A Hyperspectral Data 3D Convolutional Neural Network Classification Model for Diagnosis of Gray Mold Disease in Strawberry Leaves.

Dae-Hyun Jung,Taek Sung Lee,Jeong Do Kim,Soo Hyun Park,Hyoung Seok Kim,Ho-Youn Kim

doi:10.3389/fpls.2022.837020

Abstract

Gray mold disease is one of the most frequently occurring diseases in strawberries. Given that it spreads rapidly, rapid countermeasures are necessary through the development of early diagnosis technology. In this study, hyperspectral images of strawberry leaves that were inoculated with gray mold fungus to cause disease were taken; these images were classified into healthy and infected areas as seen by the naked eye. The areas where the infection spread after time elapsed were classified as the asymptomatic class. Square regions of interest (ROIs) with a dimensionality of 16 × 16 × 150 were acquired as training data, including infected, asymptomatic, and healthy areas. Then, 2D and 3D data were used in the development of a convolutional neural network (CNN) classification model. An effective wavelength analysis was performed before the development of the CNN model. Further, the classification model that was developed with 2D training data showed a classification accuracy of 0.74, while the model that used 3D data acquired an accuracy of 0.84; this indicated that the 3D data produced slightly better performance. When performing classification between healthy and asymptomatic areas for developing early diagnosis technology, the two CNN models showed a classification accuracy of 0.73 with regards to the asymptomatic ones. To increase accuracy in classifying asymptomatic areas, a model was developed by smoothing the spectrum data and expanding the first and second derivatives; the results showed that it was possible to increase the asymptomatic classification accuracy to 0.77 and reduce the misclassification of asymptomatic areas as healthy areas. Based on these results, it is concluded that the proposed 3D CNN classification model can be used as an early diagnosis sensor of gray mold diseases since it produces immediate on-site analysis results of hyperspectral images of leaves.

Highlights

The strawberry (Fragaria ananassa) is a widely grown species in the Fragaria genus, and it is produced and consumed throughout the world
The present study proposes a deep learning model for classifying infected, asymptomatic infected, and non-infected areas among local areas using hyperspectral images in order to perform early diagnosis of gray mold disease in strawberries
This study aimed to develop a classification model that uses hyperspectral imaging for early diagnosis of gray mold disease in strawberries and find an approach that can be used in practice

Summary

Introduction

The strawberry (Fragaria ananassa) is a widely grown species in the Fragaria genus, and it is produced and consumed throughout the world. In 2021, the global fresh strawberry market was estimated to be 1.772 billion dollars, making it an important crop production system. In South Korea, the total production value of strawberries is 1.34 trillion South Korean won, which is the largest market among horticultural crops. The gray mold disease, which is caused by Botrytis cinerea, occurs in an average of 10–15% of strawberry-cultivated land each year, resulting in an economic damage of 110 billion South Korean won. Many spores are formed in the lesion and propagate to other places, spreading the disease and increasing the damage. It is necessary to diagnose and contain the disease at its early stages (Back et al, 2014; Hao et al, 2017)

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Results