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Agricultural Applications Using Hyperspectral Data

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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 October 2023) | Viewed by 9978

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Special Issue Editors

Dr. María Luisa Pérez-Bueno

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Guest Editor

Department of Plant Physiology, University of Granada, Granada, Spain
Interests: plant phenotyping; chlorophyll fluorescence; hyperspectral reflectance; thermography; plant stress detection
Special Issues, Collections and Topics in MDPI journals

Dr. Gregorio Egea

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Guest Editor

School of Agricultural Engineering, University of Sevilla, Ctra. Utrera Km 1, 41013 Seville, Spain
Interests: irrigation management; deficit irrigation; precision agriculture; crop monitoring; crop modelling; plant phenotyping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agricultural and food science face great challenges imposed by climate change in the context of a growing population. In the future, global warming is set to undermine the yield and quality of crops, while more socially, economically and environmentally sustainable agriculture and forestry is strongly demanded by the society and governmental policies.

Hyperspectral imaging, combined with other imaging techniques, is a promising technique that could provide efficient methods for monitoring physiological and morphological status of crops or natural environments on a space and time scale. Furthermore, the control and monitoring of agricultural during postharvest could benefit from the implementation of hyperspectral imaging, contributing to the development of precision agriculture.

In this Special Issue, we encourage the submission of original research articles and reviews showing the state-of-the-art advances in:

Agricultural crop assessment;
Vegetation health monitoring;
Plant disease detection;
Detection of nutritional deficiencies;
Plant phenotyping;
Species detection;
Crop yield prediction and quality;
Field-level recommendations.

Dr. María Luisa Pérez-Bueno
Dr. Gregorio Egea
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

hyperspectral imaging
precision agriculture
remote sensing
machine learning
plant imaging phenotyping
vegetation health monitoring

Published Papers (6 papers)

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Research

18 pages, 3009 KiB

Open AccessArticle

Early Detection of Dicamba and 2,4-D Herbicide Drifting Injuries on Soybean with a New Spatial–Spectral Algorithm Based on LeafSpec, an Accurate Touch-Based Hyperspectral Leaf Scanner

by Zhongzhong Niu, Julie Young, William G. Johnson, Bryan Young, Xing Wei and Jian Jin

Remote Sens. 2023, 15(24), 5771; https://doi.org/10.3390/rs15245771 - 17 Dec 2023

Viewed by 1141

Abstract

In soybeans, off-target damage from the use of dicamba and 2,4-D herbicides for broadleaf weed control can significantly impact sensitive vegetation and crops. The early detection and assessment of such damage are critical for plant diagnostic labs and regulatory agencies to inform regulated usage policies. However, the existing technologies that calculate the average spectrum often struggle to detect and differentiate the damage caused by these herbicides, as they share a similar mode-of-action. In this study, a high-precision spatial and spectral imaging solution was tested for the early detection of dicamba and 2,4-D-induced damage in soybeans. A 2021 study was conducted using LeafSpec, a touch-based hyperspectral leaf scanner, to detect damage on soybean leaves. VIS-NIR (visible–near infrared) hyperspectral images were captured from 180 soybean plants exposed to nine different herbicide treatments at different intervals after spraying. Leaf damage was distinguished as early as 2 h after treatment (HAT) using pairwise partial least squares discriminant analysis (PLS-DA) models based on spectral data. Leaf color distribution, texture, and morphological features were analyzed to separate herbicide dosages. By fully exploiting the spatial and spectral information from high-resolution hyperspectral images, classification accuracy was improved from 57.4% to over 80% for all evaluation dates. This work demonstrates the potential and advantages of using spectral and spatial features of LeafSpec hyperspectral images for the early and accurate detection of herbicide damage in soybean plants. Full article

(This article belongs to the Special Issue Agricultural Applications Using Hyperspectral Data)

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14 pages, 2085 KiB

Open AccessArticle

Hyperspectral Imaging of Adaxial and Abaxial Leaf Surfaces for Rapid Assessment of Foliar Nutrient Concentrations in Hass Avocado

by Nimanie S. Hapuarachchi, Stephen J. Trueman, Wiebke Kämper, Michael B. Farrar, Helen M. Wallace, Joel Nichols and Shahla Hosseini Bai

Remote Sens. 2023, 15(12), 3100; https://doi.org/10.3390/rs15123100 - 13 Jun 2023

Cited by 1 | Viewed by 1129

Abstract

Rapid assessment tools are required for monitoring crop nutrient status and managing fertiliser applications in real time. Hyperspectral imaging has emerged as a promising assessment tool to manage crop nutrition. This study aimed to determine the potential of hyperspectral imaging for predicting foliar nutrient concentrations in avocado trees and establish whether imaging different sides of the leaves affects prediction accuracy. Hyperspectral images (400–1000 nm) were taken of both surfaces of leaves collected from Hass avocado trees 0, 6, 10 and 28 weeks after peak anthesis. Partial least squares regression (PLSR) models were developed to predict mineral nutrient concentrations using images from (a) abaxial surfaces, (b) adaxial surfaces and (c) combined images of both leaf surfaces. Modelling successfully predicted foliar nitrogen (R_P² = 0.60, RPD = 1.61), phosphorus (R_P² = 0.71, RPD = 1.90), aluminium (R_P² = 0.88, RPD = 2.91), boron (R_P² = 0.63, RPD = 1.67), calcium (R_P² = 0.88, RPD = 2.86), copper (R_P² = 0.86, RPD = 2.76), iron (R_P² = 0.81, RPD = 2.34), magnesium (R_P² = 0.87, RPD = 2.81), manganese (R_P² = 0.87, RPD = 2.76) and zinc (R_P² = 0.79, RPD = 2.21) concentrations from either the abaxial or adaxial surface. Foliar potassium concentrations were predicted successfully only from the adaxial surface (R_P² = 0.56, RPD = 1.54). Foliar sodium concentrations were predicted successfully (R_P² = 0.59, RPD = 1.58) only from the combined images of both surfaces. In conclusion, hyperspectral imaging showed great potential as a rapid assessment tool for monitoring the crop nutrition status of avocado trees, with adaxial surfaces being the most useful for predicting foliar nutrient concentrations. Full article

(This article belongs to the Special Issue Agricultural Applications Using Hyperspectral Data)

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16 pages, 2275 KiB

Open AccessArticle

The Estimation of Maize Grain Protein Content and Yield by Assimilating LAI and LNA, Retrieved from Canopy Remote Sensing Data, into the DSSAT Model

by Bingxue Zhu, Shengbo Chen, Zhengyuan Xu, Yinghui Ye, Cheng Han, Peng Lu and Kaishan Song

Remote Sens. 2023, 15(10), 2576; https://doi.org/10.3390/rs15102576 - 15 May 2023

Cited by 4 | Viewed by 1425

Abstract

The assimilation of remote sensing data into mechanistic models of crop growth has become an available method for estimating yield. The objective of this study was to explore an effective assimilation approach for estimating maize grain protein content and yield using a canopy remote sensing data and crop growth model. Based on two years of field experiment data, the remote sensing inversion model using assimilation intermediate variables, namely leaf area index (LAI) and leaf nitrogen accumulation (LNA), was constructed with an R² greater than 0.80 and a low root-mean-square error (RMSE). The different data assimilation approaches showed that when the LAI and LNA variables were used together in the assimilation process (V_LAI+LNA), better accuracy was achieved for LNA estimations than the assimilation process using single variables of LAI or LNA (V_LAI or V_LNA). Similar differences in estimation accuracy were found in the maize yield and grain protein content (GPC) simulations. When the LAI and LNA were both intermediate variables in the assimilation process, the estimation accuracy of the yield and GPC were better than that of the assimilation process with only one variable. In summary, these results indicate that two physiological and biochemical parameters of maize retrieved from hyperspectral data can be combined with the crop growth model through the assimilation method, which provides a feasible method for improving the estimation accuracy of maize LAI, LNA, GPC and yield. Full article

(This article belongs to the Special Issue Agricultural Applications Using Hyperspectral Data)

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19 pages, 7609 KiB

Open AccessArticle

Monitoring the Degree of Mosaic Disease in Apple Leaves Using Hyperspectral Images

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

Remote Sens. 2023, 15(10), 2504; https://doi.org/10.3390/rs15102504 - 10 May 2023

Viewed by 1447

Abstract

Mosaic of apple leaves is a major disease that reduces the yield and quality of apples, and monitoring for the disease allows for its timely control. However, few studies have investigated the status of apple pests and diseases, especially mosaic diseases, using hyperspectral imaging technology. Here, hyperspectral images of healthy and infected apple leaves were obtained using a near-ground imaging high spectrometer and the anthocyanin content was measured simultaneously. The spectral differences between the healthy and infected leaves were analyzed. The content of anthocyanin in the leaves was estimated by the optimal model to determine the degree of apple mosaic disease. The leaves exhibited stronger reflectance at a range of 500–560 nm as the degree of disease increased. The correlation between the spectral reflectance processed by the Gaussian1 wavelet transform and anthocyanin was significantly improved compared to the corresponding correlation results with the original spectrum. The VPs-XGBoost anthocyanin estimation model performed the best, which was sufficient to monitor the degree of the disease. The findings provide theoretical support for the quantitative estimation of leaf anthocyanin content by remote sensing to monitor the degree of disease; they lay the foundation for large-scale monitoring of the degree of apple mosaic disease by remote sensing. Full article

(This article belongs to the Special Issue Agricultural Applications Using Hyperspectral Data)

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17 pages, 3098 KiB

Open AccessArticle

Multispectral UAV-Based Monitoring of Leek Dry-Biomass and Nitrogen Uptake across Multiple Sites and Growing Seasons

by Jérémie Haumont, Peter Lootens, Simon Cool, Jonathan Van Beek, Dries Raymaekers, Eva Ampe, Tim De Cuypere, Onno Bes, Jonas Bodyn and Wouter Saeys

Remote Sens. 2022, 14(24), 6211; https://doi.org/10.3390/rs14246211 - 08 Dec 2022

Cited by 2 | Viewed by 1681

Abstract

Leek farmers tend to apply too much nitrogen fertilizer as its cost is relatively low compared to the gross value of leek. Recently, several studies have shown that proximal sensing technologies could accurately monitor the crop nitrogen content and biomass. However, their implementation is impeded by practical limitations and the limited area they can cover. UAV-based monitoring might alleviate these issues. Studies on UAV-based vegetable crop monitoring are still limited. Because of the economic importance and environmental impact of leeks in Flanders, this study aimed to investigate the ability of UAV-based multispectral imaging to accurately monitor leek nitrogen uptake and dry biomass across multiple fields and seasons. Different modelling approaches were tested using twelve spectral VIs and the interquartile range of each of these VIs within the experimental plots as predictors. In a leave-one-flight out cross-validation (LOF-CV), leek dry biomass (DBM) was most accurately predicted using a lasso regression model (RMSE_ct = 6.60 g plant⁻¹, R²= 0.90). Leek N-uptake was predicted most accurately by a simple linear regression model based on the red wide dynamic range (RWDRVI) (RMSE_ct = 0.22 gN plant⁻¹, R² = 0.85). The results showed that randomized Kfold-CV is an undesirable approach. It resulted in more consistent and lower RMSE values during model training and selection, but worse performance on new data. This would be due to information leakage of flight-specific conditions in the validation data split. However, the model predictions were less accurate for data acquired in a different growing season (DBM: RMSEP = 8.50 g plant⁻¹, R² = 0.77; N-uptake: RMSEP = 0.27 gN plant⁻¹, R² = 0.68). Recalibration might solve this issue, but additional research is required to cope with this effect during image acquisition and processing. Further improvement of the model robustness could be obtained through the inclusion of phenological parameters such as crop height. Full article

(This article belongs to the Special Issue Agricultural Applications Using Hyperspectral Data)

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Graphical abstract

22 pages, 3844 KiB

Open AccessArticle

Detection of Peanut Leaf Spot Disease Based on Leaf-, Plant-, and Field-Scale Hyperspectral Reflectance

by Qiang Guan, Kai Song, Shuai Feng, Fenghua Yu and Tongyu Xu

Remote Sens. 2022, 14(19), 4988; https://doi.org/10.3390/rs14194988 - 07 Oct 2022

Cited by 5 | Viewed by 2148

Abstract

Leaf spot (LS) caused by Cercosporidium personatum is one of the most harmful peanut diseases in the late growth stage and severely affects the yield of peanuts. Hyperspectral disease detection technology is efficient, objective, and accurate and is suitable for large-scale crop management practices. To establish a multi-scale spectral index (SI) with high accuracy and stability for the detection of peanut LS disease, the spectral reflectance of different disease severity levels at leaf, plant, and field scales was collected, and the difference in wavelength caused by disease severity was analyzed using the mean, variance, and dispersion matrix of hyperspectral reflectance. Meanwhile, the feature weights at different scales were obtained using Relief-F, and the average feature weights identified 540, 660, and 770 nm as multi-scale sensitive wavelengths. Three new SIs were constructed by combining single, ratiometric, and normalized wavelengths. The new SIs were compared and analyzed with 35 commonly used SIs by correlation analysis and M-statistic values, and 6 SIs were significantly correlated with disease severity levels and had good separability. Finally, k-nearest neighbor (KNN) and multinomial logistic regression (MLR) were used to evaluate the ability of the above SIs to detect LS severity. The results showed that the leaf spot multi-scale spectral index (LS-MSSI) constructed in this study was superior to the other SIs and obtained high accuracy at different scales simultaneously. At the leaf and plant scales, the MLR obtained high accuracy, with the overall accuracy (OA) reaching 93.77% and 92.50% and Kappa reaching 91.59% and 89.97%, respectively. At the field scale, the KNN obtained high accuracy, with the OA and Kappa reaching 90.29% and 87.04%, respectively. The LS-MSSI proposed in this study has high accuracy, stability, and robustness in the detection of LS severity at multiple scales, providing a technical basis and scientific guidance for the detection and precise management of peanuts. Full article

(This article belongs to the Special Issue Agricultural Applications Using Hyperspectral Data)

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