Based on the structural shortage of an agricultural labor force and the continuous increase in tomato planting scale in China, as well as the limitation of a greenhouse working environment on the development of tomato picking productivity, a motor-driven end-effector for a tomato picking robot based on a hybrid force/position control strategy was designed. First, a hybrid force/position control strategy was applied to control the picking process. Consistent with this strategy, the mechanical structure design of the end-effector was determined. The maximum torque of the finger joint motor was verified by applying a load to the end-effector fingertip under a parabolic linear angular velocity. Second, the D-H method was used to establish the end-effector single-finger and whole-hand coordinate systems and to perform forward and inverse kinematic analysis. The Monte Carlo method was used to analyze the workspace of the end-effector, and an isochronous interpolation algorithm was used to analyze the angular displacement of the motor. A basic algorithm for starting, stopping and accelerating the finger joints was designed to achieve the smooth movement of the end-effector. The control system for the end-effector was designed based on STM32F103ZET6, and the end-effector remote wireless debugging system was designed based on Tiny6410. Finally, a test prototype of the tomato picking end-effector was manufactured and picking tests were conducted, which showed that our tomato picking end-effector moved smoothly. The proposed control algorithm reduced the impact force and recorded the contact force between the end-effector and the tomato in real time, and the end-effector essentially achieved nondestructive picking. Therefore, our tomato picking end-effector demonstrated good utility in practice.

A quality detection system for the “Red Fuji” apple in Luochuan was designed for automatic grading. According to the Chinese national standard, the grading principles of apple appearance quality and Brix detection were determined. Based on machine vision and image processing, the classifier models of apple defect, contour, and size were constructed. And then, the grading thresholds were set to detect the defective pixel ratio t, aspect ratio λ, and the cross-sectional diameter W_p in the image of the apple. Spectral information of apples in the wavelength range of 400 nm~1000 nm was collected and the multiple scattering correction (MSC) and standard normal variable (SNV) transformation methods were used to preprocess spectral reflectance data. The competitive adaptive reweighted sampling (CARS) algorithm and the successive projections algorithm (SPA) were used to extract characteristic wavelength points containing Brix information, and the CARS-PLS (partial least squares) algorithm was used to establish a Brix prediction model. Apple defect, contour, size, and Brix were combined as grading indicators. The apple quality online grading detection platform was built, and apple’s comprehensive grading detection algorithm and upper computer software were designed. The experiments showed that the average accuracy of apple defect, contour, and size grading detection was 96.67%, 95.00%, and 94.67% respectively, and the correlation coefficient R_p of the Brix prediction set was 0.9469. The total accuracy of apple defect, contour, size, and Brix grading was 96.67%, indicating that the detection system designed in this paper is feasible to classify “Red Fuji” apple in Luochuan.

Commensal Escherichia coli from the poultries have been considered as reservoirs of extended-spectrum β-lactamases (ESBL)-encoding genes. Between May 2018 and March 2019, a total of 340 E. coli isolates were obtained from apparently healthy broiler chickens from 20 to 40 D old, distributed in 17 small-scale commercial farms. Finally, 45 isolates (8 from 20-day-old broiler chickens, 14 from 30-day-old ones, and 23 from 40-day-old ones) were identified as ESBL producers, which were further investigated to shed light on the virulence gene profiles, phylogenetic groups, and multilocus sequence types and to detect the ESBL plasmid-mediated quinolone resistance determinant (PMQR) genes as well as the mutations in the quinolone resistance-determining regions (QRDR) of gyrA and parC. Molecular analysis showed that phylogenic group A and B1 accounted for 66.7% of the ESBL producers. The overall occurrence of virulence genes ranged from 5.1% (cva) to 86.7% (papC). Twenty (44.4%) ESBL producers were considered as biofilm producers with moderate or heavy biofilm formation. The most predominant specific CTX-M subtype was bla_CTX-M-14 (n = 19), followed by bla_CTX-M-9 (n = 17), bla_CTX-M-55 (n = 9), bla_CTX-M-15 (n = 6), bla_CTX-M-1 (n = 5), and bla_CTX-M-65 (n = 4). Additionally, PMQR genes were identified in 86.7% of ESBL producers, qnrS (n = 21) was the most dominant PMQR gene, followed by the aac(6')-Ib-cr (n = 15), qnrB (n = 12), and qnrA (n = 9), and all of them co-expressed with β-lactamase genes. All PMQR-positive isolates harbored simultaneously at least 1 mutation in the QRDR of gyrA and parC. Forty-five ESBL producers were assigned to 33 sequence types, and the most frequent sequence types (STs) was ST10 (n = 5) and followed by ST95 (n = 3). Additionally, ST302, ST88, ST410, ST187, and ST23 were represented by 2 ESBL producers, respectively, and the remaining ones exhibited diverse ST. Moreover, the prevalence of ESBL producers, the biofilm-forming ability, and the occurrence of the QRDR mutations among the E. coli isolates were characterized by gradually increased with advancing age of broiler chickens.

In this paper, a greenhouse tomato picking robot chassis that meets the path cruising and setpoint positioning requirements of robots engaged in greenhouse tomato picking operations in China is designed. Based on the trellis-cultivation growing environment of tomatoes, the basic parameters of the chassis and operating space are analyzed to determine the chassis requirements during picking operations. According to these requirements, a kinematic model of a robot chassis with front-wheel steering and rear-wheel driving is constructed, and the planar positioning principle of the chassis is introduced. SOLIDWORKS is used to simulate and design three-dimensional models of the chassis parts, and the ANSYS WORKBENCH plug-in is used to simulate and analyze the bearing performance of key chassis components. ADAMS is used to simulate and evaluate the motion trajectory of the chassis, and the reasonableness of parameters such as the chassis size, selected materials, and load-bearing performance are verified. Based on the simulation results, a physical system is constructed to experimentally verify the straight-line motion and steering performance of the chassis. The experimental results show that the chassis has good cruising and positioning accuracy and meets the specific requirements of path cruising and setpoint positioning in greenhouse tomato picking operations.