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Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN - ScienceDirect
Source: sciencedirect.com
Article summary:
1. Apple fruits on one tree are labeled into 4 classes based on occlusion conditions.
2. A multi-class apple detection method in dense-foliage fruiting-wall trees was proposed based on Faster Region-Convolutional Neural Network.
3. The results indicated that all the apples in different classes could be effectively detected, which can help the robot to decide the picking strategy and avoid potential damage by branches and trellis wires.
Article analysis:
This article is generally reliable and trustworthy, as it provides a detailed description of a multi-class apple detection method using Faster Region-Convolutional Neural Network (FRCN). The article is well researched and provides evidence for its claims, such as the mean average precision of 0.879 achieved by VGG16 with a detection speed of 0.241 s/image. Furthermore, the article acknowledges potential risks associated with robotic harvesting, such as potential damage caused by branches or trellis wires when high-vigor rootstock apple cultivar is used.
However, there are some points of consideration that are missing from this article. For example, it does not explore any counterarguments to its claims or provide any alternative solutions to the problem being addressed. Additionally, there is no discussion about how this technology could be used in other contexts or how it could be improved upon in the future. Finally, while the article does present both sides of the argument equally, it does not provide any information about potential biases or sources of bias that may have influenced its conclusions.