Neural network for identifying apple fruits on the crown of a tree

Smirnov, Igor; Kutyrev, Alexey; Kiktev, Nikolay

doi:10.1051/e3sconf/202127001021

Cited by 11 publications

(4 citation statements)

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“…In our subsequent research on navigation, we also plan to use convolutional neural networks to refine the route. We used this toolkit to identify an apple when it was picked from a tree [16,17].…”

Section: Comparison Of Laboratory and Field Research Resultsmentioning

confidence: 99%

“…In an industrial garden, signals such as laser (clouds of points, 3D matrices and digital twins), ultrasonic and radio signals are used to determine the location and movement of an autonomous vehicle [14,15]. Speed, acceleration and changes in the position of the grippers and other working parts when the manipulator is operating under field conditions are controlled using accelerometers, gyroscopes and global satellite systems [16,17]. In this study, we will consider the use of a LiDAR (Light Detection and Ranging) sensor to construct maps of the obstacles and trajectories of an autonomous vehicle under the field conditions of an industrial garden.…”

Section: Introductionmentioning

confidence: 99%

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Laser Rangefinder Methods: Autonomous-Vehicle Trajectory Control in Horticultural Plantings

Kutyrev,

Kiktev,

Smirnov

2024

Sensors

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This article presents a developed motion control system for a robotic platform based on laser-ranging methods, a graph traversal algorithm and the search for the optimal path. The algorithm was implemented in an agricultural building and in the field. As a result, the most efficient algorithm for finding the optimal path (A*) for the robotic platform was chosen when performing various technological operations. In the Rviz visualization environment, a program code was developed for planning the movement path and setting the points of the movement trajectory in real time. To find the optimal navigation graph in an artificial garden, an application was developed using the C# programming language and Visual Studio 2019. The results of the experiments showed that field conditions can differ significantly from laboratory conditions, while the positioning accuracy is significantly lower. The statistical processing of the experimental data showed that, for the movement of a robotic platform along a given trajectory in the field, the most effective conditions are as follows: speed: 2.5 km/h; illumination: 109,600 lux; distance to the tree: 0.5 m. An analysis of the operating parameters of the LiDAR sensor showed that it provides a high degree of positioning accuracy under various lighting conditions at various speeds in the aisles of a garden 3 m wide with an inter-stem distance of 1.5 m and a tree crown width of 0.5 m. The use of sensors—rangefinders of the optical range—allows for the performance of positional movements of the robotic platform and ensures the autonomous performance of the basic technological operations of the units in intensive gardens with a deviation from the specified trajectory of no more than 8.4 cm, which meets the agrotechnical requirements.

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Section: Comparison Of Laboratory and Field Research Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Laser Rangefinder Methods: Autonomous-Vehicle Trajectory Control in Horticultural Plantings

Kutyrev,

Kiktev,

Smirnov

2024

Sensors

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“…These models demonstrate high efficiency in processing visual data, including images of biological objects. They have the ability to extract essential features and provide high accuracy in classification tasks [14,15]. Among various neural network models, modern YOLO convolutional models stand out as the most efficient.…”

Section: Methodsmentioning

confidence: 99%

Apple Flower Recognition Using Convolutional Neural Networks with Transfer Learning and Data Augmentation Technique

Kutyrev,

Andriyanov

2024

E3S Web Conf.

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Automated monitoring of apple flowers using convolutional neural networks will enable informed decision-making for planning thinning and fruit set operations, optimizing crop load, preventing fruiting periodicity, and enhancing crop quality. The article presents the results of apple flower recognition quality on images using the YOLOv8 (You Only Look Once version 8) convolutional neural network model with the application of transfer learning and data augmentation technique. Pre-trained weights on the Common Objects in Context (COCO) dataset were utilized in the research. To expand the dataset and enhance model performance, the tools Flip, 90° Rotate, Crop, Rotation, Shear, Grayscale, Hue, Saturation, Brightness, Exposure, Blur, Noise, and Cutout were applied. The result showed that artificial augmentation of the training dataset significantly improves the quality of training for the YOLOv8 convolutional neural network model, increasing the average accuracy of detecting class features apple flowers. The analysis of the Precision-Recall curve allowed establishing a classification threshold (0.47) that provides the optimal balance between precision and recall in recognizing apple flowers at the flowering stage in images. The mAP metric for recognizing the «flower» class (flowers in the flowering stage) was 0.595. The analysis of the obtained results revealed an increase in the Precision metric by 2.1%, Recall metric by 10.13%, and mAP@0.5 metric by 5.31% when using the augmentation technique. The obtained results indicate a significant improvement in the performance of the model in recognizing apple flowers when applying the augmentation technique to the training dataset.

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“…Accelerometers, gyroscopes, and global satellite systems are used to control acceleration, movement speed and position changes in the working bodies in space. To recognize fruits on a tree, various computer vision methods are used, including neural networks (Smirnov et al, 2021;Khort et al, 2020).…”

Section: Sensorsmentioning

confidence: 99%

Robotic Technologies in Horticulture: Analysis and Implementation Prospects

Hutsol

Kutyrev

Kiktev

et al. 2023

Agricultural Engineering

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The article contains an analytical review and perspectives of robotic technologies in horticulture. Trends in the growth of production, implementation, and sales of robots in various regions of the world are revealed. The analysis showed a lag in the introduction of agricultural robots compared to other sectors of the economy, as well as a significant gap between the countries of the Asian region and other continents. A review of technical means of three main components of ground agricultural robots is considered: navigation systems, sensors, and platform design. Examples of constructing a tree trajectory using the A* algorithm and using the Rviz visualization tools and the Github PathFindings graphical web service are given. As a result of the conducted research, the use of Lidar sensors is recommended, which will make it possible to design the route of robotic platforms, build maps by scanning a previously unknown surrounding space and updating the resulting map at each step of the algorithm in real time. The use of existing modern sensors with an optical rangefinder with a resolution of 4.5 million pixels, a frame rate of 25 frames per second and the ability to automatically adapt to the light level in combination with stereo cameras and GPS/GLONASS navigation will improve the positioning accuracy of robotic platforms and ensure autonomous operation. To perform basic technological operations for the care of plantings with row spacing of 2.5-4 m, a tree crown height up to 3-3.5 m with intensive technologies, the following design parameters of a robotic platform are required: agro-treatment of at least 1200 mm, adjustable track width of 1840-2080 mm, weight not more than 400 kg, load capacity not less than 1000 kg, the power of the power plant is not less than 5 kW.

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Neural network for identifying apple fruits on the crown of a tree

Cited by 11 publications

References 11 publications

Laser Rangefinder Methods: Autonomous-Vehicle Trajectory Control in Horticultural Plantings

Laser Rangefinder Methods: Autonomous-Vehicle Trajectory Control in Horticultural Plantings

Apple Flower Recognition Using Convolutional Neural Networks with Transfer Learning and Data Augmentation Technique

Robotic Technologies in Horticulture: Analysis and Implementation Prospects

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