COMPUTER VISION SYSTEM FOR DETECTING ORCHARD TREES FROM UAV IMAGES

Orchard tree inventory plays an important role in acquiring up-to-date information on planted trees for effective treatments and crop insurance purposes. Determining tree damage could help assess orchards’ health faster and cheaper. Having accurate information on the tree’s status could also help ma...

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Published in:International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLIII-B4-2022; pp. 661 - 668
Main Authors: Jemaa, H., Bouachir, W., Leblon, B., Bouguila, N.
Format: Journal Article Conference Proceeding
Language:English
Published: Gottingen Copernicus GmbH 02.06.2022
Copernicus Publications
Subjects:
Algorithms
Apples
Color imagery
Computer vision
Computers
Crop insurance
Damage assessment
Datasets
Deep learning
Detection
Fieldwork
Fruit trees
Fruits
Imagery
Machine learning
Orchards
Testing
Training
Trees
Unmanned aerial vehicles
Vision systems
ISSN:2194-9034, 1682-1750, 2194-9034
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Abstract Orchard tree inventory plays an important role in acquiring up-to-date information on planted trees for effective treatments and crop insurance purposes. Determining tree damage could help assess orchards’ health faster and cheaper. Having accurate information on the tree’s status could also help managers to plan necessary fieldwork and predict productivity. Traditional orchard inventory is often performed manually, and thus is time-consuming, costly, and subject to error. An alternative is computer vision algorithms that could automatically detect orchard trees based on UAV imagery. The objective of this study is to develop a method using advanced computer vision algorithms to automatically detect apple trees on UAV multispectral images. This task is challenging since apple trees are overlapping over the UAV images, and hence distinguishing different crowns could be difficult. Motivated by the latest advances in UAV imagery and deep-learning models, addressed the tree detection problem by exploring the two CNN models YOLO (You Only Look Once) and DeepForest for detecting apple trees on UAV images. We first constructed a labelled dataset by dividing the study area into equally sized patches. Then we manually annotated all apple trees seen in RGB images. The annotated dataset was then randomly divided into three subsets (training, validation, and testing), for training and testing machine learning models. The performed experiments demonstrate the efficiency and validity of the proposed approach for orchard tree inventory. In particular, the proposed framework achieved a precision of 91% and an F1-score of 87% by adopting the DeepForest model for tree detection.
AbstractList Orchard tree inventory plays an important role in acquiring up-to-date information on planted trees for effective treatments and crop insurance purposes. Determining tree damage could help assess orchards’ health faster and cheaper. Having accurate information on the tree’s status could also help managers to plan necessary fieldwork and predict productivity. Traditional orchard inventory is often performed manually, and thus is time-consuming, costly, and subject to error. An alternative is computer vision algorithms that could automatically detect orchard trees based on UAV imagery. The objective of this study is to develop a method using advanced computer vision algorithms to automatically detect apple trees on UAV multispectral images. This task is challenging since apple trees are overlapping over the UAV images, and hence distinguishing different crowns could be difficult. Motivated by the latest advances in UAV imagery and deep-learning models, addressed the tree detection problem by exploring the two CNN models YOLO (You Only Look Once) and DeepForest for detecting apple trees on UAV images. We first constructed a labelled dataset by dividing the study area into equally sized patches. Then we manually annotated all apple trees seen in RGB images. The annotated dataset was then randomly divided into three subsets (training, validation, and testing), for training and testing machine learning models. The performed experiments demonstrate the efficiency and validity of the proposed approach for orchard tree inventory. In particular, the proposed framework achieved a precision of 91% and an F1-score of 87% by adopting the DeepForest model for tree detection.
Author Bouguila, N.
Jemaa, H.
Bouachir, W.
Leblon, B.
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SubjectTerms Algorithms
Apples
Color imagery
Computer vision
Computers
Crop insurance
Damage assessment
Datasets
Deep learning
Detection
Fieldwork
Fruit trees
Fruits
Imagery
Machine learning
Orchards
Testing
Training
Trees
Unmanned aerial vehicles
Vision systems
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