Topic Editors

Dr. Changyuan Zhai
Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Department of Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK 74078, USA
Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA

Current Research on Intelligent Equipment for Agriculture

Abstract submission deadline
30 April 2024
Manuscript submission deadline
30 June 2024
Viewed by
7092

Topic Information

Dear Colleagues,

Intelligent agricultural machinery and equipment play an increasingly critical role in improving agricultural production and efficiency. Thanks to the advances in sensors, the internet of things (IoT), robotics, and artificial intelligence (AI), intelligent agricultural equipment integrates information perception, end‒edge‒cloud computing systems, and AI-enabled decision making and control to allow autonomous operations. Examples of intelligent agricultural equipment include autonomous tractors, robotic farming devices, agricultural drones, and smart sensor systems. Intelligent agricultural machinery and equipment are expected to promote innovations and practices in autonomous agricultural machinery operation, accurate data acquisition, smart decision making, operation quality and efficiency, and improved agricultural resilience and sustainability. We are pleased to announce a Topic entitled “Current Research on Intelligent Equipment for Agriculture” to showcase cutting-edge technologies and state-of-the-art research around core technological innovations. The topics of interest include, but are not limited to, the following:

(1) Intelligent perception for operational environment and autonomous navigation.
(2) Intelligent disease, pest, and weed identification and management.
(3) Smart sensors and IoT for agricultural machinery monitoring.
(4) Automatic control methods and systems for agricultural machinery operation.
(5) Intelligent farm equipment (e.g., soil preparation, planting, application, weeding, harvesting, transportation, and nondestructive testing).
(6) Agricultural robot technology for crop and livestock management.

Dr. Changyuan Zhai
Prof. Dr. Ning Wang
Dr. Jianfeng Zhou
Topic Editors

Keywords

  • precision agriculture
  • big data analytics
  • agricultural robot
  • intelligent perception
  • internet of things
  • decision making
  • artificial intelligence
  • precision livestock farming

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Agriculture
agriculture
3.6 3.6 2011 17.7 Days CHF 2600 Submit
AgriEngineering
agriengineering
2.8 4.6 2019 25.8 Days CHF 1600 Submit
Agronomy
agronomy
3.7 5.2 2011 15.8 Days CHF 2600 Submit

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Published Papers (8 papers)

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17 pages, 21783 KiB  
Article
Application of Disturbance Observer-Based Fast Terminal Sliding Mode Control for Asynchronous Motors in Remote Electrical Conductivity Control of Fertigation Systems
Agriculture 2024, 14(2), 168; https://doi.org/10.3390/agriculture14020168 - 23 Jan 2024
Viewed by 465
Abstract
In addressing the control of asynchronous motors in the remote conductivity of fertigation machines, this study proposes a joint control strategy based on the Fast Terminal Sliding Mode Control-Disturbance Observer (FTSMC-DO) system for asynchronous motors. The goal is to enhance the dynamic performance [...] Read more.
In addressing the control of asynchronous motors in the remote conductivity of fertigation machines, this study proposes a joint control strategy based on the Fast Terminal Sliding Mode Control-Disturbance Observer (FTSMC-DO) system for asynchronous motors. The goal is to enhance the dynamic performance and disturbance resistance of asynchronous motors, particularly under low-speed operating conditions. The approach involves refining the two-degree-of-freedom internal model controller using fractional-order functions to explicitly separate the controller’s robustness and tracking capabilities. To mitigate the motor’s sensitivity to external disturbances during variable speed operations, a load disturbance observer is introduced, employing hyperbolic tangent and Fal functions for real-time monitoring and compensation, seamlessly integrated into the sliding mode controller. To address issues related to low-speed chattering typically associated with sliding mode controllers, this study introduces a revised non-singular fast terminal sliding mode surface. Additionally, guided by fuzzy control principles, the study enables real-time selection of sliding mode approaching law parameters. Experimental results from the asynchronous motor control platform demonstrate that FTSMC-DO control significantly reduces adjustment time and speed fluctuations during operation, minimizing the impact of load disturbances on the system. The system exhibits robust disturbance rejection, improved robustness, and enhanced control capability. Furthermore, field tests validate the effectiveness of the FTSMC-DO system in regulating remote electrical conductivity (EC) levels. The control time is observed to be less than 120 s, overshoot less than 16.1%, and EC regulation within 0.2 mS·cm−1 over a pipeline distance of 120 m. The FTSMC-DO control consistently achieves the desired EC levels with minimal fluctuation and overshoot, outperforming traditional PID and SMC methods. This high level of precision is crucial for ensuring optimal nutrient delivery and efficient water usage in agricultural irrigation systems, highlighting the system’s potential as a valuable tool in modern, sustainable farming practices. Full article
(This article belongs to the Topic Current Research on Intelligent Equipment for Agriculture)
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17 pages, 6045 KiB  
Article
Is It Possible to Measure the Quality of Sugarcane in Real-Time during Harvesting Using Onboard NIR Spectroscopy?
AgriEngineering 2024, 6(1), 64-80; https://doi.org/10.3390/agriengineering6010005 - 09 Jan 2024
Viewed by 475
Abstract
In-field quality prediction in agricultural products is mainly based on near-infrared spectroscopy (NIR). However, initiatives applied to sugarcane quality are only observed under laboratory-controlled conditions. This study proposed a framework for NIR spectroscopy sensing to measure sugarcane quality during a real harvest operation. [...] Read more.
In-field quality prediction in agricultural products is mainly based on near-infrared spectroscopy (NIR). However, initiatives applied to sugarcane quality are only observed under laboratory-controlled conditions. This study proposed a framework for NIR spectroscopy sensing to measure sugarcane quality during a real harvest operation. A platform was built to support the system composed of the NIR sensor and external lighting on the elevator of a sugarcane harvester. Real-time data were acquired in commercial fields. Georeferenced samples were collected for calibration, validation, and adjustment of the multivariate models by partial least squares (PLS) regression. In addition, subsamples of defibrated cane were NIR-acquired for the development of calibration transfer models by piecewise direct standardization (PDS). The method allowed the adjustment of the spectra collected in real time to predict the quality properties of soluble solids content (Brix), apparent sucrose in juice (Pol), fiber, cane Pol, and total recoverable sugar (TRS). The results of the relative mean square error of prediction (RRMSEP) were from 1.80 to 2.14%, and the ratio of interquartile performance (RPIQ) was from 1.79 to 2.46. The PLS-PDS models were applied to data acquired in real-time, allowing estimation of quality properties and identification of the existence of spatial variability in quality. The results showed that it is possible to monitor the spatial variability of quality properties in sugarcane in the field. Future studies with a broader range of quality attribute values and the evaluation of different configurations for sensing devices, calibration methods, and data processing are needed. The findings of this research will enable a valuable spatial information layer for the sugarcane industry, whether for agronomic decision-making, industrial operational planning, or financial management between sugar mills and suppliers. Full article
(This article belongs to the Topic Current Research on Intelligent Equipment for Agriculture)
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22 pages, 3478 KiB  
Article
Model for Detecting Boom Height Based on an Ultrasonic Sensor for the Whole Growth Cycle of Wheat
Agriculture 2024, 14(1), 21; https://doi.org/10.3390/agriculture14010021 - 22 Dec 2023
Viewed by 547
Abstract
Ultrasonic feedback energy is affected by the variety, planting, and growth state of crops; therefore, it is difficult to find applications for this energy in precision agriculture systems. To this end, an ultrasonic sensor was mounted in a spray boom height detection system. [...] Read more.
Ultrasonic feedback energy is affected by the variety, planting, and growth state of crops; therefore, it is difficult to find applications for this energy in precision agriculture systems. To this end, an ultrasonic sensor was mounted in a spray boom height detection system. Winter wheat was used as the test object to obtain feedback energy values for the spray boom height from the top of the wheat in the field during six critical growth stages: the standing stage, the jointing stage, the booting stage, the heading stage, the filling stage, and the maturity stage. The relationship between the actual value of the height from the spray boom at the top of the wheat (Habw) and the detected value of the height from the spray boom at the top of the wheat (Hdbw) was analyzed. A spray boom height detection model based on the ultrasonic sensor during the full growth cycle of wheat was determined. Field validation tests showed that the applicability of the spray boom height detection distance (Dd) of the spray boom height detection model proposed in the present study was 450~950 mm. Within the applicable Dd range, the detection error of the detection model was ≤50 mm during the full growth cycle. This study provides a method for constructing a boom height detection model based on the whole growth cycle of wheat, which improves the reliability and accuracy of ultrasonic boom height detection for different wheat growth stages. The proposed method solves the problem of low accuracy of repeated detection of low-cost ultrasonic sensors in different environments and can provide technical support for improving field applications of the boom height control system based on ultrasonic sensors. Full article
(This article belongs to the Topic Current Research on Intelligent Equipment for Agriculture)
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24 pages, 6640 KiB  
Article
Design and Experiment of a Breakpoint Continuous Spraying System for Automatic-Guidance Boom Sprayers
Agriculture 2023, 13(12), 2203; https://doi.org/10.3390/agriculture13122203 - 27 Nov 2023
Cited by 3 | Viewed by 892
Abstract
Repeated and missed spraying are common problems during the working of boom sprayers, especially in the breakpoint continuous process. Therefore, the present study investigated a breakpoint continuous spraying system for automatic-guidance boom sprayers based on a hysteresis compensation algorithm for spraying. An operational [...] Read more.
Repeated and missed spraying are common problems during the working of boom sprayers, especially in the breakpoint continuous process. Therefore, the present study investigated a breakpoint continuous spraying system for automatic-guidance boom sprayers based on a hysteresis compensation algorithm for spraying. An operational breakpoint identification algorithm, which combines a real-time kinematic global navigation satellite system (RTK-GNSS) and wheel odometer, was proposed; a pre-adjusted proportional-integral-derivative (PID) control algorithm for the opening degree of the proportional control valve was designed in thus study. Tests were conducted to establish equations correlating the opening degree of the proportional control valve, pump output flow rate, and main pipeline flow rate, with an R2 ≥ 0.9525. The time to adjust to the target flow rate was experimentally tested. The breakpoint identification accuracy of the RTK-GNSS and RTK-GNSS + wheel odometer was experimentally assessed. A field spraying deposition variation experiment was conducted. According to the results, the system effectively eliminated missed spraying, with a maximum repeated spraying distance of ≤3.3 m, and it achieved a flow control error within 3%. This system also reduced the repeated spraying area and enhanced the pesticide spraying quality of breakpoint continuous spraying for automatic-guidance boom sprayers. Full article
(This article belongs to the Topic Current Research on Intelligent Equipment for Agriculture)
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18 pages, 4694 KiB  
Article
Atomization Characteristics of a Hollow Cone Nozzle for Air-Assisted Variable-Rate Spraying
Agriculture 2023, 13(10), 1992; https://doi.org/10.3390/agriculture13101992 - 13 Oct 2023
Viewed by 827
Abstract
During variable-rate spraying in orchards, the atomization characteristics and distribution of droplets in and out of the target area can be affected by the sprayer pressure. In this study, a variable-rate spraying control system test bench was designed, and a hollow cone nozzle [...] Read more.
During variable-rate spraying in orchards, the atomization characteristics and distribution of droplets in and out of the target area can be affected by the sprayer pressure. In this study, a variable-rate spraying control system test bench was designed, and a hollow cone nozzle QY82.317.22 was selected. The droplet atomization characteristics, including volume median diameter (Dv0.5), the relative span of the droplet spectrum, and droplet velocity at different spray pressures, were studied at distances ranging from 0.4 to 2.4 m from the nozzle orifice with an air velocity of 10 m/s at the nozzle orifice position. The effects of longitudinal distance, transverse distance, and spray pressure on Dv0.5, relative span, and droplet velocity were analysed by multiple linear regression analysis, and the regression model was established. The experimental results show that at a longitudinal distance of 1.8 m, Dv0.5 ranges from 120 to 150 μm, meeting the requirements for optimal droplet size for controlling crawling pests and plant diseases on crop leaves; and the relative span is 1.2, indicating a wide droplet spectrum. At different pressure conditions, Dv0.5 decreases as pressure increases. Through multiple linear regression analysis, the longitudinal distance, the transverse distance, and the spray pressure have high significance for Dv0.5 and the droplet velocity. The longitudinal distance and the transverse distance have a highly significant effect on the relative span. In this study, the mathematical relational model of droplet characteristics at different spatial positions and different pressures was established, providing an agricultural reference for predicting the droplet characteristics at different spatial positions to achieve the best application effect. This model is conducive to the effective use of pesticides and reduces environmental pollution. Full article
(This article belongs to the Topic Current Research on Intelligent Equipment for Agriculture)
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19 pages, 6945 KiB  
Article
System of Counting Green Oranges Directly from Trees Using Artificial Intelligence
AgriEngineering 2023, 5(4), 1813-1831; https://doi.org/10.3390/agriengineering5040111 - 09 Oct 2023
Viewed by 1238
Abstract
Agriculture is one of the most essential activities for humanity. Systems capable of automatically harvesting a crop using robots or performing a reasonable production estimate can reduce costs and increase production efficiency. With the advancement of computer vision, image processing methods are becoming [...] Read more.
Agriculture is one of the most essential activities for humanity. Systems capable of automatically harvesting a crop using robots or performing a reasonable production estimate can reduce costs and increase production efficiency. With the advancement of computer vision, image processing methods are becoming increasingly viable in solving agricultural problems. Thus, this work aims to count green oranges directly from trees through video footage filmed in line along a row of orange trees on a plantation. For the video image processing flow, a solution was proposed integrating the YOLOv4 network with object-tracking algorithms. In order to compare the performance of the counting algorithm using the YOLOv4 network, an optimal object detector was simulated in which frame-by-frame corrected detections were used in which all oranges in all video frames were detected, and there were no erroneous detections. Being the scientific and technological innovation the possibility of distinguishing the green color of the fruits from the green color of the leaves. The use of YOLOv4 together with object detectors managed to reduce the number of double counting errors and obtained a count close to the actual number of oranges visible in the video. The results were promising, with an mAP50 of 80.16%, mAP50:95 of 53.83%, precision of 0.92, recall of 0.93, F1-score of 0.93, and average IoU of 82.08%. Additionally, the counting algorithm successfully identified and counted 204 oranges, closely approaching the actual count of 208. The study also resulted in a database with an amount of 644 images containing 43,109 orange annotations that can be used in future works. Full article
(This article belongs to the Topic Current Research on Intelligent Equipment for Agriculture)
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17 pages, 6518 KiB  
Article
Evaluation Method of Potato Storage External Defects Based on Improved U-Net
Agronomy 2023, 13(10), 2503; https://doi.org/10.3390/agronomy13102503 - 28 Sep 2023
Cited by 1 | Viewed by 790
Abstract
The detection of potato surface defects is the key to ensuring potato storage quality. This research explores a method for detecting surface flaws in potatoes, which can promptly identify storage defects such as dry rot and the shriveling of potatoes. In order to [...] Read more.
The detection of potato surface defects is the key to ensuring potato storage quality. This research explores a method for detecting surface flaws in potatoes, which can promptly identify storage defects such as dry rot and the shriveling of potatoes. In order to assure the quality and safety of potatoes in storage, we used a closed keying method to obtain the pixel area of the mask image for a potato’s surface. The improved U-Net realizes the segmentation and pixel area measurement of potato surface defects and enhances the feature extraction capability of the network model by adding a convolutional block attention module (CBAM) to the baseline network. Compared with the baseline network, the improved U-Net showed a much better performance with respect to MIoU (mean intersection over union), precision, and Fβ, which were improved by 1.99%, 8.27%, and 7.35%, respectively. The effect and efficiency of the segmentation algorithm were also superior compared to other networks. Calculating the fraction of potato surface faults in potato mask images allows for the quantitative detection of potato surface problems. The experimental results show that the absolute accuracy of the quantitative potato evaluation method proposed in this study was greater than 97.55%, allowing it to quantitatively evaluate potato surface defects, provide methodological references for potato detection in the field of deep processing of potatoes, and provide a theoretical basis and technical references for the evaluation of potato surface defects under complex lighting conditions. Full article
(This article belongs to the Topic Current Research on Intelligent Equipment for Agriculture)
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16 pages, 5916 KiB  
Article
Modeling and Optimizing the Performance of Green Forage Maize Harvester Header Using a Combined Response Surface Methodology–Artificial Neural Network Approach
Agriculture 2023, 13(10), 1890; https://doi.org/10.3390/agriculture13101890 - 27 Sep 2023
Cited by 2 | Viewed by 734
Abstract
Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing and statistical [...] Read more.
Green forage maize harvesters face challenges such as high soil humidity and soft soil in the field, mismatched working parameters, and poor reliability and adaptability. These challenges often result in header blockage, significant harvest loss, and increased energy consumption. Traditional testing and statistical analysis methods used in most existing studies are limited by complex test processes, their time-consuming nature, high costs, and poor prediction accuracy. To address these problems, a test bench was constructed to analyze the effects of forward speed, cutting height, number of rows, and their interactions on specific energy consumption and harvest loss of the green forage maize (GFM) header. A combined response surface method (RSM)–artificial neural network (ANN) approach is proposed for modeling and predicting the performance parameters of the header. The optimal conditions were determined by optimizing the specific energy consumption and loss rate. The optimal combination parameters are a forward speed of 1.6 km/h, a cutting height of 167 mm, and a number of rows of 4. However, RSM–ANN has larger R2 values and lower root mean square errors (RMSE) and mean square errors (MSE) compared to RSM. Specifically, the R2 of the RSM–ANN model for specific energy consumption and loss rate a 0.9925 and 0.9906, MSE are 0.00001775 and 0.004558, and RMSE are 0.004214 and 0.006752, respectively. The results show that the combined RSM–ANN method has higher precision and accuracy and can better predict and optimize the header performance. This study overcomes the limitations of traditional methods and has the potential to provide data and method references for the design, optimization, prediction, and intelligent diagnosis of faults in the operational parameters of agricultural machinery. Full article
(This article belongs to the Topic Current Research on Intelligent Equipment for Agriculture)
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