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Advisor(s)
Abstract(s)
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.
Description
Keywords
Convolutional neural network Deep learning Fruit detection Precision agriculture Sustainability