A field-based apple detection and grading device was developed and used to detect and grade apples in the field using a deep learning framework. Four features were selected for apple grading, namely, size, color, shape, and surface defects, and detection algorithms were designed to discriminate between the four features using machine vision and other methods. Then, the four apple features were fused, and a support vector machine (SVM) was used for infield apple grading into three grades: first-grade fruit, second-grade fruit, and other-grade fruit. The results showed that for a single index, the accuracy of detecting the apple size, the fruit shape, the color, and the surface defects, were 99.04%, 97.71%, 98%, and 95.85%. The grading accuracies for the first-grade fruit, second-grade fruit, other-grade fruit, and the average grading accuracy based on multiple features were 94.55%, 95.71%, 100%, and 95.49%, respectively. The field experiment showed that the average grading accuracy was 94.12% when the feeding interval of the apples was less than 1.5 s and the walking speed did not exceed 0.5 m/s, meeting the accuracy requirements of field-based apple grading.

With the increase of labor cost and the development of agricultural mechanization, standardized orchards suitable for autonomous operations of agricultural machinery will be a future development trend of the fruit-planting industry. For field-planting processes of standardized orchards, autonomous navigation of orchard vehicles in complex environments is the foundation of mechanized and intelligent field operations. In order to realize autonomous driving and path-tracking of vehicles in complex standardized orchards that involve much noise and interference between rows of fruit trees, an automatic navigation system was designed for orchard vehicles, based on 2D lasers. First, considering the agronomic requirements for orchard planting such as plant spacing, row spacing and trunk diameter, different filtering thresholds were established to eliminate discrete points of 2D laser point cloud data effectively. Euclidean clustering algorithm and the important geometric theorems of three points collinearity was used to extract the central feature points of the trunk, as the same time, navigation path was fitted based on the least square method. Secondly, an automatic navigation control algorithm was designed, and the fuzzy control was used to realize the dynamic adjustment of the apparent distance of the pure pursuit model. Finally, the reliability of the proposed approach was verified by simulation using MATLAB/Simulink, and field tests were carried out based on electric agricultural vehicle. Experimental results show that the method proposed in this study can effectively improve the precision of automatic navigation in complex orchard environment and realize the autonomous operation of orchard vehicles.

Mikania micrantha Kunth is an invasive alien weed and known as a plant killer around the world. Accurately and rapidly identifying M. micrantha in the wild is important for monitoring its growth status, as this helps management officials to take the necessary steps to devise a comprehensive strategy to control the invasive weed in the identified area. However, this approach still mainly depends on satellite remote sensing and manual inspection. The cost is high and the accuracy rate and efficiency are low. We acquired color images of the monitoring area in the wild environment using an Unmanned Aerial Vehicle (UAV) and proposed a novel network —MmNet— based on a deep Convolutional Neural Network (CNN) to identify M. micrantha in the images. The network consists of AlexNet Local Response Normalization (LRN), along with the GoogLeNet and continuous convolution of VGG inception models. After training and testing, the identification of 400 testing samples by MmNet is very good, with accuracy of 94.50% and time cost of 10.369 s. Moreover, in quantitative comparative analysis, the proposed MmNet not only has high accuracy and efficiency but also simple construction and outstanding repeatability. Compared with recently popular CNNs, MmNet is more suitable for the identification of M. micrantha in the wild. However, to meet the challenge of wild environments, more M. micrantha images need to be acquired for MmNet training. In addition, the classification labels need to be sorted in more detail. Altogether, this research provides some theoretical and scientific basis for the development of intelligent monitoring and early warning systems for M. micrantha and other invasive species.

The application method of granular fertilizers and wheat seeds depends on the colter design and parameters. In this research, a new double disc colter is studied to apply the wheat seeds to the horizontal band 12 cm in width and apply granular fertilizers deeper 2 cm than the wheat seed level precisely to the middle of the band. Applying granular fertilizers and wheat seeds at different levels increases the granular fertilizer dose without harm to the wheat seeds. Furthermore, applying high doses of wheat seeds to the horizontal band decreases the competition between the seeds and suppresses the weeds. Therefore, preparing a plain seedbed after applying the fertilizers and distributing wheat seeds to the horizontal band was the research objective. The comparison experiments of the base and designed double disc colters were provided in the soil bin determining the horizontal and vertical forces and the placement of the fertilizers and seeds. The discrete element method (DEM) was used to track the soil particle behavior interacting with the double-disc colter. The simulation results and actual experiment results were satisfactory when the AB length of the wing orifice was 60 mm.

A large high-clearance self-propelled sprayer chassis was designed according to the requirements of the sprayer’s driving operation and the overall goal. The structural characteristics and working principle of the chassis drive system, suspension system, track adjustment system, and frame were analyzed. A finite element analysis of the frame was carried out under four extreme conditions: full load bending, full load torsion, full load emergency braking and full load emergency turning. Under these four conditions, the maximum stress values of the frame were 149.45 MPa, 219 MPa, 151.44 MPa, and 123.27 MPa respectively, and the maximum strain values were 1.12 mm, 2.22 mm, 0.95 mm, and 1.16 mm respectively. A theoretical analysis of the stability, steering ability, and obstacle navigation of the sprayer chassis was conducted. The results showed that the upper angle θlim, lower angle θ׳lim and lateral limit tumbling angle φlim of the sprayer chassis were 50.1°, 30.0° and 35.3° respectively. The minimum turning radius of the chassis was 5816 mm, and the minimum turning width was 4113 mm. The maximum obstacle crossing heights of the front and rear wheels were 466 mm and 457 mm. The theoretical analysis showed that the designed chassis met the field management operations of large plots. A field experiment of the high-clearance self-propelled sprayer chassis at full load was conducted. The experimental results were consistent with the theoretical analysis results. The sprayer chassis not only good handling stability but also good ride comfort. The results of this study provide references for the design of high-clearance self-propelled sprayer chassis. Keywords: high-clearance self-propelled sprayer, chassis performance, frame, suspension, design, experiment DOI: 10.25165/j.ijabe.20201302.5262 Citation: Chen Y, Mao E R, Li W, Zhang S, Song Z H, Yang S J, et al. Design and experiment of a high-clearance self-propelled sprayer chassis. Int J Agric & Biol Eng, 2020; 13(2): 71–80.

The interaction properties of the seeds have to be calibrated to simulate the realistic behavior of the seed bulk. Here, a simple and accurate calibration method of DEM interaction properties of seeds with adequate equipment to simulate each seed’s behavior remains a challenge. In this research, the rotary drum is chosen as simple equipment to calibrate particle–particle and particle–material interaction properties, as there is a lack of research on whether the rotary drum is adequate equipment to calibrate particle–material interaction properties. Therefore, this article calibrates particle–particle and particle–material static and rolling interaction coefficients using a rotary drum. The calibration of particle–material static and rolling friction coefficients are described using the rotating drum with a 45 degrees inclination. The particle–particle static and rolling friction coefficients were calibrated according to the angle of repose when the rotary drum is vertical.