Using deep learning to identify maturity and 3D distance in pineapple fields

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 ~ 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.

Code and data availability

The paper's pineapple image database (8,852 field images plus bagged-fruit images) is explicitly not public, and the authors' network structure and program are available only by contacting the corresponding author. No public paper-specific dataset, code repository, or trained model is provided; the YOLOv5 GitHub link,

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