In the era of smart agriculture, intelligent fruit maturity detection has become a critical task. However, in complex orchard environments, factors such as occlusion by branches and leaves and interference from bagging materials pose significant chal...
To address the issues of low detection accuracy and poor adaptability in complex orchard environments (such as varying lighting conditions, branch and leaf occlusion, fruit overlap, and small targets), this paper proposes an improved pear detection m...
Accurately recognizing apples in complex environments is essential for automating apple picking operations, particularly under challenging natural conditions such as cloudy, snowy, foggy, and rainy weather, as well as low-light situations. To overcom...
To address the challenges of leaf–branch occlusion, fruit mutual occlusion, complex background interference, and scale variations in peach detection within complex orchard environments, this study proposes an improved YOLOv11n-based peach detec...
The global agriculture industry is encountering challenges due to labor shortages and the demand for increased efficiency. Currently, fruit yield estimation in guava orchards primarily depends on manual counting. Machine vision is an essential techno...
To address the challenges of poor model adaptability and high computational complexity in complex orchard environments characterized by variable lighting, severe occlusion, and dense fruit clusters, an enhanced kiwifruit detection network, Kiwi-YOLO,...
The rapid development of artificial intelligence and remote sensing technologies is indispensable for modern agriculture. In orchard environments, challenges such as varying light conditions and shading complicate the tasks of intelligent picking rob...
The intelligent detection of young peaches is the main technology of fruit-thinning robots, which is crucial for enhancing peach fruit quality and reducing labor costs. This study presents the lightweight YOLO-PEM model based on YOLOv8s to achieve hi...
Apples are cultivated over a large global area with high yields, and efficient robotic harvesting requires accurate detection and localization, particularly in complex orchard environments where occlusion by leaves and fruits poses substantial challe...
Accurate and efficient detection of red fruits in complex orchard environments is crucial for the autonomous operation of agricultural harvesting robots. However, existing methods still face challenges such as high false negative rates, poor localiza...
This study addresses the problem of detecting occluded apples in complex unstructured environments in orchards and proposes an apple detection and segmentation model based on improved YOLOv8n-SGW-YOLOv8n. The model improves apple detection and segmen...
To solve the problem of cumulative errors when robots build maps in complex orchard environments due to their large scene size, similar features, and unstable motion, this study proposes a loopback registration algorithm based on the fusion of Faster...
Mandarin fruit detection provides crucial technical support for yield prediction and the precise identification and harvesting of mandarin fruits. However, challenges such as occlusion from leaves or branches, the presence of small or partially visib...
Leaves are the key organs in photosynthesis and nutrient production, and leaf counting is an important indicator of banana plant health and growth rate. However, in complex orchard environments, leaves often overlap, the background is cluttered, and...
Highly effective pesticide applications require a continual adjustment of the pesticide spray flow rate that attends to different canopy characterizations. Real-time image processing with rapid target detection and data-processing technologies is vit...
Accurate object detection is a fundamental component of autonomous apple-picking systems. In response to the insufficient recognition performance and poor generalization capacity of existing detection algorithms under unstructured orchard scenarios,...
The pollination of kiwiberry flowers is closely related to fruit growth, development, and yield. Rapid and precise identification of flowers under natural field conditions plays a key role in enhancing pollination efficiency and improving overall fru...
Accurate detection of tea shoots in natural environments is crucial for facilitating intelligent tea picking, field management, and automated harvesting. However, the detection performance of existing methods in complex scenes remains limited due to...
In the complex environment of orchards, in view of low fruit recognition accuracy, poor real-time and robustness of traditional recognition algorithms, this paper propose an improved fruit recognition algorithm based on deep learning. Firstly, the re...
Orchard mobile robots (OMR) represent a critical research focus in the field of modern intelligent agricultural equipment, offering the potential to significantly enhance operational efficiency through the integration of path planning and tracking co...
With the increase of labor cost and the development of agricultural mechanization, standardized orchards suitable for autonomous operations of agricultural machinery will be a future development trend of the fruit-planting industry. For field-plantin...
Agricultural robots play a crucial role in ensuring the sustainability of agriculture. Fruit detection is an essential part of orange-harvesting robot design. Ripe oranges need to be detected accurately in an orchard so they can be successfully picke...
To guide grape picking robots to recognize and classify the grapes with different maturity quickly and accurately in the complex environment of the orchard, and to obtain the spatial position information of the grape clusters, an algorithm of grape m...
Due to the complex spatial structure, dense tree distribution, and narrow passages in orchard environments, traditional path planning algorithms often struggle with large path deviations, frequent turning, and reduced navigational safety. In order to...
With the development of agriculture, the complexity and dynamism of orchard environments pose challenges to the perception and positioning of inter-row environments for agricultural vehicles. This paper proposes a method for extracting navigation lin...
Visual harvest maturity is a key visual phenotype for orchard management and harvesting decisions, yet chestnut fruits in natural orchards often exhibit weak color contrast, subtle texture variation, blurred boundaries, and frequent occlusion under c...
Accurate citrus disease identification is essential for targeted orchard pesticide application. Current models struggle with accuracy and efficiency due to diverse leaf lesion patterns and complex orchard environments. This study presents YOLOv8n-DE,...
Dense foliage, severe illumination variations, and interference from multiple individuals with similar appearances in complex orchard environments pose significant challenges for vision-based following robots in maintaining persistent target percepti...
To address the insufficient accuracy of traditional single-sensor navigation methods in dense planting environments of pomegranate orchards, this paper proposes a vision and LiDAR fusion-based navigation line extraction method for orchard environment...
Community orchards could play a valuable role as nature-based solutions to complex challenges we face today. In these unique plantings, a variety of nut- and fruit-producing trees and berry shrubs are often established together on public spaces to pr...
Apple detection in natural environments is crucial for advancing agricultural automation. However, orchards often employ bagging techniques to protect apples from pests and improve quality, which introduces significant detection challenges due to the...
With the decreasing and aging agricultural workforce, fruit harvesting robots equipped with higher degrees of freedom (DoF) manipulators are seen as a promising solution for performing harvesting operations in unstructured and complex orchard environ...
As one of the world’s economically valuable fruit crops, citrus has its quality and productivity closely tied to the degree of fruit ripeness. However, accurately and efficiently detecting citrus ripeness in complex orchard environments for sel...
Accurate apple yield estimation is essential for effective orchard management, market planning, and ensuring growers’ income. However, complex orchard conditions, such as dense foliage occlusion and overlapping fruits, present challenges to lar...
In complex orchard environments, rapidly and accurately identifying pomegranate fruits at various growth stages remains a significant challenge. Therefore, we propose YOLO-MSNet, a lightweight and enhanced pomegranate fruit detection model developed...
The detection of apple yield in complex orchards plays an important role in smart agriculture. Due to the large number of fruit trees in the orchard, improving the speed of apple detection has become one of the challenges of apple yield detection. Ad...
Autonomous navigation in agricultural environments requires precise obstacle classification to ensure collision-free movement. This study proposes a convolutional neural network (CNN)-based model designed to enhance obstacle classification for agricu...
An accurate and effective peach recognition algorithm is a key part of automated picking in orchards; however, the current peach recognition algorithms are mainly targeted at bare fruit scenarios and face challenges in recognizing flat peach bagged f...
Recognition of the orchard environment is a prerequisite for realizing the autonomous operation of intelligent horticultural tractors. Due to the complexity of the environment and the algorithm’s dependence on ambient light, the traditional rec...
Agricultural robotics is a complex, challenging, and exciting research topic nowadays. However, orchard environments present harsh conditions for robotics operability, such as terrain irregularities, illumination, and inaccuracies in GPS signals. To...
In order to shorten detection times and improve average precision in embedded devices, a lightweight and high-accuracy model is proposed to detect passion fruit in complex environments (e.g., with backlighting, occlusion, overlap, sun, cloud, or rain...
Path planning is a fundamental challenge for autonomous robots, particularly in unstructured environments, where issues such as low search efficiency, suboptimal path quality, and local optima often arise. To address these challenges and enable a non...
The application of autonomous navigation in intelligent agriculture is becoming more and more extensive. Traditional navigation schemes in greenhouses, orchards, and other agricultural environments often have problems such as the inability to deal wi...
Traditional Japanese orchards control the growth height of fruit trees for the convenience of farmers, which is unfavorable to the operation of medium- and large-sized machinery. A compact, safe, and stable spraying system could offer a solution for...
To address the challenges of low pesticide utilization, insufficient automation, and health risks in orchard plant protection, we developed an autonomous spraying vehicle using ArduPilot firmware and a robot operating system (ROS). The system tackles...
The real-time detection of banana bunches and stalks in banana orchards is a key technology in the application of agricultural robots. The complex conditions of the orchard make accurate detection a difficult task, and the light weight of the deep le...
In orchard fruit picking systems for pears, the challenge is to identify the full shape of the soft fruit to avoid injuries while using robotic or automatic picking systems. Advancements in computer vision have brought the potential to train for diff...
Aiming at the problem that the current traditional apple yield estimation methods rely on manual investigation and do not make full use of multi-source information, this paper proposes an apple orchard yield prediction framework combining multispectr...
In complex orchard environments, orchard mowing robots are prone to longitudinal slippage because of the characteristics of tires and the adhesion conditions of the road surface, which makes it difficult for the robots to maintain high-precision path...
Aiming at the problems of complex image background and numerous interference factors faced by visual navigation systems in orchard environments, this paper proposes an orchard navigation line recognition method based on U-Net. Firstly, the drivable a...
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