Continuing progress in machine learning (ML) has led to significant advancements in agricultural tasks. Due to its strong ability to extract high-dimensional features from fruit images, deep learning (DL) is widely used in fruit detection and automatic harvesting. Convolutional neural networks (CNN) in particular have demonstrated the ability to attain accuracy and speed levels comparable to those of humans in some fruit detection and automatic harvesting fields. This paper presents a comprehensive overview and review of fruit detection and recognition based on DL for automatic harvesting from 2018 up to now. We focus on the current challenges affecting fruit detection performance for automatic harvesting: the scarcity of high-quality fruit datasets, fruit detection of small targets, fruit detection in occluded and dense scenarios, fruit detection of multiple scales and multiple species, and lightweight fruit detection models. In response to these challenges, we propose feasible solutions and prospective future development trends. Future research should prioritize addressing these current challenges and improving the accuracy, speed, robustness, and generalization of fruit vision detection systems, while reducing the overall complexity and cost. This paper hopes to provide a reference for follow-up research in the field of fruit detection and recognition based on DL for automatic harvesting.

The accuracy, speed, and robustness of object detection and recognition are directly related to the harvesting efficiency, quality, and speed of fruit and vegetable harvesting robots. In order to explore the development status of object detection and recognition techniques for fruit and vegetable harvesting robots based on digital image processing and traditional machine learning, this article summarizes and analyzes some representative methods. This article also demonstrates the current challenges and future potential developments. This work aims to provide a reference for future research on object detection and recognition techniques for fruit and vegetable harvesting robots based on digital image processing and traditional machine learning.

In order to achieve accurate, fast, and robust recognition of blueberry fruit maturity stages for edge devices such as orchard inspection robots, this research proposes a lightweight detection method based on an improved YOLOv5 algorithm. In the improved YOLOv5 algorithm, the ShuffleNet module is used to achieve lightweight deep-convolutional neural networks. The Convolutional Block Attention Module (CBAM) is also used to enhance the feature fusion capability of lightweight deep-convolutional neural networks. The effectiveness of this method is evaluated using the blueberry fruit dataset. The experimental results demonstrate that this method can effectively detect blueberry fruits and recognize their maturity stages in orchard environments. The average recall (R) of the detection is 92.0%. The mean average precision (mAP) of the detection at a threshold of 0.5 is 91.5%. The average speed of the detection is 67.1 frames per second (fps). Compared to other detection algorithms, such as YOLOv5, SSD, and Faster R-CNN, this method has a smaller model size, smaller network parameters, lower memory usage, lower computation usage, and faster detection speed while maintaining high detection performance. It is more suitable for migration and deployment on edge devices. This research can serve as a reference for the development of fruit detection systems for intelligent orchard devices.

A wildfire occurred in Shawnee State Forest located in southern Ohio that consumed 1215 hectares. Based on earlier forest inventories it was known that paulownia (Paulownia tomentosa), a non-native invasive tree species, occurred in the forest. The objective of this study was to determine if paulownia heavily colonized areas two years after the fire where the burn occurred, and if its presence had a negative impact on the regeneration (<137 cm height) of native species—red and white oaks (Quercus sp.), red maple (Acer rubrum), and yellow-poplar (Liriodendron tulipifera). Two years after the fire, paulownia had invaded the burned areas but not at significantly higher densities than occurred in the unburned areas. Fire significantly reduced the number of regenerating stems of white oak and red maple two years after the fire, whereas the number of regenerating stems of red oak increased slightly and that of yellow-poplar increased significantly. In areas where paulownia occurred that experienced wildfire, all species studied displayed a reduction in the number of regenerating stems compared to paulownia’s absence in the burn areas. Where paulownia occurred in areas not affected by the wildfire, all the native species studied displayed a reduction in the number of regenerating stems. The average heights of red oak, white oak, and red maple were significantly taller when growing in areas affected by the wildfire due to a more open canopy. However, there was no significant change in the average heights of yellow-poplar. The presence of paulownia in both the burned and unburned areas reduced the number of regenerating stems of the native species studied.

Since China possesses the vast territory and a large blueberry planting area, blueberry harvesting with hand is laborious and time consuming. As the blueberry planting agronomy in China is different from other countries, it is pretty significant to develop blueberry harvesting machinery applicable to the domestic planting agronomy to achieve mechanized harvesting of blueberries. In blueberry harvesting operation, the harvesting system as the core component of the machine is the key technology of the harvesting machinery. The previous study found that: in terms of harvesting machinery, most of the literature has studied the dynamic characteristics, while few articles have been published to study the transmission clearance. But the clearance collision force generated by the transmission clearance of the harvesting system directly affected the output load moment and harvesting force of the machine developed by acting on the plant, and then affected the picking efficiency and the quality of picked fruit. Therefore, this paper focuses on the study of the transmission clearance of the blueberry harvesting machine. Firstly, the MLSD modeling method was applied to establish the transmission clearance model of the harvesting device, and the corresponding mechanical analysis of the transmission clearance was carried out. Secondly, after programming in MATLAB software and simulation in ADAMS software, the correctness of the transmission clearance model of the harvesting device was verified in different environments. The multi-body dynamics analysis software ADAMS was used for building the transmission clearance model of the harvesting device and perform mechanical simulation to analyze the clearance collision force and the output load moment of the harvesting device. Pro/E, ADAMS and ANSYS software were integrated to establish the flexible body of blueberry plant. Then the flexible body was combined with the mechanical model of harvesting device for rigid-flexible coupling simulation analysis to study the fruit harvesting force under different transmission clearances. Finally, the orthogonal method was used to conduct field test on blueberry harvesting to study the influence of the transmission clearance on the quality of picked fruit and picking efficiency of the machinery. Therefore, the best combination of the transmission clearance of the harvester was obtained as follows: cam clearance (the clearance joints of cam) was 0.25 mm, the slider clearance was 0.2 mm, left connecting rod clearance pair was 0.1 mm, right connecting rod clearance pair was 0.2 mm; the field picking test was conducted to obtain the machine's picking efficiency was 3.93 kg min⁻¹, the shedding rate of unripe fruit was 3.1 %, and the damage rate of picked fruit was 2.8 %. The research findings of this paper can offer referenced basis and theoretical support for berry harvesting machinery, and also can provide guidance for the design and improvement of other agricultural and forestry harvesting machinery.

China possesses a vast territory, and the manual harvesting of blueberries is time-consuming and labor-intensive. Due to the planting agronomy differences in other countries, China needs to develop a domestic blueberry harvester to realize mechanical blueberry harvesting. In the harvesting process, “collision” is the core problem. Most of the literature has studied rigid body–rigid body collision, while few authors have studied rigid–flexible coupling collision mechanisms in the field of berry harvesting. In this paper, a rigid–flexible coupling collision model between the harvester and the blueberry plant was established based on the L-N nonlinear spring damping model (describing the collision force model between two colliding objects, consisting of the nonlinear spring and the damper) and improved the Coulomb model (the tangential collision force model), and the collision mechanism of blueberry harvesting was analyzed. The harvesting collision process was analyzed using both MATLAB and ADAMS software and the same conclusions were obtained: the collision force and fruit harvesting force were inversely proportional to the machine velocity but positively proportional to the rotational velocity of the hydraulic motor of the harvesting device. The following machine parameters were required to meet harvesting conditions: a harvesting device output rotational velocity of 120–150 r/min and a machine velocity of 40–50 m/min. A harvesting field test using a self-propelled blueberry harvester was conducted, which showed that the test results were consistent with the software simulation conclusions. When the machine velocity of the harvester and the output rotational velocity of the hydraulic motor were 45 m/min and 130 r/min, respectively, the machine provided optimum harvesting efficiency and fruit quality with the following optimum parameters: a harvesting efficiency of 5.1 kg/min, a raw fruit harvesting rate of 2.9%, and a damaged fruit harvesting rate of 3.6%. This research can lay the preliminary theoretical foundation for the analysis of a blueberry harvesting mechanism, and the research results can provide a theoretical reference for the harvesting of other similar berry shrubs.