Hybrid Deep Learning Models with Sparse Enhancement Technique for Detection of Newly Grown Tree Leaves

Abstract

The life cycle of leaves, from sprout to senescence, is the phenomenon of regular changes such as budding, branching, leaf spreading, flowering, fruiting, leaf fall, and dormancy due to seasonal climate changes. It is the effect of temperature and moisture in the life cycle on physiological changes, so the detection of newly grown leaves (NGL) is helpful for the estimation of tree growth and even climate change. This study focused on the detection of NGL based on deep learning convolutional neural network (CNN) models with sparse enhancement (SE). As the NGL areas found in forest images have similar sparse characteristics, we used a sparse image to enhance the signal of the NGL. The difference between the NGL and the background could be further improved. We then proposed hybrid CNN models that combined U-net and SegNet features to perform image segmentation. As the NGL in the image were relatively small and tiny targets, in terms of data characteristics, they also belonged to the problem of imbalanced data. Therefore, this paper further proposed 3-Layer SegNet, 3-Layer U-SegNet, 2-Layer U-SegNet, and 2-Layer Conv-U-SegNet architectures to reduce the pooling degree of traditional semantic segmentation models, and used a loss function to increase the weight of the NGL. According to the experimental results, our proposed algorithms were indeed helpful for the image segmentation of NGL and could achieve better kappa results by 0.743.

Surface Defect Detection of Wet-Blue Leather Using Hyperspectral Imaging

“…Another characteristic is to maintain high channel number of up-sampling so that relative position relation and detailed features can be fully combined and the quality of recognition is upgraded. The U-Net has been used in image segmentation, such as medical images [50][51] and remote sensing [52]; it can be also extended and enhanced to the 3D-UNet models, which have been applied in medical applications [53][54][55]. The network architecture with parameters is shown in Fig.…”

Plant Species Classification Based on Hyperspectral Imaging via a Lightweight Convolutional Neural Network Model

“…The reason is that CNN can automatically learn objective, multi-scale, and most discriminative features from raw data without human subjectivity. Following this trend, a few CNN-based plant recognition methods were proposed ( Lee et al, 2015 ; Grinblat et al, 2016 ; Carranza-Rojas et al, 2017 ; Lee et al, 2017 ; Chen et al, 2018 ; Zhu et al, 2019 ; Chen et al, 2021 ). A two-dimensional (2D)-CNN model is adopted in each work to learn the discriminative features from the entire RGB plant images.…”