Rapid estimation of leaf nitrogen content in apple-trees based on canopy hyperspectral reflectance using multivariate methods

Chen, Shaomin; Hu, Tiantian; Luo, Lihua; He, Qiong; Zhang, Shaowu; Li, Mengyue; Cui, Xiaolu

doi:10.1016/j.infrared.2020.103542

Cited by 41 publications

(16 citation statements)

References 46 publications

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“…In order to solve the problem of band multicollinearity, we use SPA function to further screen the highly sensitive spectral features, which can improve the speed and stability of later modeling. The SPA function is widely used in sensitive hyperspectral feature extraction [55][56][57]. After the Pearson-Findpeaks-SPA process, we extracted the sensitive hyperspectral bands that met our experimental requirements.…”

Section: Model Resultsmentioning

confidence: 99%

Detection of Larch Forest Stress from Jas’s Larch Inchworm (Erannis jacobsoni Djak) Attack Using Hyperspectral Remote Sensing

Huang

Xie

et al. 2021

Remote Sensing

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Detection of forest pest outbreaks can help in controlling outbreaks and provide accurate information for forest management decision-making. Although some needle injuries occur at the beginning of the attack, the appearance of the trees does not change significantly from the condition before the attack. These subtle changes cannot be observed with the naked eye, but usually manifest as small changes in leaf reflectance. Therefore, hyperspectral remote sensing can be used to detect the different stages of pest infection as it offers high-resolution reflectance. Accordingly, this study investigated the response of a larch forest to Jas’s Larch Inchworm (Erannis jacobsoni Djak) and performed the different infection stages detection and identification using ground hyperspectral data and data on the forest biochemical components (chlorophyll content, fresh weight moisture content and dry weight moisture content). A total of 80 sample trees were selected from the test area, covering the following three stages: before attack, early-stage infection and middle- to late-stage infection. Combined with the Findpeaks-SPA function, the response relationship between biochemical components and spectral continuous wavelet coefficients was analyzed. The support vector machine classification algorithm was used for detection infection. The results showed that there was no significant difference in the biochemical composition between healthy and early-stage samples, but the spectral continuous wavelet coefficients could reflect these subtle changes with varying degrees of sensitivity. The continuous wavelet coefficients corresponding to these stresses may have high potential for infection detection. Meanwhile, the highest overall accuracy of the model based on chlorophyll content, fresh weight moisture content and dry weight moisture content were 90.48%, 85.71% and 90.48% respectively, and the Kappa coefficients were 0.85, 0.79 and 0.86 respectively.

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Section: Model Resultsmentioning

confidence: 99%

Detection of Larch Forest Stress from Jas’s Larch Inchworm (Erannis jacobsoni Djak) Attack Using Hyperspectral Remote Sensing

Huang

Xie

et al. 2021

Remote Sensing

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“…The extreme learning machine (ELM) [37,38] is a single hidden layer feedforward neural network. Compared with a three-layer feedforward neural network, ELM has a faster training speed and stronger generalization ability.…”

Section: Elm Modelmentioning

confidence: 99%

Non-Destructive Detection of pH Value of Kiwifruit Based on Hyperspectral Fluorescence Imaging Technology

Wang

Chen

et al. 2022

Agriculture

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Non-destructive detection of the pH value of kiwifruit has important practical significance for its quality classification. In this study, hyperspectral fluorescence imaging technology was proposed to quantitatively predict the pH value of kiwifruit non-destructively. Firstly, the SPXY algorithm was used to divide samples into training and prediction sets and three different algorithms were used to preprocess the raw spectral data. Secondly, algorithms such as the iteratively retaining information variables (IRIV), the variable iterative space shrinkage approach (VISSA), the model adaptive space shrinkage (MASS), the random frog (RF), and their combination (i.e., IRIV + VISSA + MASS + RF, IVMR) were used to extract effective variables from the preprocessed spectral data. Moreover, the second extractions, such as IRIV-VISSA and IRIV-MASS, and the third extraction (i.e., IVMR-VISSA-IRIV) were used to further reduce the redundant variables. Based on the effective variables, four regression models—random forest (RF), partial least square (PLSR), extreme learning machines (ELM), and multiple-kernel support vector regression (MK-SVR)—were built and compared for predicting. The results show that IVMR-VISSA-IRIV-MK-SVR had the best prediction results, with RP2, RC2 and RPD of 0.8512, 0.8580, and 2.66, respectively, which verifies that hyperspectral fluorescence imaging technology is reliable for predicting the pH value of kiwifruit non-destructively.

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“…Introduced the variable with the largest projection vector into the variable combination, established the Partial Least Squares (PLS) regression model, and calculated the Root Mean Square Error (RMSE) obtained by different parameter combinations. The loop ends when the number of characteristic parameters reached the value corresponding to the minimum RMSE [33], [34].…”

Section: A Chlorophyll Fluorescent Parameters Selectionmentioning

confidence: 99%

Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging

Yan

2022

IEEE Access

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Drought has become one of the main challenges facing global agricultural production and crop safety. Drought stress will lead to the termination of crop photosynthesis, which will seriously affect the growth and development of crops. We aimed to study a method for identificaton of the drought stress in tomato seedlings using chlorophyll fluorescence imaging. In this study, chlorophyll fluorescence parameters and there corresponding chlorophyll fluorescence images of 4 different drought stress levels were collected. Then three feature optimization algorithms which were Successive Projections Algorithm, Iteratively Retains Informative Variables and Variable Iterative Space Shrinkage Approac were used to choose important parameters. A total of five common parameters were obtained, and the corresponding chlorophyll fluorescence images of the five common parameters were selected. And two types of image features were used to study and analyze drought stress classes: histogram features and texture features. The Pearson correlations of the features were calculated and the high correlated features were input into three models, which were Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and k-Nearest Neighbor (KNN), to identify drought stress classes. The recognition accuracy rate of LDA, SVM and KNN were 86.8%, 87.1% and 76.5% respectively. Our experiment results showed that the five common fluorescence parameters and there corresponding image features could be used to evaluate the drought stress classes of tomato seedlings, and had a good evaluation effect. This research provideed a new method for monitoring drought stress classes and had considerable prospects for non-destructive diagnosis of plant drought stress. INDEX TERMSChlorophyll fluorescence imaging, drought stress, feature extraction, machine learning, tomato seedlings. YAN LONG received the B.S. degree in electronic and information engineering and the M.S. degree in communication engineering from Shandong University, China, in 2005 and 2008, respectively, and the Ph.D. degree in agricultural electrification and automation from Northwest A&F University, China, in 2015. Since 2018, she has been an Associate Professor with the Mechanical and Electronic Engineering Department, Northwest A&F University. Her research interests include digital agriculture and agricultural information engineering, agricultural electronics, and automation technology. MINJUAN MA received the B.S. degree in electronic information engineering from Northwest A&F University, China, in 2019, where she is currently pursuing the M.S. degree in agricultural electrification and automation. Her research interests include digital agriculture and agricultural information engineering.

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Rapid estimation of leaf nitrogen content in apple-trees based on canopy hyperspectral reflectance using multivariate methods

Cited by 41 publications

References 46 publications

Detection of Larch Forest Stress from Jas’s Larch Inchworm (Erannis jacobsoni Djak) Attack Using Hyperspectral Remote Sensing

Detection of Larch Forest Stress from Jas’s Larch Inchworm (Erannis jacobsoni Djak) Attack Using Hyperspectral Remote Sensing

Non-Destructive Detection of pH Value of Kiwifruit Based on Hyperspectral Fluorescence Imaging Technology

Recognition of Drought Stress State of Tomato Seedling Based on Chlorophyll Fluorescence Imaging

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