Peaches Detection Using a Deep Learning Technique—A Contribution to Yield Estimation, Resources Management, and Circular Economy

Fruit detection is crucial for yield estimation and fruit picking system performance. Many state-of-the-art methods for fruit detection use convolutional neural networks (CNNs). This paper presents the results for peach detection by applying a faster R-CNN framework in images captured from an outdoo...

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Bibliographic Details
Published inClimate (Basel) Vol. 10; no. 2; p. 11
Main Authors Assunção, Eduardo T., Gaspar, Pedro D., Mesquita, Ricardo J. M., Simões, Maria P., Ramos, António, Proença, Hugo, Inacio, Pedro R. M.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2022
Subjects
Agricultural production
Agricultural wastes
Agriculture
Annotations
Apples
Artificial neural networks
Branches
Carbon dioxide
Circular economy
Climate change
Climate change mitigation
Consumption
convolutional neural network
Datasets
Deep learning
Detection
Emissions
Food waste
fruit detection
Fruits
Herbicides
Irrigation water
Machine learning
Neural networks
Nutrients
Orchards
Peaches
Pesticides
precision agriculture
Precision farming
Resource management
sustainability
Yields
Portugal
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Summary:Fruit detection is crucial for yield estimation and fruit picking system performance. Many state-of-the-art methods for fruit detection use convolutional neural networks (CNNs). This paper presents the results for peach detection by applying a faster R-CNN framework in images captured from an outdoor orchard. Although this method has been used in other studies to detect fruits, there is no research on peaches. Since the fruit colors, sizes, shapes, tree branches, fruit bunches, and distributions in trees are particular, the development of a fruit detection procedure is specific. The results show great potential in using this method to detect this type of fruit. A detection accuracy of 0.90 using the metric average precision (AP) was achieved for fruit detection. Precision agriculture applications, such as deep neural networks (DNNs), as proposed in this paper, can help to mitigate climate change, due to horticultural activities by accurate product prediction, leading to improved resource management (e.g., irrigation water, nutrients, herbicides, pesticides), and helping to reduce food loss and waste via improved agricultural activity scheduling.
ISSN:2225-1154
2225-1154
DOI:10.3390/cli10020011