Kiwifruit is recognized as a favorite fruit crop in China, due to the significant economic and nutritional value. However, the large-scale production has still remained largely on the manual labor, particularly in the key operations, such as the pollination, pruning, thinning, and harvesting. Efficiency and quality are often required in its rapid industrial expansion in recent years, due to the highly labor-intensive and time-sensitive. It is a pressing need for the labor-saving, precise, and reliable alternatives in industry, with the increasing cost of seasonal labor. Among them, the agricultural robots have received growing attention in kiwifruit production. As a representative direction of smart agriculture, the agricultural robots can typically integrate the machine vision, intelligent decision-making, and automated execution. The broad application prospects can also improve the precision for the standardized management of the orchards throughout the year. Multi-degree-of-freedom manipulators, and environment-aware perception modules can also suitable for the complex environment of field orchard. This study aims to systematically review the recent technologies in the kiwifruit robots, particularly on their system structures, perception strategies, motion planning, and end-effector designs. The robot enabling technology was also examined to clarify the current research landscape and development bottlenecks in intelligent horticulture. Special emphasis was placed on the robotic applications during pollination and harvesting. The system performance was evaluated to fully meet the short operation windows and high accuracy requirements. Meanwhile, the intelligent robotic equipment was integrated into the broader context of fully intelligent kiwifruit production (such as bud and young fruit thing, as well as branch pruning). The entire production cycle was involved the sensing, decision-making, execution, and feedback. The selective operation techniques were further enhanced the production efficiency. The targeted interventions were employed to manage only the necessary fruits, flowers, or buds. This selective process was optimized the resource use for the overall productivity without excessive input. Furthermore, a comprehensive framework was also proposed to adapt the intelligent technologies in real-world orchards, including the spatial compatibility between robotic equipment and orchard infrastructure, biological coordination with the phenological development of crops, and the modularity in equipment design to accommodate diverse operational scenarios. The need was highlighted to coordinate the planning in orchard, the standardization in cultivation practices, and equipment innovation. An integrated production was fully met the demands of modern fruit farming. Representative case studies and experimental results were presented to assess the feasibility, efficiency, and practical challenges after robotic deployment in kiwifruit production. Strategic guidance can also provide for the future innovation, standardization, and industrial application, ultimately supporting the transition toward a fully automated, efficient, and sustainable kiwifruit production.

The coefficient of restitution (CoR) is an important parameter for designing vibration-harvesting machinery. There are three main types of fruit-to-fruit collisions during vibration harvesting: collision between fruits collected using a collection device and falling fruits, collision between fruits on branches before being removed, and collision of fruits in the air. The CoR for the first two types of collision was investigated separately using drop and pendulum methods. However, there have been few studies on CoR for the collision of fruits in the air. In this study, a platform was designed to simulate the collision of fruits in the air during vibration harvesting for the ‘Gala’ apple, where influences of collision velocity on CoR were studied. Images from a high-speed camera were processed based on RGB to Lab conversion to extract the bruise surface and calculate the bruise volume. Total bruise volume, the sum of two apple bruise volumes, was calculated and analyzed in relation to the CoR. Results showed that the CoR decreased with collision velocity increasing from 1.0 m/s to 1.4 m/s, where the CoR reached 0.93 or higher when collision velocity was 1.0 m/s, making fruits not bruise, while fruits began to bruise when collision velocity increased from 1.2 m/s. The CoR did not continue to decrease when collision velocity exceeded 1.4 m/s due to rotation. There was little correlation between total bruise volume and the CoR due to the composite motion of fruits in the air, indicating that the CoR may not be an indicator to determine the degree of fruit bruise when the fruit made a composite motion during the collision. Therefore, this research is expected to guide the establishment of a more accurate fruit model to design optimal vibration harvesting machinery.

Recent approaches to the internal quality inspection of apples with the application of hyperspectral imaging technology are highly cost-intensive because of labor involvement for the data collection on a fixed posture and manual selection of the region of interest (RoI). In addition, several studies have repeated the data acquisition for the same apple. Current methods cannot meet the automation requirements of the sorting line. Therefore, this study proposed a novel method for automatically selecting RoI in hyperspectral images of apples with random poses. Firstly, the preliminary RoI selection of apple hyperspectral image was carried out, followed by the performance of histogram statistics of each pixel with spectral intensity at 700 nm wavelength. The top 40% area of the spectral intensity was reserved to obtain the magnitude relationship of the spectral intensity of each pixel point and a morphological erosion operation. Original apple RoI was acquired and overexposed pixels were removed with spectral intensity greater than 3900 (maximum 4095) in the reserved area at 700 nm. Secondly, the relationship between apple size and prediction accuracy was measured for the in-depth RoI analysis. A partial least square regression (PLSR) model was established between the average spectrum and apple sugar content of RoI with different sizes. Finally, the established model with the top 70% of the spectral intensity achieved the best prediction accuracy. Non-destructive estimation of apple sugar content was performed through hyperspectral imaging technology with reference to the proposed RoI selection method. A competitive adaptive reweighted sampling algorithm along the PLSR (CARS-PLSR) model was established after black-and-white correction and standard normal transformation (SNV) preprocessing and obtained the highest prediction accuracy. The determination coefficient of cross-validation (R_cv) and root mean square error of cross-validation (RMSECV) were 0.9595 and 0.3203°Brix, respectively. The determination coefficient of prediction (R_p) was 0.9308, and the root mean square error of prediction (RMSEP) was 0.4681°Brix. Results proved that the auto-selection of RoI is an efficient and accurate method, which can provide a foundation in practical application for online apple grading systems with hyperspectral imaging technology.