SegCoformer accurately segments leaves, lesions, and backgrounds within the images obtained from the first stage. Furthermore, it assigns severity labels to the identified leaf lesions. To validate the accuracy and processing speed of the proposed methods, we conduct experimental comparisons. The experimental results indicate that Coformer achieves a classification accuracy as high as 97.86%, while SegCoformer achieves an mIoU of 88.50% for semantic segmentation. The combined accuracy of this method reaches 84%, outperforming the Sit + Unet_V accuracy by 1%. Notably, this approach achieves heightened accuracy while maintaining a faster processing speed, completing image processing in just 258.26 ms. This research methodology effectively enhances agricultural production efficiency. Full article

(This article belongs to the Special Issue Smart Mechanization and Automation in Agriculture)

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18 pages, 17087 KiB

Open AccessArticle

Determination of Ellipsoidal Seed–Soil Interaction Parameters for DEM Simulation

by Tianyue Xu, Hao Fu, Jianqun Yu, Chunrong Li, Jingli Wang and Ruxin Zhang

Agriculture 2024, 14(3), 376; https://doi.org/10.3390/agriculture14030376 - 27 Feb 2024

Cited by 1 | Viewed by 1198

Abstract

During precision sowing, the contact process between the soil and seeds cannot be ignored. The constitutive relationship of soil is relatively complex, with characteristics such as high nonlinearity, while the contact mechanism between the soil and seeds is unclear. To better understand the contact between seeds and soil, it is necessary to establish a reasonable contact model. Ellipsoidal seeds, such as soybean, red bean, and kidney bean seeds, were adopted as research objects. In this paper, we used the discrete element method to establish an ellipsoidal seed–soil contact model. The JKR + bonding model was adopted for describing the adhesion between soil particles, and the Hertz–Mindlin new restitution (HMNS) model was used for ellipsoidal seed particles to eliminate the multiple contact point issue when modeling with the multi-sphere filling method. Moreover, both simulations and experiments were conducted to calibrate the interaction parameters between soil and seeds. The path of steepest ascent test and Box‒Behnken design (BBD) tests were also used, as well as direct shear tests. Thus, certain soil parameter values were obtained, namely the JKR surface energy was 4.436 J/m², the normal stiffness per unit area was 2.86 × 10⁶ N/m³, the shear stiffness per unit area was 5.54 × 10⁵ N/m³, the critical normal stress was 1833 Pa, and the critical shear stress was 3332 Pa. In addition, the simulation parameters for ellipsoidal seeds were obtained from previous works. Moreover, to obtain more accurate ellipsoidal seed–soil interaction parameters, collision tests, static friction tests, and rolling friction tests were adopted. A single-factor test was used to calibrate the ellipsoidal seed–soil interaction parameters. The calibration results were as follows: the collision restitution coefficients of ellipsoidal seeds with soil were all 0.25. The static friction coefficient of soybeans with soil was 0.6, that of red beans with soil was 0.65, and that of kidney beans with soil was 0.5. The rolling friction coefficient of soybeans with soil was 0.1, that of red beans with soil was 0.14, and that of kidney beans with soil was 0.14. Finally, the rationality of parameter selection was verified through piling tests between ellipsoidal seeds and soil. The relative error of the angle of repose of soybean/soil was 2.99%, that of red bean/soil was 0.60%, and that of kidney bean/soil was 0.55%. Thus, the feasibility and rationality of the contact models between the ellipsoidal seeds and soil established in this paper, as well as the parameter selection, were verified. Full article

(This article belongs to the Special Issue Smart Mechanization and Automation in Agriculture)

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21 pages, 10312 KiB

Open AccessArticle

Numerical Simulation and Experimental Study of Corn Straw Grinding Process Based on Computational Fluid Dynamics–Discrete Element Method

by Xin Wang, Haiqing Tian, Ziqing Xiao, Kai Zhao, Dapeng Li and Di Wang

Agriculture 2024, 14(2), 325; https://doi.org/10.3390/agriculture14020325 - 18 Feb 2024

Cited by 1 | Viewed by 1365

Abstract

To improve the operational efficiency of a hammer mill and delve into a high-efficiency, energy-saving grinding mechanism, the crucial parameters influencing the grinding of corn straw were identified as the spindle speed, hammer–sieve gap, and sieve pore diameter. According to the force analysis and kinematics analysis, the key factors affecting corn straw grinding were the spindle speed, the hammer–sieve gap, and the sieve pore diameter. The grinding process of corn straw was studied using computational fluid dynamics (CFDs) and the discrete element method (DEM) gas–solid coupling numerical simulation and experiment. The numerical simulation results showed that with the growth of time, the higher the spindle speed, the faster the bonds broke in each part, and the higher the grinding efficiency. When the energy loss of the hammer component was in the range of 985.6~1312.2 J, and the total collision force of the corn straw was greater than 47,032.5 N, the straw grinding effect was better, and the per kW·h yield was higher. The experimental results showed that the optimum combination of operating parameters was a spindle speed of 2625 r/min, a hammer-screen gap of 14 mm, and a sieve pore diameter of 8 mm. Finally, the CFD–DEM gas–solid coupling numerical simulation validation tests were performed based on the optimal combination of the operating parameters. The results showed that the energy loss of the hammer component was 1189.5 J, and the total collision force of the corn straw was 49,523.5 N, both of which were within the range of better results in terms of numerical simulation. Thus, the CFD–DEM gas–solid coupling numerical simulation could accurately predict the corn straw grinding process. This study provides a theoretical basis for improving a hammer mill’s key components and grinding performance. Meanwhile, the proposed gas–solid two-phase flow method provided theoretical references for other research in agricultural machinery. Full article

(This article belongs to the Special Issue Smart Mechanization and Automation in Agriculture)

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18 pages, 2097 KiB

Open AccessArticle

Design and Experiment of Air-Suction Maize Seed-Metering Device with Auxiliary Guide

by Li Ding, Yechao Yuan, Yufei Dou, Chenxu Li, Zhan He, Guangmeng Guo, Yi Zhang, Bingjie Chen and He Li

Agriculture 2024, 14(2), 169; https://doi.org/10.3390/agriculture14020169 - 23 Jan 2024

Viewed by 1458

Abstract

Due to the irrational design of the seed discharge plate and the vacuum chamber, the high-speed seed filling effect of the air-suction maize precision seed-metering device is poor. Therefore, an air-suction maize seed-metering device with an auxiliary guide is designed to realize high-speed precision seed discharging. An auxiliary guide filling theory is put forward, and the design of the seed plate type hole charging structure is formulated. Fluent 2022 software is used to analyze nine kinds of vacuum chamber structures; the optimal vacuum chamber structure parameters were determined by polar analysis. In order to investigate the changes of negative pressure and flow speed under the dynamic flow field, a slip grid was used to analyze the dynamic flow field with three different operating speeds and negative pressures. It found that the size of negative pressure did not affect the flow field distribution, and the pressure and flow speed gradually decreased as the distance from the inlet was farther away; meanwhile, the negative pressure distribution and air speed distribution were almost unchanged when the holes at different rotational speeds were at the same position. Finally, bench tests were carried out, and three indexes, namely, the qualified index, the multiple index and the missing index, were selected, with operating speed and negative pressure as factors, two-factor five-level orthogonal test was carried out, and the optimal parameter combinations at 6.0, 7.5, 9.0, 10.5, and 12 km/h forward velocity were derived and verified by regression equations. The results showed that the designed seed-metering device was repeated five times when the pressure of the vacuum chamber was −3.5 kPa and the rotational speed of the seed-metering device was 23 r/min, the average grain spacing qualified index was 95.8%, the missing index was 1.6%, the multiple index was 2.6%, and the indexes met the requirements of precision sowing. It is of great significance for our country’s seeder to develop in the direction of high-speed and precision. Full article

(This article belongs to the Special Issue Smart Mechanization and Automation in Agriculture)

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