Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images

Abstract

This paper focuses on the gaps that occur inside plantations; these gaps, although not having anything growing in them, still happen to be watered. This action ends up wasting tons of liters of water every year, which translates into financial and environmental losses. To avoid these losses, we suggest early detection. To this end, we analyzed the different available neural networks available with multispectral images. This entailed training each regional and regression-based network five times with five different datasets. Networks based on two possible solutions were chosen: unmanned aerial vehicle (UAV) depletion or post-processing with external software. The results show that the best network for UAV depletion is the Tiny-YOLO (You Only Look Once) version 4-type network, and the best starting weights for Mask-RCNN were from the Tiny-YOLO network version. Although no mean average precision (mAP) of over 70% was achieved, the final trained networks managed to detect mostly gaps, including low-vegetation areas and very small gaps, which had a tendency to be overlooked during the labeling stage.

Effect of the Light Environment on Image-Based SPAD Value Prediction of Radish Leaves

“…HSV and HSL color systems, which are obtained by converting from the RGB color system, are also used for semantic segmentation [12], such as georeferencing and crop quality monitoring [13]. There are also studies using multispectral cameras [14,15] and hyperspectral cameras [16,17], but these methods require high equipment costs.…”