Detection and Classification of Rice Infestation with Rice Leaf Folder (Cnaphalocrocis medinalis) Using Hyperspectral Imaging Techniques

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 22; p. 4587
• Main Authors: Liang, Gui-Chou, Ouyang, Yen-Chieh, Dai, Shu-Mei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2021
• Subjects: Agriculture; Algorithms; Artificial neural networks; Background noise; band selection; Calibration; Classification; Commercialization; Computer applications; Data analysis; Deep learning; Energy consumption; Error reduction; Feature extraction; Frequencies; Human error; Humidity; hyperspectral image classification; Hyperspectral imaging; Image classification; Image processing; Imaging techniques; Infestation; Insecticides; Learning algorithms; Leaves; Light; Machine learning; Neural networks; Noise; Plant diseases; Remote sensing; Rice; rice leaf folder; Sensors; Spectra; Spectrum analysis; target detection; Target recognition; Taiwan
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13224587

The detection of rice leaf folder (RLF) infestation usually depends on manual monitoring, and early infestations cannot be detected visually. To improve detection accuracy and reduce human error, we use push-broom hyperspectral sensors to scan rice images and use machine learning and deep neural learning methods to detect RLF-infested rice leaves. Different from traditional image processing methods, hyperspectral imaging data analysis is based on pixel-based classification and target recognition. Since the spectral information itself is a feature and can be considered a vector, deep learning neural networks do not need to use convolutional neural networks to extract features. To correctly detect the spectral image of rice leaves infested by RLF, we use the constrained energy minimization (CEM) method to suppress the background noise of the spectral image. A band selection method was utilized to reduce the computational energy consumption of using the full-band process, and six bands were selected as candidate bands. The following method is the band expansion process (BEP) method, which is utilized to expand the vector length to improve the problem of compressed spectral information for band selection. We use CEM and deep neural networks to detect defects in the spectral images of infected rice leaves and compare the performance of each in the full frequency band, frequency band selection, and frequency BEP. A total of 339 hyperspectral images were collected in this study; the results showed that six bands were sufficient for detecting early infestations of RLF, with a detection accuracy of 98% and a Dice similarity coefficient of 0.8, which provides advantages of commercialization of this field.