Current Research on Intelligent Equipment for Agriculture

The autonomous navigation system of feed-pushing robots is one of the key technologies for the intelligent breeding of dairy cows, and its accuracy has a significant influence on the quality of feed-pushing operations. Currently, the navigation methods of feed-pushing robots in the complex environment of cattle barns mainly include visual, LiDAR, and geomagnetic navigation, but there are still problems relating to low navigation accuracy. An autonomous navigation system based on ultra-wideband (UWB) positioning utilizing the dynamic forward-looking distance pure pursuit algorithm is proposed in this paper. First, six anchor nodes were arranged in the corners and central feeding aisle of a 30 × 86 m rectangular standard barn to form a rectangular positioning area. Then, utilizing the 9ITL-650 feed-pushing robot as a platform and integrating UWB wireless positioning technology, a global coordinate system for the cattle barn was established, and the expected path was planned. Finally, the pure pursuit model was improved based on the robot’s two-wheel differential kinematics model, and a dynamic forward-looking distance pure pursuit controller based on PID regulation was designed to construct a comprehensive autonomous navigation control system. Subsequently, field experiments were conducted in the cattle barn. The experimental results show that the static positioning accuracy of the UWB system for the feed-pushing robot was less than 16 cm under no-line-of-sight conditions in the cattle barn. At low speeds, the robot was subjected to linear tracking comparative experiments with forward-looking distances of 50, 100, 150, and 200 cm. The minimum upper-line distance of the dynamic forward-looking distance model was 205.43 cm. In the steady-state phase, the average lateral deviation was 3.31 cm, with an average standard deviation of 2.58 cm and the average root mean square error (RMSE) of 4.22 cm. Compared with the fixed forward-looking distance model, the average lateral deviation, the standard deviation, and the RMSE were reduced by 42.83%, 37.07%, and 42.90%, respectively. The autonomous navigation experiments conducted on the feed-pushing robot at travel speeds of 6, 8, and 10 m/min demonstrated that the maximum average lateral deviation was 7.58 cm, the maximum standard deviation was 8.22 cm, and the maximum RMSE was 11.07 cm, meeting the autonomous navigation requirements for feed-pushing operations in complex barn environments. This study provides support for achieving high-precision autonomous navigation control technology in complex environments. Full article

In breeding programs, the demand for high-throughput phenotyping is substantial as it serves as a crucial tool for enhancing technological sophistication and efficiency. This advanced approach to phenotyping enables the rapid and precise measurement of complex traits. Therefore, the objective of this study was to estimate the correlation between vegetation indices (VIs) and grain yield and to identify the optimal timing for accurately estimating yield. Furthermore, this study aims to employ photographic quantification to measure the characteristics of corn ears and establish their correlation with corn grain yield. Ten corn hybrids were evaluated in a Complete Randomized Block (CRB) design with three replications across three locations. Vegetation and green leaf area indices were estimated throughout the growing cycle using an unmanned aerial vehicle (UAV) and were subsequently correlated with grain yield. The experiments consistently exhibited high levels of experimental quality across different locations, characterized by both high accuracy and low coefficients of variation. The experimental quality was consistently significant across all sites, with accuracy ranging from 79.07% to 95.94%. UAV flights conducted at the beginning of the crop cycle revealed a positive correlation between grain yield and the evaluated vegetation indices. However, a positive correlation with yield was observed at the V5 vegetative growth stage in Lavras and Ijaci, as well as at the V8 stage in Nazareno. In terms of corn ear phenotyping, the regression coefficients for ear width, length, and total number of grains (TNG) were 0.92, 0.88, and 0.62, respectively, demonstrating a strong association with manual measurements. The use of imaging for ear phenotyping is promising as a method for measuring corn components. It also enables the identification of the optimal timing to accurately estimate corn grain yield, leading to advancements in the agricultural imaging sector by streamlining the process of estimating corn production. Full article

In order to solve the problem of the low pickup efficiency of the robotic arm when harvesting safflower filaments, we established a pickup trajectory cycle and an improved velocity profile model for the harvest of safflower filaments according to the growth characteristics of safflower. Bezier curves were utilized to optimize the picking trajectory, mitigating the abrupt changes produced by the delta mechanism during operation. Furthermore, to overcome the slow convergence speed and the tendency of the ant colony algorithm to fall into local optima, a safflower harvesting trajectory planning method based on an ant colony genetic algorithm is proposed. This method includes enhancements through an adaptive adjustment mechanism, pheromone limitation, and the integration of optimized parameters from genetic algorithms. An optimization model with working time as the objective function was established in the MATLAB environment, and simulation experiments were conducted to optimize the trajectory using the designed ant colony genetic algorithm. The simulation results show that, compared to the basic ant colony algorithm, the path length with the ant colony genetic algorithm is reduced by 1.33% to 7.85%, and its convergence stability significantly surpasses that of the basic ant colony algorithm. Field tests demonstrate that, while maintaining an S-curve velocity, the ant colony genetic algorithm reduces the harvesting time by 28.25% to 35.18% compared to random harvesting and by 6.34% to 6.81% compared to the basic ant colony algorithm, significantly enhancing the picking efficiency of the safflower-harvesting robotic arm. Full article

The high clearance spray is a type of large and efficient agricultural machinery used for plant protection, and path tracking control is the key to ensure the efficient and safe operation of spray. Sliding mode control and other methods are commonly used abroad to track vehicles, while fuzzy control, neural networks and other methods are commonly used at home. However, domestic and foreign research on autonomous agricultural machinery is mainly focused on tractors and other machinery, while research on self-propelled spray in high clearance is less abundant. This paper takes the path tracking algorithm in the integrated navigation system of spray as the main research goal, studies the path tracking control algorithm for straight lines and turning curves that can realize the automatic driving of spray by establishing the path tracking algorithm for unmanned spray based on dual control strategies, designs the path tracking controller, including the preview model theoretical path tracking controller and variable domain fuzzy controller, and determines the preview model through the design of the preview model theoretical path tracking controller. The lateral and longitudinal errors of the model algorithm are analyzed, and the driving characteristics under the complex spray road surface are analyzed. The design of the variable domain fuzzy predictor theory path tracking controller is proposed, and the design of the road model selection controller is calculated and analyzed in detail, including the determination of the road roughness coefficient and the selection of the range of the difference between the average value of the excitation before and after sampling, which improves the performance of the spray path tracking algorithm. The experiment shows that the proposed path tracking control algorithm can meet the path tracking requirements of unmanned spray in the current road environment, and provide a reliable solution for the automatic control of high clearance spray. Full article

Scales are widely used in many agricultural applications, ranging from weighing crops at harvest to determine crop yields to regularly weighing animals to determine growth rate. In agricultural research applications, there is a long history of measuring crop water use (evapotranspiration [ET]) using a particular type of scale called weighing lysimeters. Typically, weighing lysimeters require very accurate data logging systems that tend to be expensive. Recent developments in open-source technologies, such as micro-controllers and Internet of Things (IoT) platforms, have created opportunities for developing effective and affordable ways to monitor crop water use and transmit the data to the Internet in near real-time. Therefore, this study aimed to create an affordable Internet of Things (IoT) scale system to measure crop ET. A scale system to monitor crop ET was developed using an Arduino-compatible microcontroller with cell phone communication, electronic load cells, an Inter-Integrated Circuit (I2C) multiplexer, and analog-to-digital converters (ADCs). The system was powered by a LiPo battery, charged by a small (6 W) solar panel. The IoT scale system was programmed to collect data from the load cells at regular time intervals and send the data to the ThingSpeak IoT platform. The system performed successfully during indoor and outdoor experiments conducted in 2023 at the Clemson University Edisto Research and Education Center, Blackville, SC. Calibrations relating the measured output of the scale load cells to changes in mass resulted in excellent linear relationships during the indoor (r² = 1.0) and outdoor experiments (r² = 0.9994). The results of the outdoor experiments showed that the IoT scale system could accurately measure changes in lysimeter mass during several months (Feb to Jun) without failure in data collection or transmission. The changes in lysimeter mass measured during that period reflected the same trend as concurrent soil moisture data measured at a nearby weather station. The changes in lysimeter mass measured with the IoT scale system during the outdoor experiment were accurate enough to derive daily and hourly crop ET and even detect what appeared to be dew formation during the morning hours. The IoT scale system can be built using open-source, off-the-shelf electronic components which can be purchased online and easily replaced or substituted. The system can also be developed at a fraction of the cost of data logging, communication, and visualization systems typically used for lysimeter and scale applications. Full article

The pulse-width modulation (PWM) variable spray system is the most widely used variable spray system in the world at present, which has the characteristics of a fast response, large flow adjustment range, and good atomization. Recently, the pressure fluctuation and droplet deposition uniformity of the PWM variable spray system caused by the intermittent spray mode of the nozzle have attracted more and more attention. In this study, a method for eliminating the inhomogeneity of ground deposition in low-frequency PWM variable sprays based on a staggered-phase drive mode was proposed, and a PWM variable spray system was built. The experimental results indicated that the pressure fluctuation amplitude upstream of the nozzle of the PWM variable spray system with the staggered-phase drive was reduced by 40.91%, and the dispersion rate of the pressure fluctuation was reduced by 62.78% (the initial pressure was 0.3 MPa, solenoid valve frequency was 5 Hz, and duty cycle was 50%). The PWM control parameters had a significant effect on the upstream pressure fluctuation (initial pressure > duty cycle > frequency). The droplet spectrum relative span of the staggered phased PWM variable spray system decreased by 24.83%, the coefficient of variation of the droplet particle size decreased by 4.40%, the particle size was more uniform, and the atomization effect was improved. The average deposition of droplets in the forward direction driven by the staggered phase was 4.87% greater than that in the same phase, and the variation rate decreased by 20.87%. The average deposition amount increased, and the deposition became more uniform. Staggered-phase spray control could effectively reduce the inhomogeneity of deposition in low-frequency PWM intermittent spraying. This research provides strong technical support for a precision variable spraying effect and droplet drift prevention. Full article

In the context of plant factories relying on artificial light sources, energy consumption stands out as a significant cost factor. Implementing early seedling removal and replacement operations has the potential to enhance the yield per unit area and the per-energy consumption. Nevertheless, conventional transplanting machines are limited to handling older seedlings with well-established roots. This study addresses these constraints by introducing a transplanting workstation based on the UR5 industrial robot tailored to early plug tray seedlings in plant factories. A diagonal oblique insertion end effector was employed, ensuring stable grasping even in loose substrate conditions. Robotic vision technology was utilized for the recognition of nongerminating holes and inferior seedlings. The integrated robotic system seamlessly managed the entire process of removing and replanting the plug tray seedlings. The experimental findings revealed that the diagonal oblique-insertion end effector achieved a cleaning rate exceeding 65% for substrates with a moisture content exceeding 70%. Moreover, the threshold-segmentation-based method for identifying empty holes and inferior seedlings demonstrated a recognition accuracy surpassing 97.68%. The success rate for removal and replanting in transplanting process reached an impressive 95%. This transplanting robot system serves as a reference for the transplantation of early seedlings with loose substrate in plant factories, holding significant implications for improving yield in plant factory settings. Full article

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

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

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 (H_abw) and the detected value of the height from the spray boom at the top of the wheat (H_dbw) 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 (D_d) of the spray boom height detection model proposed in the present study was 450~950 mm. Within the applicable D_d 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

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

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 (D_v0.5), 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 D_v0.5, 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, D_v0.5 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, D_v0.5 decreases as pressure increases. Through multiple linear regression analysis, the longitudinal distance, the transverse distance, and the spray pressure have high significance for D_v0.5 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

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

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 $F_{\beta }$, 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

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