Improvements to and large‐scale evaluation of a robotic kiwifruit harvester

AcNAC10, regulated by AcTGA07, enhances kiwifruit resistance to Pseudomonas syringae pv. actinidiae via inhibiting jasmonic acid pathway

Kiwifruit bacterial canker is a devastating disease caused by Pseudomonas syringae pv. actinidiae (Psa). NAC transcription factors play a significant role in host immunity. However, the potential molecular mechanism of resistance to semi-biotrophic Psa mediated by NAC transcription factors in kiwifruit remains unclear. In this study, we identified a typical NAC transcription factor, AcNAC10, which is involved in the jasmonic acid (JA) pathway and is highly expressed in resistant variety RH12 responsing to Psa. By overexpression and silencing of AcNAC10 in kiwifruit, it plays a positive role in enhancing kiwifruit resistance. Likewise, heterologous expression of AcNAC10 in transgenic Arabidopsis and tomato enhanced resistance to P. syringae. By directly binding to the promoter of AcLOX3, AcNAC10 inhibited its expression as a transcriptional suppressor. Using a yeast one-hybrid screening library, electrophoretic mobility shift assay (EMSA), and dual-luciferase reporter assays, it showed that AcTGA07 can activate the expression of AcNAC10. Moreover, we demonstrated that AcTGA07 decreased JA accumulation independently of the AcNAC10-AcLOX3 pathway. Our study elucidated the transcriptional cascade regulatory network of AcTGA07-AcNAC10-AcLOX3, which enhanced the disease resistance of kiwifruit to Psa by inhibiting JA synthesis.

Design and experimentation of multi-fruit envelope-cutting kiwifruit picking robot

Currently kiwifruit picking process mainly leverages manual labor, which has low productivity and high labor intensity, meanwhile, the existing kiwifruit picking machinery also has low picking efficiency and easily damages fruits. In this regard, a kiwifruit picking robot suitable for orchard operations was developed in this paper for kiwifruit grown in orchard trellis style. First, based on the analysis of kiwifruit growth pattern and cultivation parameters, the expected design requirements and objectives of a kiwifruit picking robot were proposed, and the expected workflow of the robot in the kiwifruit orchard environment was given, which in turn led to a multi-fruit envelope-cutting kiwifruit picking robot was designed. Then, the D-H method was used to establish the kinematic Equations of the kiwifruit-picking robot, the forward and inverse kinematic calculations were carried out, and the Monte Carlo method was used to analyze the workspace of the robot. By planning the trajectory of the robotic arm and calculating critical nodes in the picking path, the scheme of trajectory planning of the robot was given, and MATLAB software was applied to simulate the motion trajectory as well as to verify the feasibility of the trajectory planning scheme and the picking strategy. Finally, a kiwifruit picking test bed was set up to conduct picking tests in the form of fruit clusters. The results show that the average time to pick each cluster of fruit was 9.7s, the picking success rate was 88.0%, and the picking damage rate was 7.3%. All the indicators met the requirements of the expected design of the kiwifruit-picking robot.

Using deep learning for pruning region detection and plant organ segmentation in dormant spur-pruned grapevines

Even though mechanization has dramatically decreased labor requirements, vineyard management costs are still affected by selective operations such as winter pruning. Robotic solutions are becoming more common in agriculture, however, few studies have focused on grapevines. This work aims at fine-tuning and testing two different deep neural networks for: (i) detecting pruning regions (PRs), and (ii) performing organ segmentation of spur-pruned dormant grapevines. The Faster R-CNN network was fine-tuned using 1215 RGB images collected in different vineyards and annotated through bounding boxes. The network was tested on 232 RGB images, PRs were categorized by wood type (W), orientation (Or) and visibility (V), and performance metrics were calculated. PR detection was dramatically affected by visibility. Highest detection was associated with visible intermediate complex spurs in Merlot (0.97), while most represented coplanar simple spurs allowed a 74% detection rate. The Mask R-CNN network was trained for grapevine organs (GOs) segmentation by using 119 RGB images annotated by distinguishing 5 classes (cordon, arm, spur, cane and node). The network was tested on 60 RGB images of light pruned (LP), shoot-thinned (ST) and unthinned control (C) grapevines. Nodes were the best segmented GOs (0.88) and general recall was higher for ST (0.85) compared to C (0.80) confirming the role of canopy management in improving performances of hi-tech solutions based on artificial intelligence. The two fine-tuned and tested networks are part of a larger control framework that is under development for autonomous winter pruning of grapevines.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11119-023-10006-y.

Integration of stereo vision system calibration and kinematic calibration for an autonomous kiwifruit harvesting system

Stereo vision system and manipulator are two major components of an autonomous fruit harvesting system. In order to raise the fruit-harvesting rate, stereo vision system calibration and kinematic calibration are two significant processes to improve the positional accuracy of the system. This article reviews the mathematics of these two calibration processes and presents an integrated approach for acquiring calibration data and calibrating both components of an autonomous kiwifruit harvesting system. The calibrated harvesting system yields good positional accuracy in the laboratory tests, especially in harvesting individual kiwifruit. However, the performance is not in line with the outcomes in the orchard field tests due to the cluster growing style of kiwifruit. In the orchard test, the calibrations reduce the fruit drop rate but it does not impressively raise the fruit harvesting rate. Most of the fruit in the clusters remain in the canopy due to the invisibility of the stereo vision system. After analyzing the existing stereo vision system, a future visual sensing system research direction for an autonomous fruit harvesting system is justified.

A review on structural development and recognition–localization methods for end-effector of fruit–vegetable picking robots

The excellent performance of fruit and vegetable picking robots is usually contributed by the reasonable structure of end-effector and recognition–localization methods with high accuracy. As a result, efforts are focused on two aspects, and diverse structures of end-effector, target recognition methods as well as their combinations are yielded continuously. A good understanding for the working principle, advantages, limitations, and the adaptability in respective fields is helpful to design picking robots. Therefore, depending on different grasping ways, separating methods, structures, materials, and driving modes, main characteristics existing in traditional schemes will be depicted firstly. According to technical routes, advantages, potential applications, and challenges, underactuated manipulators and soft manipulators representing future development are then summarized systematically. Secondly, partial recognition and localization methods are also demonstrated. Specifically, current recognition manners adopting the single-feature, multi-feature fusion and deep learning are explained in view of their advantages, limitations, and successful instances. In the field of 3D localization, active vision based on the structured light, laser scanning, time of flight, and radar is reflected through the respective applications. Also, another 3D localization method called passive vision is also evaluated by advantages, limitations, the degree of automation, reconstruction effects, and the application scenario, such as monocular vision, binocular vision, and multiocular vision. Finally portrayed from structural development, recognition, and localization methods, it is possible to develop future end-effectors for fruit and vegetable picking robots with superior characteristics containing the less driving element, rigid–flexible–bionic coupling soft manipulators, simple control program, high efficiency, low damage, low cost, high versatility, and high recognition accuracy in all-season picking tasks.

Role of the Type VI Secretion System in the Pathogenicity of Pseudomonas syringae pv. actinidiae, the Causative Agent of Kiwifruit Bacterial Canker

The type VI secretion system (T6SS), a macromolecular machine, plays an important role in the pathogenicity of many Gram-negative bacteria. However, the role of T6SS in the pathogenicity of Pseudomonas syringae pv. actinidiae (Psa), the pathogen of kiwifruit bacterial canker, is yet to be studied. Here, we found a T6SS gene cluster consisting of 13 core genes (A-J) in the genome of Psa M228 based on a genome-wide analysis. To determine whether the T6SS gene cluster affects the pathogenicity of Psa M228, T6SS and its 13 core gene deletion mutants were constructed and their pathogenicity was determined. The deletion mutants showed different degrees of reduction in pathogenicity compared with the wild-type strain M228; in tssM and tssJ mutants, pathogenicity was significantly reduced by 78.7 and 71.3%, respectively. The pathogenicity results were also confirmed by electron microscopy. To further confirm that the reduction in pathogenicity is related to the function of T6SS, we selected the T6SS gene cluster, comprising tssM and tssJ, for further analyses. Western blot results revealed that tssM and tssJ were necessary for hemolytic co-regulatory protein secretion, indicating that they encode a functional T6SS. Further, we explored the mechanism by which T6SS affects the pathogenicity of Psa M228. The ability of bacterial competition, biofilm formation, hydrogen peroxide tolerance, and proteolytic activity were all weakened in the deletion mutants M228ΔT6SS, M228ΔtssM, and M228ΔtssJ. All these properties of the two gene complementation mutants were restored to the same levels as those of the wild-type strain, M228. Quantitative real-time results showed that during the interaction between the deletion mutant M228ΔT6SS and the host, expression levels of T3SS transcriptional regulatory gene hrpR, structural genes hrpZ, hrcC, hopP1, and effector genes hopH1 and hopM1 were down-regulated at different levels. Taken together, our data provide evidence for the first time that the T6SS plays an important role in the pathogenicity of Psa, probably via effects on bacterial competition, biofilm formation, and environmental adaptability. Moreover, a complicated relationship exists between T6SS and T3SS.