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.

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Fruit Detection and Recognition Based on Deep Learning for Automatic Harvesting: An Overview and Review

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 Lightweight Detection Method for Blueberry Fruit Maturity Based on an Improved YOLOv5 Algorithm

This research presents a groundbreaking approach in aerial image analysis by integrating the Real-Time Detection and Recognition (RT-DETR-X) model with the Slicing Aided Hyper Inference (SAHI) methodology, utilizing the VisDrone-DET dataset. Aimed at enhancing the efficiency of drone technology across a spectrum of applications, including water conservancy, geological exploration, and military operations, this study focuses on harnessing the real-time, end-to-end object detection capabilities of RT-DETR-X. Characterized by its high-speed and high-accuracy performance, particularly in UAV aerial photography, RT-DETR-X demonstrates a remarkable 54.8% Average Precision (AP) and 74 frames per second (FPS), surpassing similar models in both speed and accuracy. The research thoroughly examines the VisDrone-DET dataset, which encompasses a diverse range of small targets in UAV aerial photography scenes. Covering 10 distinct categories, the dataset provides a robust platform for rigorous model testing. The study emphasizes the utilization of the original image dataset for comprehensive training and evaluation, alongside the practical implementation of the SAHI method for enhanced detection of small-scale objects. Through an in-depth exploration of the model’s performance in various scenarios and a detailed analysis of the environmental setup, this paper underscores the impact of integrating RT-DETR with the SAHI approach. The findings reveal significant progress in drone detection technologies, offering a holistic framework for effective and efficient aerial surveillance. The integration not only boosts the model’s detection accuracy but also opens new avenues for advanced image analysis in UAV applications.

Enhancing UAV Aerial Image Analysis: Integrating Advanced SAHI Techniques With Real-Time Detection Models on the VisDrone Dataset

Abstract Real-time control systems(RTCS) have become an indispensable part of modern industry, finding widespread applications in fields such as robotics, intelligent manufacturing and transportation. However, these systems face significant challenges, including complex nonlinear dynamics, uncertainties and various constraints. These challenges result in weakened disturbance rejection and reduced adaptability, which make it difficult to meet increasingly stringent performance requirements. In fact, RTCS generate a large amount of data, which presents an important opportunity to enhance control effectiveness. Machine learning, with its efficiency in extracting valuable information from big data, holds significant potential for applications in RTCS. Exploring the applications of machine learning in RTCS is of great importance for guiding scientific research and industrial production. This paper first analyzes the challenges currently faced by RTCS, elucidating the motivation for integrating machine learning into these systems. Subsequently, it discusses the applications of machine learning in RTCS from various aspects, including system identification, controller design and optimization, fault diagnosis and tolerance, and perception. The research indicates that data-driven machine learning methods exhibit significant advantages in addressing the multivariable coupling characteristics of complex nonlinear systems, as well as the uncertainties arising from environmental disturbances and faults, thereby effectively enhancing the system's flexibility and robustness. However, compared to traditional methods, the applications of machine learning also faces issues such as poor model interpretability, high computational requirements leading to insufficient real-time performance, and a strong dependency on high-quality data. This paper discusses these challenges and proposes potential future research directions. 

Applications of Machine Learning in Real-Time Control Systems: A Review

In the process of mechanized production of mulberry, falling collision damage is one of the main damage forms, which causes serious damage and accelerates fruit decay. Through the drop collision test and simulation analysis of mulberry fruit, the relationship between drop conditions and fruit damage degree was established, so as to provide reference for reducing fruit falling damage. The drop test is carried out. The relationship between factors and indicators is established. The drop height, fruit plate angle, fruit plate material, and drop direction were taken as factors, and then the energy loss was selected as indicator. The drop process of mulberry fruit is analyzed by numerical simulation, and the maximum stress and deformation of mulberry fruit are analyzed. With the increase of the drop height, the kinetic energy of the fruit increases. When the fruit plate material is pearl cotton, the drop height changes from 200 to 1000 mm, the energy loss of fruit falling increases from 4.42 × 10−0.3 J to 2.14 × 10−0.2 J. When the drop height of fruit is 200 mm, the angle of fruit plate is 30°, and the material of fruit plate is pearl cotton, the minimum energy loss of fruit is 3.39 × 10−0.3 J, the fruit damage was the least. This study can provide a reference to reduce the damage of mulberry fruit in the process of mechanical harvest and transportation. Practical Applications The existing mulberry harvesting methods are manual harvesting, while the vibration harvesting methods have high harvesting efficiency and serious damage. When the harvesting parts hit the tree trunk, the mulberry fruit will leave the tree and falls onto the fruit-receiving device in the process of mechanical harvesting. There will be a collision and falling of the mulberry, which may cause serious damage. Therefore, in the process of mulberry fruit harvest, transportation, the falling, and collision damage is the main damage form. At present, there is few research on the drop impact damage test of mulberry fruit. In this study, the effects of mulberry falling direction, fruit plate material and drop height on mulberry damage were discussed by drop experiment. Then, the optimization results were carried out. The change and distribution of stress and strain in the process of collision were analyzed by numerical simulation, and the relationship between falling conditions and damage level was established. It is important to analyze the dynamic characteristics of mulberry in the process of drop. This study can be applied as a reference to reduce mulberry fruit impact damage, and provide a theoretical reference for the design of mulberry vibration harvesting machinery. 

Analysis of damage caused by drop impacts for mulberry fruits: An dropping experiment and numerical simulation

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.

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Object Detection and Recognition Techniques Based on Digital Image Processing and Traditional Machine Learning for Fruit and Vegetable Harvesting Robots: An Overview and Review

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.

/pdf/analysis-and-testing-of-rigid-flexible-coupling-collision-2q7f79s5.pdf

Analysis and Testing of Rigid–Flexible Coupling Collision Harvesting Processes in Blueberry Plants

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Object Detection and Recognition Techniques Based on Digital Image Processing and Traditional Machine Learning for Fruit and Vegetable Harvesting Robots: An Overview and Review

Analysis and Testing of Rigid–Flexible Coupling Collision Harvesting Processes in Blueberry Plants