AI-driven orchard management: Advancing sustainable apple production through convolutional neural network recognition
The aim of this study is to develop a convolutional neural network architecture designed for apple recognition in images. The relevance of this task is tied to the need for fruit recognition to automate the process of apple crop harvesting. To reduce computations, it is proposed to convert the image...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/14/e3sconf_icaw2024_03018.pdf |
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| author | Karabanov Georgy Ricardo Oke Olouafemi Krakhmalev Alexey |
| author_facet | Karabanov Georgy Ricardo Oke Olouafemi Krakhmalev Alexey |
| author_sort | Karabanov Georgy |
| collection | DOAJ |
| description | The aim of this study is to develop a convolutional neural network architecture designed for apple recognition in images. The relevance of this task is tied to the need for fruit recognition to automate the process of apple crop harvesting. To reduce computations, it is proposed to convert the image captured by the camera from RGB format to HSV format. Using the example of a red apple, the creation of a bitmask is demonstrated, which allows for the identification of regions of the desired color within the image. A structure and parameters of the convolutional neural network were proposed, along with a method for computing the distance between the detected object and the camera based on the pre-calculation of the focal length. To analyze the results of the neural network under consideration, software was developed in Python using the TensorFlow and Keras libraries. The training and testing of the neural network were conducted on a PC Aspire A315-23 with an AMD Athlon Silver 3050U 1.2 GHz processor, 4 GB DDR4 RAM, and an AMD Radeon Graphics 2.30 GHz graphics card, running Windows 11 Pro operating system. The neural network was trained for 15 epochs, taking 217 seconds in total. Object recognition by the trained neural network took around 1 second. The proposed convolutional neural network model demonstrated a recognition accuracy of 86% on the test image set. |
| format | Article |
| id | doaj-art-a64680107f7146e2b5cc672334eee84d |
| institution | DOAJ |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-a64680107f7146e2b5cc672334eee84d2025-08-20T03:12:46ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016140301810.1051/e3sconf/202561403018e3sconf_icaw2024_03018AI-driven orchard management: Advancing sustainable apple production through convolutional neural network recognitionKarabanov Georgy0Ricardo Oke Olouafemi1Krakhmalev Alexey2Department of Computer Science and Computer Engineering of Food Production, Russian Biotechnological University «ROSBIOTECH»Department of Automated Control Systems for Biotechnological Processes, Russian Biotechnological University «ROSBIOTECH»Russian State Agrarian University–Timiryazev Moscow Agricultural Academy, Institute of Mechanics and PowerThe aim of this study is to develop a convolutional neural network architecture designed for apple recognition in images. The relevance of this task is tied to the need for fruit recognition to automate the process of apple crop harvesting. To reduce computations, it is proposed to convert the image captured by the camera from RGB format to HSV format. Using the example of a red apple, the creation of a bitmask is demonstrated, which allows for the identification of regions of the desired color within the image. A structure and parameters of the convolutional neural network were proposed, along with a method for computing the distance between the detected object and the camera based on the pre-calculation of the focal length. To analyze the results of the neural network under consideration, software was developed in Python using the TensorFlow and Keras libraries. The training and testing of the neural network were conducted on a PC Aspire A315-23 with an AMD Athlon Silver 3050U 1.2 GHz processor, 4 GB DDR4 RAM, and an AMD Radeon Graphics 2.30 GHz graphics card, running Windows 11 Pro operating system. The neural network was trained for 15 epochs, taking 217 seconds in total. Object recognition by the trained neural network took around 1 second. The proposed convolutional neural network model demonstrated a recognition accuracy of 86% on the test image set.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/14/e3sconf_icaw2024_03018.pdf |
| spellingShingle | Karabanov Georgy Ricardo Oke Olouafemi Krakhmalev Alexey AI-driven orchard management: Advancing sustainable apple production through convolutional neural network recognition E3S Web of Conferences |
| title | AI-driven orchard management: Advancing sustainable apple production through convolutional neural network recognition |
| title_full | AI-driven orchard management: Advancing sustainable apple production through convolutional neural network recognition |
| title_fullStr | AI-driven orchard management: Advancing sustainable apple production through convolutional neural network recognition |
| title_full_unstemmed | AI-driven orchard management: Advancing sustainable apple production through convolutional neural network recognition |
| title_short | AI-driven orchard management: Advancing sustainable apple production through convolutional neural network recognition |
| title_sort | ai driven orchard management advancing sustainable apple production through convolutional neural network recognition |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/14/e3sconf_icaw2024_03018.pdf |
| work_keys_str_mv | AT karabanovgeorgy aidrivenorchardmanagementadvancingsustainableappleproductionthroughconvolutionalneuralnetworkrecognition AT ricardookeolouafemi aidrivenorchardmanagementadvancingsustainableappleproductionthroughconvolutionalneuralnetworkrecognition AT krakhmalevalexey aidrivenorchardmanagementadvancingsustainableappleproductionthroughconvolutionalneuralnetworkrecognition |