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Application of Hyperspectral Imagery in Precision Agriculture

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 6892

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Special Issue Editor

Dr. Donna M. Delparte

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Department of Geosciences, Idaho State University, 921 S 8th Ave, STOP 8072, Pocatello, ID 83209, USA
Interests: GIS; remote sensing; photogrammetry, structure from motion (SfM), LiDAR; precision agriculture; hyperspectral imaging; spectroscopy; machine learning; PVY potato virus Y; CNN

Special Issue Information

Dear Colleagues,

Precision agriculture leveraging remote sensing will be vital for future global food security. Growers need to be equipped with strategies to detect crop threats and respond with targeted mitigation that is cost-effective and sustainable. This Special Issue provides an opportunity to highlight leading-edge remote sensing research that addresses critical issues facing agricultural systems, such as efficient water use, monitoring croplands for crop pest and disease threats, and developing targeted nutrient applications.

New sensor systems in precision agriculture include high-resolution spectral and spatial hyperspectral sensors aboard UAVs that allow the identification of crop threats at leaf scale by examining their spectral signatures. Machine learning and artificial intelligence applied to these data are opening the doors to the early detection and removal of diseased plants or those infected with viruses or other pathogens. Papers describing hyperspectral sensor applications across agricultural crop types are encouraged for this Special Issue.

Dr. Donna M. Delparte
Guest Editor

Manuscript Submission Information

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Keywords

machine learning
hyperspectral remote sensing
spectral analysis
CNN
crop threats
crop yield forecasting

Published Papers (4 papers)

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Research

22 pages, 14476 KiB

Open AccessArticle

Estimation and Mapping of Soil Organic Matter Content Using a Stacking Ensemble Learning Model Based on Hyperspectral Images

by Menghong Wu, Sen Dou, Nan Lin, Ranzhe Jiang and Bingxue Zhu

Remote Sens. 2023, 15(19), 4713; https://doi.org/10.3390/rs15194713 - 26 Sep 2023

Cited by 3 | Viewed by 1165

Abstract

Fast and accurate SOM estimation and spatial mapping are significant for cultivated land planning and management, crop growth monitoring, and soil carbon pool estimation. It is a key problem to construct a fast and efficient estimation model based on hyperspectral remote sensing image data to realize the inversion mapping of SOM in large areas. In order to solve the problem that the estimation accuracy is not high due to the influence of hyperspectral image quality and soil sample quantity during the estimation model construction, this study explored a method for constructing an estimation model of SOM contents based on a new stacking ensemble learning algorithm and hyperspectral images. Surface soil samples in Huangzhong County of Qinghai Province were collected, and their ZY1-02D hyperspectral remote sensing images were investigated. As input data, a feature band dataset was constructed using the Pearson correlation coefficient and successive projections algorithm. Based on the dataset, a new SOM estimation model under the stacking ensemble learning framework combined with heterogeneous models was developed by optimizing the combination of base and meta-learners. Finally, the spatial distribution map of SOM was plotted based on the result of the model over the study area. The result suggested that the input data quality of the estimation model is improved by constructing a feature band dataset. The multi-class ensemble learning estimation model with the combination strategy of the base and meta-learners has better predictive effects and stability than the single-algorithm and single-level ensemble models with homogeneous learners. The coefficient of determination is 0.829, the residual prediction deviation is 2.85, and the predictive set root mean square error is 1.953. The results can provide new ideas for estimating SOM content using hyperspectral images and ensemble learning algorithms, and serve as a reference for mapping large-scale SOM spatial distribution using space-borne hyperspectral images. Full article

(This article belongs to the Special Issue Application of Hyperspectral Imagery in Precision Agriculture)

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19 pages, 14731 KiB

Open AccessArticle

Quantitative Assessment of Apple Mosaic Disease Severity Based on Hyperspectral Images and Chlorophyll Content

by Yanfu Liu, Yu Zhang, Danyao Jiang, Zijuan Zhang and Qingrui Chang

Remote Sens. 2023, 15(8), 2202; https://doi.org/10.3390/rs15082202 - 21 Apr 2023

Cited by 17 | Viewed by 2216

Abstract

The infection of Apple mosaic virus (ApMV) can severely damage the cellular structure of apple leaves, leading to a decrease in leaf chlorophyll content (LCC) and reduced fruit yield. In this study, we propose a novel method that utilizes hyperspectral imaging (HSI) technology to non-destructively monitor ApMV-infected apple leaves and predict LCC as a quantitative indicator of disease severity. LCC data were collected from 360 ApMV-infected leaves, and optimal wavelengths were selected using competitive adaptive reweighted sampling algorithms. A high-precision LCC inversion model was constructed based on Boosting and Stacking strategies, with a validation set

R_{v}^{2}

of 0.9644, outperforming traditional ensemble learning models. The model was used to invert the LCC distribution image and calculate the average and coefficient of variation (CV) of LCC for each leaf. Our findings indicate that the average and CV of LCC were highly correlated with disease severity, and their combination with sensitive wavelengths enabled the accurate identification of disease severity (validation set overall accuracy = 98.89%). Our approach considers the role of plant chemical composition and provides a comprehensive evaluation of disease severity at the leaf scale. Overall, our study presents an effective way to monitor and evaluate the health status of apple leaves, offering a quantifiable index of disease severity that can aid in disease prevention and control. Full article

(This article belongs to the Special Issue Application of Hyperspectral Imagery in Precision Agriculture)

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28 pages, 10179 KiB

Open AccessArticle

Evaluation of Absolute Measurements and Normalized Indices of Proximal Optical Sensors as Estimators of Yield in Muskmelon and Sweet Pepper

by Cihan Karaca, Rodney B. Thompson, M. Teresa Peña-Fleitas, Marisa Gallardo and Francisco M. Padilla

Remote Sens. 2023, 15(8), 2174; https://doi.org/10.3390/rs15082174 - 20 Apr 2023

Cited by 2 | Viewed by 1247

Abstract

The generally established protocol for leaf measurement with proximal optical sensors is to use the most recently fully expanded leaf. However, differences in the nitrogen (N) status of lower and upper leaves could possibly be used to enhance optical sensor measurement. Normalized indices that consider both upper and lower leaves have been proposed to improve the assessment of crop N status and yield estimation. This study evaluated whether normalized indices improved the estimation of crop yield from measurements with three different proximal optical sensors: (i) SPAD-502 leaf chlorophyll meter, (ii) Crop Circle ACS 470 canopy reflectance sensor, and (iii) Multiplex fluorescence meter. The study was conducted with sweet pepper (Capsicum annuum L.) and muskmelon (Cucumis melo L.) in plastic greenhouses in Almeria, Spain. Measurements were made on the latest (most recent) leaf (L1), and the second (L2), third (L3) and fourth (L4) fully expanded leaves. Yield estimation models, using linear regression analysis, were developed and validated from the absolute and normalized measurements of the three optical sensors. Overall, the calibration and validation results indicated that the absolute measurements generally had better yield estimation performance than the normalized indices for all the leaves and different leaf profiles. In both species, there was a better performance at the early phenological stages, such as the vegetative and flowering stages, for the absolute and normalized indices for the three optical sensors. Absolute proximal optical sensor measurements on the lower leaves (L2, L3 and L4) slightly improved yield estimation compared to the L1 leaf. Normalized indices that included the L4 leaf (L1–L4) had better yield estimation compared to those using L2 and L3 (e.g., L1–L2 and L1–L3). Of the normalized indices evaluated, the yield performance of the Relative Index (RI), Relative Difference Index (RDI), and Normalized Difference Index (NDI) were very similar, and generally superior to the Difference Index (DI). Overall, the results of this study demonstrated that for three different proximal optical sensors in both muskmelon and sweet pepper (i) normalized indices did not improve yield estimation, and (ii) that absolute measurements on lower leaves (L2, L3 and L4) slightly improved yield estimation performance. Full article

(This article belongs to the Special Issue Application of Hyperspectral Imagery in Precision Agriculture)

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21 pages, 5239 KiB

Open AccessArticle

Forecasting Table Beet Root Yield Using Spectral and Textural Features from Hyperspectral UAS Imagery

by Mohammad S. Saif, Robert Chancia, Sarah Pethybridge, Sean P. Murphy, Amirhossein Hassanzadeh and Jan van Aardt

Remote Sens. 2023, 15(3), 794; https://doi.org/10.3390/rs15030794 - 31 Jan 2023

Cited by 1 | Viewed by 1521

Abstract

New York state is among the largest producers of table beets in the United States, which, by extension, has placed a new focus on precision crop management. For example, an operational unmanned aerial system (UAS)-based yield forecasting tool could prove helpful for the efficient management and harvest scheduling of crops for factory feedstock. The objective of this study was to evaluate the feasibility of predicting the weight of table beet roots from spectral and textural features, obtained from hyperspectral images collected via UAS. We identified specific wavelengths with significant predictive ability, e.g., we down-select >200 wavelengths to those spectral indices sensitive to root yield (weight per unit length). Multivariate linear regression was used, and the accuracy and precision were evaluated at different growth stages throughout the season to evaluate temporal plasticity. Models at each growth stage exhibited similar results (albeit with different wavelength indices), with the LOOCV (leave-one-out cross-validation) R² ranging from 0.85 to 0.90 and RMSE of 10.81–12.93% for the best-performing models in each growth stage. Among visible and NIR spectral regions, the 760–920 nm-wavelength region contained the most wavelength indices highly correlated with table beet root yield. We recommend future studies to further test our proposed wavelength indices on data collected from different geographic locations and seasons to validate our results. Full article

(This article belongs to the Special Issue Application of Hyperspectral Imagery in Precision Agriculture)

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