|Title:||Using deep learning to identify maturity and 3D distance in pineapple fields||Authors:||Chia-Ying Chang
|Issue Date:||May-2022||Publisher:||Springer||Journal Volume:||12||Journal Issue:||1||Start page/Pages:||8749||Source:||Scientific Reports||Abstract:||
Pineapples are an important agricultural economic crop in Taiwan. Considerable human resources are required to protect pineapples from excessive solar radiation, which could otherwise lead to overheating and subsequent deterioration. Note that simple covering all of the fruit with a paper bag is not a viable solution, due to the fact that it makes it impossible to determine whether the fruit is ripe. This paper proposes a system by which to automate the detection of ripe pineapples. The proposed deep learning architecture enables detection regardless of lighting conditions, achieving accuracy of more than 99.27% with error of less than 2% at distances of 300 similar to 800 mm. This proposed system using an Nvidia TX2 is capable of 15 frames per second, thereby making it possible to mount the device on machines that move at walking speed.
|Appears in Collections:||SCI期刊|
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