TheLNet270v1 - A Novel Deep-Network Architecture for the Automatic Classification of Thermal Images for Greenhouse Plants.

Md Parvez Islam,Yuka Nakano,Keinichi Tokuda,Nobuo Kochi,Unseok Lee

doi:10.3389/fpls.2021.630425

Abstract

The real challenge for separating leaf pixels from background pixels in thermal images is associated with various factors such as the amount of emitted and reflected thermal radiation from the targeted plant, absorption of reflected radiation by the humidity of the greenhouse, and the outside environment. We proposed TheLNet270v1 (thermal leaf network with 270 layers version 1) to recover the leaf canopy from its background in real time with higher accuracy than previous systems. The proposed network had an accuracy of 91% (mean boundary F1 score or BF score) to distinguish canopy pixels from background pixels and then segment the image into two classes: leaf and background. We evaluated the classification (segment) performance by using more than 13,766 images and obtained 95.75% training and 95.23% validation accuracies without overfitting issues. This research aimed to develop a deep learning technique for the automatic segmentation of thermal images to continuously monitor the canopy surface temperature inside a greenhouse.

Highlights

Leaf surface and internal structure changes are due to adverse growth, stomatal resistance, diseases, leaf angles, depth of the canopy, and water stress conditions, which alter the absorbance-reflection process of solar radiation (Lili et al, 1991; Kraft et al, 1996; Raza et al, 2015)
Over the last few years, the advancement of fast computing power, low-cost imaging systems with image processing software, and deep learning (DL) techniques have allowed for nondestructive disease diagnosis and detection of various stress conditions of plants in a timely manner (Liu and Wang, 2020)
The DL based on a convolution neural network (CNN) is the successor of traditional machine learning approaches that can learn features with greater precision and accuracy by activating maximum networkability (Christopher et al, 2018)

Summary

Introduction

Leaf surface and internal structure changes are due to adverse growth, stomatal resistance, diseases, leaf angles, depth of the canopy, and water stress conditions, which alter the absorbance-reflection process of solar radiation (Lili et al, 1991; Kraft et al, 1996; Raza et al, 2015). Thermography detected this reflected (emitted) long-wave infrared (8–14 μm), converted it into thermal images, and a false-color gradient demonstrated the temperature level of the plant leaves of canopies (Chaerle and Van Der Straeten, 2000).

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