A review of deep learning used in the hyperspectral image analysis for agriculture

Wang, Qianqian; Liu, Baohua; Liu, Lipeng; Zhu, Yanjun; Hou, Jialin; Liu, Ping; Li, Xiang

doi:10.1007/s10462-021-10018-y

Cited by 167 publications

(50 citation statements)

References 140 publications

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“…Recently, deep learning, as a new method of machine learning, has gained remarkable results for detecting and classifying the spectral and spatio-spectral signatures in HIS. Deep learning learns features deeply and automatically, and processes large volumes of data effectively [ 24 , 25 , 26 ]. Thus, it can construct a network containing many neurons efficiently and quickly, and it is applied widely in spectroscopy [ 27 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning

Yan

Zhang

et al. 2022

Foods

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Rapid and accurate detection of pesticide residue levels can help to prevent the harm of pesticide residue. This study used visible/near-infrared (Vis-NIR) (376–1044 nm) and near-infrared (NIR) (915–1699 nm) hyperspectral imaging systems (HISs) to detect the level of pesticide residues. Three different varieties of grapes were sprayed with four levels of pesticides. Logistic regression (LR), support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and residual neural network (ResNet) models were used to build classification models for pesticide residue levels. The saliency maps of CNN and ResNet were conducted to visualize the contribution of wavelengths. Overall, the results of NIR spectra performed better than those of Vis-NIR spectra. For Vis-NIR spectra, the best model was ResNet, with the accuracy of over 93%. For NIR spectra, LR was the best, with the accuracy of over 97%, but SVM, CNN, and ResNet also showed closed and fine results. The saliency map of CNN and ResNet presented similar and closed ranges of crucial wavelengths. Overall results indicated deep learning performed better than conventional machine learning. The study showed that the use of hyperspectral imaging technology combined with machine learning can effectively detect the level of pesticide residues in grapes.

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Section: Introductionmentioning

confidence: 99%

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning

Yan

Zhang

et al. 2022

Foods

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“…Adopting the hyperspectral imaging technique to detect leaf disease has become a popular approach because hyperspectral imaging has high potential for finding new insights about plant diseases. Through the information fusion of multiple wavelengths, hyperspectral imaging can achieve better classification performance than using RGB images ( Cabrera Ardila et al, 2020 ; Wang et al, 2021 ). Koushik et al (2019) used hyperspectral imaging to detect soybean charcoal rot disease.…”

Section: Introductionmentioning

confidence: 99%

Detecting Asymptomatic Infections of Rice Bacterial Leaf Blight Using Hyperspectral Imaging and 3-Dimensional Convolutional Neural Network With Spectral Dilated Convolution

Cao

Yuan

et al. 2022

Front. Plant Sci.

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Rice is one of the most important food crops for human beings. Its total production ranks third in the grain crop output. Bacterial Leaf Blight (BLB), as one of the three major diseases of rice, occurs every year, posing a huge threat to rice production and safety. There is an asymptomatic period between the infection and the onset periods, and BLB will spread rapidly and widely under suitable conditions. Therefore, accurate detection of early asymptomatic BLB is very necessary. The purpose of this study was to test the feasibility of detecting early asymptomatic infection of the rice BLB disease based on hyperspectral imaging and Spectral Dilated Convolution 3-Dimensional Convolutional Neural Network (SDC-3DCNN). First, hyperspectral images were obtained from rice leaves infected with the BLB disease at the tillering stage. The spectrum was smoothed by the Savitzky–Golay (SG) method, and the wavelength between 450 and 950 nm was intercepted for analysis. Then Principal Component Analysis (PCA) and Random Forest (RF) were used to extract the feature information from the original spectra as inputs. The overall performance of the SDC-3DCNN model with different numbers of input features and different spectral dilated ratios was evaluated. Lastly, the saliency map visualization was used to explain the sensitivity of individual wavelengths. The results showed that the performance of the SDC-3DCNN model reached an accuracy of 95.4427% when the number of inputs is 50 characteristic wavelengths (extracted by RF) and the dilated ratio is set at 5. The saliency-sensitive wavelengths were identified in the range from 530 to 570 nm, which overlaps with the important wavelengths extracted by RF. According to our findings, combining hyperspectral imaging and deep learning can be a reliable approach for identifying early asymptomatic infection of the rice BLB disease, providing sufficient support for early warning and rice disease prevention.

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“…It should be noted that the deep learning model has become more common for spectral data processing than conventional machine learning tools in recent years. Both supervised and unsupervised classes were applied in the disease identification algorithms, which included the K-means clustering algorithm [25], support vector machine (SVM) [26], Knearest neighbor (KNN) [27], decision tree algorithm [28], and deep learning methods, such as stacked auto-encoder (SAE) [29], deep belief network (DBN) [30], convolutional neural networks (CNNs) [31], etc. Meanwhile, the feature extraction methods of hyperspectral data have changed from extraction based on a single spatial or spectral feature to a space spectrum combination.…”

Section: Literature Overviewmentioning

confidence: 99%

Hyperspectral Sensing of Plant Diseases: Principle and Methods

Wan

et al. 2022

Agronomy

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Pathogen infection has greatly reduced crop production. As the symptoms of diseases usually appear when the plants are infected severely, rapid identification approaches are required to monitor plant diseases at early the infection stage and optimize control strategies. Hyperspectral imaging, as a fast and nondestructive sensing technology, has achieved remarkable results in plant disease identification. Various models have been developed for disease identification in different plants such as arable crops, vegetables, fruit trees, etc. In these models, important algorithms, such as the vegetation index and machine learning classification and methods have played significant roles in the detection and early warning of disease. In this paper, the principle of hyperspectral imaging technology and common spectral characteristics of plant disease symptoms are discussed. We reviewed the impact mechanism of pathogen infection on the photo response and spectrum features of the plants, the data processing tools and algorithms of the hyperspectral information of pathogen-infected plants, and the application prospect of hyperspectral imaging technology for the identification of plant diseases.

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A review of deep learning used in the hyperspectral image analysis for agriculture

Cited by 167 publications

References 140 publications

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning

Detection of Pesticide Residue Level in Grape Using Hyperspectral Imaging with Machine Learning

Detecting Asymptomatic Infections of Rice Bacterial Leaf Blight Using Hyperspectral Imaging and 3-Dimensional Convolutional Neural Network With Spectral Dilated Convolution

Hyperspectral Sensing of Plant Diseases: Principle and Methods

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