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The leaf area index (LAI) is a key parameter for describing the canopy structure of apple trees. This index is also employed in evaluating the amount of pesticide sprayed per unit volume of apple trees. Hence, numerous manual and automatic methods have been explored for LAI estimation. In this work, the leaf area indices for different types of apple trees are obtained in terms of multispectral remote-sensing data collected with an unmanned aerial vehicle (UAV), along with simultaneous measurements of apple orchards. The proposed approach was tested on apple trees of the “Fuji”, “Golden Delicious”, and “Ruixue” types, which were planted in the Apple Experimental Station of the Northwest Agriculture and Forestry University in Baishui County, Shaanxi Province, China. Five vegetation indices of strong correlation with the apple leaf area index were selected and used to train models of support vector regression (SVR) and gradient-boosting decision trees (GBDT) for predicting the leaf area index of apple trees. The best model was selected based on the metrics of the coefficient of determination (R2) and the root-mean-square error (RMSE). The experimental results showed that the gradient-boosting decision tree model achieved the best performance with an R2 of 0.846, an RMSE of 0.356, and a spatial efficiency (SPAEF) of 0.57. This demonstrates the feasibility of our approach for fast and accurate remote-sensing-based estimation of the leaf area index of apple trees.
Zhijie Liu; Pengju Guo; Heng Liu; Pan Fan; Pengzong Zeng; Xiangyang Liu; Ce Feng; Wang Wang; Fuzeng Yang. Gradient Boosting Estimation of the Leaf Area Index of Apple Orchards in UAV Remote Sensing. Remote Sensing 2021, 13, 3263 .
AMA StyleZhijie Liu, Pengju Guo, Heng Liu, Pan Fan, Pengzong Zeng, Xiangyang Liu, Ce Feng, Wang Wang, Fuzeng Yang. Gradient Boosting Estimation of the Leaf Area Index of Apple Orchards in UAV Remote Sensing. Remote Sensing. 2021; 13 (16):3263.
Chicago/Turabian StyleZhijie Liu; Pengju Guo; Heng Liu; Pan Fan; Pengzong Zeng; Xiangyang Liu; Ce Feng; Wang Wang; Fuzeng Yang. 2021. "Gradient Boosting Estimation of the Leaf Area Index of Apple Orchards in UAV Remote Sensing." Remote Sensing 13, no. 16: 3263.
In recent years, research into and development of hillside tractors has become a popular topic in the field of agricultural engineering in China. To solve the main problems associated with a low adjustment range of the working speed, complex operation, and low safety for slope operation of medium-sized crawler tractors, a hydrostatic drive system that can be used for hillside crawler tractors was designed. According to the operation requirements of a hillside crawler tractor, the parameters of the three-cylinder diesel engine, hydrostatic transmission (HST), drive rear axle, and other key components of the drive system were matched after the force and motion analyses of the tractor, and then the main performance indicators, including the traction performance, system pressure and working speed of the drive system were verified. On this basis, a drive system performance test bench was built, and the traction performance and starting acceleration performance of the drive system was tested. The results of the traction bench test show that when the engine was at the maximum torque point of 1700 r/min, the maximum theoretical tractive force outputted by the tractor in Gear I was 114,563 N, and the maximum theoretical tractive force outputted by tractor in Gear II was 10,959.2 N, which were both larger than the traction resistance of 9550.6 N experienced by the hillside tractor ploughing on the slope. The results of the initial acceleration bench test show that the tractor driving speed can gradually increase with increasing output of the variable pump and can reach the maximum in 3 s. When the tractor was driving on flat ground, the maximum driving speeds of Gear I, Gear II, and Gear III were 4.65 km/h, 6.58 km/h, and 8.57 km/h, respectively, which are close to the theoretical values. When the tractor was driving on a 15° slope, the maximum driving speeds of Gear I, Gear II, and Gear III were 4.55 km/h, 6.25 km/h, and 8.28 km/h, respectively. It can be concluded that the design matching of the drive system is reasonable, the speed consistency is good and there is enough power reserve, which can meet the requirements for a large workload.
Zhijie Liu; Guoqiang Zhang; Guoping Chu; Hanlin Niu; Yazhou Zhang; Fuzeng Yang. Design Matching and Dynamic Performance Test for an HST-Based Drive System of a Hillside Crawler Tractor. Agriculture 2021, 11, 466 .
AMA StyleZhijie Liu, Guoqiang Zhang, Guoping Chu, Hanlin Niu, Yazhou Zhang, Fuzeng Yang. Design Matching and Dynamic Performance Test for an HST-Based Drive System of a Hillside Crawler Tractor. Agriculture. 2021; 11 (5):466.
Chicago/Turabian StyleZhijie Liu; Guoqiang Zhang; Guoping Chu; Hanlin Niu; Yazhou Zhang; Fuzeng Yang. 2021. "Design Matching and Dynamic Performance Test for an HST-Based Drive System of a Hillside Crawler Tractor." Agriculture 11, no. 5: 466.
The apple target recognition algorithm is one of the core technologies of the apple picking robot. However, most of the existing apple detection algorithms cannot distinguish between the apples that are occluded by tree branches and occluded by other apples. The apples, grasping end-effector and mechanical picking arm of the robot are very likely to be damaged if the algorithm is directly applied to the picking robot. Based on this practical problem, in order to automatically recognize the graspable and ungraspable apples in an apple tree image, a light-weight apple targets detection method was proposed for picking robot using improved YOLOv5s. Firstly, BottleneckCSP module was improved designed to BottleneckCSP-2 module which was used to replace the BottleneckCSP module in backbone architecture of original YOLOv5s network. Secondly, SE module, which belonged to the visual attention mechanism network, was inserted to the proposed improved backbone network. Thirdly, the bonding fusion mode of feature maps, which were inputs to the target detection layer of medium size in the original YOLOv5s network, were improved. Finally, the initial anchor box size of the original network was improved. The experimental results indicated that the graspable apples, which were unoccluded or only occluded by tree leaves, and the ungraspable apples, which were occluded by tree branches or occluded by other fruits, could be identified effectively using the proposed improved network model in this study. Specifically, the recognition recall, precision, mAP and F1 were 91.48%, 83.83%, 86.75% and 87.49%, respectively. The average recognition time was 0.015 s per image. Contrasted with original YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 model, the mAP of the proposed improved YOLOv5s model increased by 5.05%, 14.95%, 4.74% and 6.75% respectively, the size of the model compressed by 9.29%, 94.6%, 94.8% and 15.3% respectively. The average recognition speeds per image of the proposed improved YOLOv5s model were 2.53, 1.13 and 3.53 times of EfficientDet-D0, YOLOv4 and YOLOv3 and model, respectively. The proposed method can provide technical support for the real-time accurate detection of multiple fruit targets for the apple picking robot.
Bin Yan; Pan Fan; Xiaoyan Lei; Zhijie Liu; Fuzeng Yang. A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5. Remote Sensing 2021, 13, 1619 .
AMA StyleBin Yan, Pan Fan, Xiaoyan Lei, Zhijie Liu, Fuzeng Yang. A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5. Remote Sensing. 2021; 13 (9):1619.
Chicago/Turabian StyleBin Yan; Pan Fan; Xiaoyan Lei; Zhijie Liu; Fuzeng Yang. 2021. "A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5." Remote Sensing 13, no. 9: 1619.
In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot’s vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red–green–blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.
Pan Fan; Guodong Lang; Bin Yan; Xiaoyan Lei; Pengju Guo; Zhijie Liu; Fuzeng Yang. A Method of Segmenting Apples Based on Gray-Centered RGB Color Space. Remote Sensing 2021, 13, 1211 .
AMA StylePan Fan, Guodong Lang, Bin Yan, Xiaoyan Lei, Pengju Guo, Zhijie Liu, Fuzeng Yang. A Method of Segmenting Apples Based on Gray-Centered RGB Color Space. Remote Sensing. 2021; 13 (6):1211.
Chicago/Turabian StylePan Fan; Guodong Lang; Bin Yan; Xiaoyan Lei; Pengju Guo; Zhijie Liu; Fuzeng Yang. 2021. "A Method of Segmenting Apples Based on Gray-Centered RGB Color Space." Remote Sensing 13, no. 6: 1211.
In the vision system of apple-picking robots, the main challenge is to rapidly and accurately identify the apple targets with varying halation and shadows on their surfaces. To solve this problem, this study proposes a novel, multi-feature, patch-based apple image segmentation technique using the gray-centered red-green-blue (RGB) color space. The developed method presents a multi-feature selection process, which eliminates the effect of halation and shadows in apple images. By exploring all the features of the image, including halation and shadows, in the gray-centered RGB color space, the proposed algorithm, which is a generalization of K-means clustering algorithm, provides an efficient target segmentation result. The proposed method is tested on 240 apple images. It offered an average accuracy rate of 98.79%, a recall rate of 99.91%, an F1 measure of 99.35%, a false positive rate of 0.04%, and a false negative rate of 1.18%. Compared with the classical segmentation methods and conventional clustering algorithms, as well as the popular deep-learning segmentation algorithms, the proposed method can perform with high efficiency and accuracy to guide robotic harvesting.
Pan Fan; Guodong Lang; Pengju Guo; Zhijie Liu; Fuzeng Yang; Bin Yan; Xiaoyan Lei. Multi-Feature Patch-Based Segmentation Technique in the Gray-Centered RGB Color Space for Improved Apple Target Recognition. Agriculture 2021, 11, 273 .
AMA StylePan Fan, Guodong Lang, Pengju Guo, Zhijie Liu, Fuzeng Yang, Bin Yan, Xiaoyan Lei. Multi-Feature Patch-Based Segmentation Technique in the Gray-Centered RGB Color Space for Improved Apple Target Recognition. Agriculture. 2021; 11 (3):273.
Chicago/Turabian StylePan Fan; Guodong Lang; Pengju Guo; Zhijie Liu; Fuzeng Yang; Bin Yan; Xiaoyan Lei. 2021. "Multi-Feature Patch-Based Segmentation Technique in the Gray-Centered RGB Color Space for Improved Apple Target Recognition." Agriculture 11, no. 3: 273.
The construction of a scientific and effective soil pressure-sinkage model under sloped terrain condition has important guiding significance for the investigation of the soil compaction effect. It is also important for the theoretical calculation of driving resistance and design optimization of the undercarriage structure of hillside metal-tracked tractors (HMTs). The classic Bekker’s pressure-sinkage model does not consider the influence of the soil water content, bulk density, slope angle, and other factors; therefore, it cannot be directly used to investigate the relationship between the soil compaction and its sinkage under sloped terrain conditions. To solve this problem, this study first verified that the soil water content and bulk density exert significant effects on the pressure–sinkage relationship under flat terrain condition. Secondly, a pressure-sinkage test was carried out using the quadratic rotation orthogonal combination design method, and the soil water content, density, and slope angle were considered. The pressure-sinkage curves of sloped terrain soils from Yangling and Yangxian in Shaanxi Province, and Huining and Jingning in Gansu Province were obtained. Then the pressure–sinkage parameters (sinkage exponent, cohesive modulus, and frictional modulus) were calculated using the weighted least-squares method. Thirdly, the mathematical relationship between the parameters and the soil water content, bulk density, and slope angle was obtained. Then Bekker’s model was modified to obtain the pressure–sinkage model of sloped terrain. Finally, the control variable method under slope angle of 10°, soil water content of 10%, and bulk density of 2 mg·m−3 were used to validate the model. The results revealed that the root-mean-square error between the calculated pressure value of the model and the measured value of the film pressure sensor was 1.614, 1.601, and 0.822, respectively. In the dynamic operation of a hillside tractor prototype, the calculated pressures between the supporting wheels were close to the measured values. It indicates that the modified soil pressure–sinkage model is more suitable for calculating the force at the bottom of the track between the supporting wheels. It can also provide an important theoretical basis for accurately calculating the pressure–sinkage parameters of sloped terrain soil. Additionally, this approach could provide theoretical and technical support for the rational arrangement of HMT undercarriages to reduce the soil sinkage and driving resistance.
Guanting Pan; Jingbin Sun; Xiaole Wang; Fuzeng Yang; Zhijie Liu. Construction and Experimental Verification of Sloped Terrain Soil Pressure-Sinkage Model. Agriculture 2021, 11, 243 .
AMA StyleGuanting Pan, Jingbin Sun, Xiaole Wang, Fuzeng Yang, Zhijie Liu. Construction and Experimental Verification of Sloped Terrain Soil Pressure-Sinkage Model. Agriculture. 2021; 11 (3):243.
Chicago/Turabian StyleGuanting Pan; Jingbin Sun; Xiaole Wang; Fuzeng Yang; Zhijie Liu. 2021. "Construction and Experimental Verification of Sloped Terrain Soil Pressure-Sinkage Model." Agriculture 11, no. 3: 243.
Multi-robots have shown good application prospects in agricultural production. Studying the synergistic technologies of agricultural multi-robots can not only improve the efficiency of the overall robot system and meet the needs of precision farming but also solve the problems of decreasing effective labor supply and increasing labor costs in agriculture. Therefore, starting from the point of view of an agricultural multiple robot system architectures, this paper reviews the representative research results of five synergistic technologies of agricultural multi-robots in recent years, namely, environment perception, task allocation, path planning, formation control, and communication, and summarizes the technological progress and development characteristics of these five technologies. Finally, because of these development characteristics, it is shown that the trends and research focus for agricultural multi-robots are to optimize the existing technologies and apply them to a variety of agricultural multi-robots, such as building a hybrid architecture of multi-robot systems, SLAM (simultaneous localization and mapping), cooperation learning of robots, hybrid path planning and formation reconstruction. While synergistic technologies of agricultural multi-robots are extremely challenging in production, in combination with previous research results for real agricultural multi-robots and social development demand, we conclude that it is realistic to expect automated multi-robot systems in the future.
Wenju Mao; Zhijie Liu; Heng Liu; Fuzeng Yang; Meirong Wang. Research Progress on Synergistic Technologies of Agricultural Multi-Robots. Applied Sciences 2021, 11, 1448 .
AMA StyleWenju Mao, Zhijie Liu, Heng Liu, Fuzeng Yang, Meirong Wang. Research Progress on Synergistic Technologies of Agricultural Multi-Robots. Applied Sciences. 2021; 11 (4):1448.
Chicago/Turabian StyleWenju Mao; Zhijie Liu; Heng Liu; Fuzeng Yang; Meirong Wang. 2021. "Research Progress on Synergistic Technologies of Agricultural Multi-Robots." Applied Sciences 11, no. 4: 1448.
To address the problems of difficult leveling and poor stability of hill crawler tractors, an attitude adjustment device based on a parallel four-bar mechanism was designed, and the mechanical reasons for the sideslip instability of hill crawler tractors were analyzed. On this basis, a posture adjustment mechanism based on a parallel four-bar mechanism was proposed, and the structure of the complete attitude adjustment device was designed. To ensure that this device meets the strength requirements during operation, a mechanical analysis of the key components (active rocker and slave rocker) was carried out to accommodate the load during leveling. Based on ANSYS software, a finite element simulation analysis was used to determine the maximum stress position of the active and slave rockers. Finally, to verify the accuracy of the above simulation analysis results and determine the influence rules of the lateral slope angle, longitudinal slope angle and loading quality on the abovementioned maximum stress, a physical model test bench of the attitude adjustment device was built. An orthogonal regression experiment was carried out with the maximum stresses of the active and slave rockers as the test indices. The experimental data were analyzed by Design-Expert 10 software, and the results show that the order of the primary and secondary factors influencing the maximum stress of the active rocker was the loading mass, lateral slope angle and longitudinal slope angle. The order of the factors influencing the maximum stress of the slave rocker was the longitudinal slope angle, lateral slope angle and loading mass. The active and slave rockers meet the strength requirements. This work provides technical support for the production of hill crawler tractor physical prototypes.
Jingbin Sun; Chong Meng; Yazhou Zhang; Guoping Chu; Yanjie Zhang; Fuzeng Yang; Zhijie Liu. Design and physical model experiment of an attitude adjustment device for a crawler tractor in hilly and mountainous regions. Information Processing in Agriculture 2020, 7, 466 -478.
AMA StyleJingbin Sun, Chong Meng, Yazhou Zhang, Guoping Chu, Yanjie Zhang, Fuzeng Yang, Zhijie Liu. Design and physical model experiment of an attitude adjustment device for a crawler tractor in hilly and mountainous regions. Information Processing in Agriculture. 2020; 7 (3):466-478.
Chicago/Turabian StyleJingbin Sun; Chong Meng; Yazhou Zhang; Guoping Chu; Yanjie Zhang; Fuzeng Yang; Zhijie Liu. 2020. "Design and physical model experiment of an attitude adjustment device for a crawler tractor in hilly and mountainous regions." Information Processing in Agriculture 7, no. 3: 466-478.