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  5. RAPID PROTOTYPING OF PEAR DETECTION NEURAL NETWORK WITH YOLO ARCHITECTURE IN PHOTOGRAPHS
 
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RAPID PROTOTYPING OF PEAR DETECTION NEURAL NETWORK WITH YOLO ARCHITECTURE IN PHOTOGRAPHS

Journal
ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference
ISSN
2256-070X
Date Issued
2023
Author(s)
Kodors, Sergejs
Rezekne Academy of Technologies 
Marks Sondors
Rezekne Academy of Technologies 
Gunārs Lācis
Institute of Horticulture (LatHort)Dobele, Latvia
Edgars Rubauskis
Institute of Horticulture (LatHort)Dobele, Latvia
Apeināns, Ilmārs
Rezekne Academy of Technologies 
Zarembo, Imants 
Rezekne Academy of Technologies 
DOI
10.17770/etr2023vol1.7293
Abstract
Fruit yield estimation and forecasting are essential processes for data-based decision-making in agribusiness to optimise fruit-growing and marketing operations. The yield forecasting is based on the application of historical data, which was collected in the result of periodic yield estimation. Meanwhile, the object detection methods and regression models are applied to calculate yield per tree. The application of powerful neural network architectures for rapid prototyping is a common approach of modern artificial intelligence engineering. Meanwhile, the most popular object detection solution is YOLO architecture. Our project team collected the dataset of fruiting pear tree photographs (Pear640) and trained YOLOv5m with mAP@0.5 95% and mAP@0.5:0.95 56%. The obtained results were compared with other YOLOv5-7.0 and YOLOv7 models and similar studies.
Subjects
  • artificial intelli...

  • deep learning

  • smart horticulture...

  • yield estimation

File(s)
 RAPID PROTOTYPING OF PEAR DETECTION NEURAL NETWORK WITH YOLO ARCHITECTURE IN PHOTOGRAPHS.pdf (2.66 MB)
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0
Acquisition Date
Jan 12, 2024
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