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Crop Disease Detection Using Remote Sensing Image Analysis II

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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 (29 February 2024) | Viewed by 20698

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Dr. Xanthoula Eirini Pantazi

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Department of Agricultural Engineering, Faculty of Agriculture, Aristotle University of Thessaloniki, Postal Box 275, 15424 Thessaloniki, Greece
Interests: artificial intelligence; biosystems engineering; automation; yield prediction; crop disease detection; weed management; remote sensing; data fusion; machine learning; deep learning; hyperspectral imaging; fluorescence kinetics
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Dear Colleagues,

The assessment of pest and crop disease severity and its real-time field distribution is regarded as valuable for the adoption and timing of relevant mitigation measures, such as fungicide applications. The types of sensors employed for crop disease detection are mainly based on their spectral, spatial, temporal resolution, radiometric and temporal resolution, and inevitably their relevant cost. Especially in occasions where iterative large-scale measurements are required, remote sensing technologies have been proven effective for data acquisition. The combination of remote sensing approaches with artificial intelligence (AI) techniques guarantees successful supervised and unsupervised image analysis, shaping critical tools for precision spraying application and decision making due to their efficiency in estimating the spatial spread of diseases and pests as well as their high performance in crop health monitoring.

This Special Issue aims to gather relevant research works on novel applications that employ remote sensing techniques for plant disease detection.

Multispectral imaging applications;
Hyperspectral imaging applications;
Thermal imaging applications;
Agricultural UAVs;
Satellite imagery;
Decision support systems for plant disease detection;
Machine learning for crop health status assessment;
Sensor fusion for crop health status assessment;
Smart farming applications for crop monitoring;
Artificial intelligence models;
Deep learning models for crop disease detection;
Precision agriculture applications for crop protection;
Fluorescence.

Dr. Xanthoula Eirini Pantazi
Guest Editor

Manuscript Submission Information

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Keywords

precision agriculture
crop health status determination
field spectroscopy
hyperspectral sensors
multispectral sensors
machine learning
deep learning
artificial intelligence
data mining
UAV

Published Papers (6 papers)

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Research

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22 pages, 15888 KiB

Open AccessArticle

Intelligent Identification of Pine Wilt Disease Infected Individual Trees Using UAV-Based Hyperspectral Imagery

by Haocheng Li, Long Chen, Zongqi Yao, Niwen Li, Lin Long and Xiaoli Zhang

Remote Sens. 2023, 15(13), 3295; https://doi.org/10.3390/rs15133295 - 27 Jun 2023

Cited by 3 | Viewed by 1528

Abstract

The pine wood nematode (PWN; Bursaphelenchus xylophilus) is a major invasive species in China, causing huge economic and ecological damage to the country due to the absence of natural enemies and the extremely rapid rate of infection and spread. Accurate monitoring of pine wilt disease (PWD) is a prerequisite for timely and effective disaster prevention and control. UAVs can carry hyperspectral sensors for near-ground remote sensing observations, which can obtain rich spatial and spectral information and have the potential for infected tree identification. Deep learning techniques can use rich multidimensional data to mine deep features in order to achieve tasks such as classification and target identification. Therefore, we propose an improved Mask R-CNN instance segmentation method and an integrated approach combining a prototypical network classification model with an individual tree segmentation algorithm to verify the possibility of deep learning models and UAV hyperspectral imagery for identifying infected individual trees at different stages of PWD. The results showed that both methods achieved good performance for PWD identification: the overall accuracy of the improved Mask R-CNN with the screened bands as input data was 71%, and the integrated method combining prototypical network classification model with individual tree segmentation obtained an overall accuracy of 83.51% based on the screened bands data, in which the early infected pine trees were identified with an accuracy of 74.89%. This study indicates that the improved Mask R-CNN and integrated prototypical network method are effective and practical for PWD-infected individual trees identification using UAV hyperspectral data, and the proposed integrated prototypical network enables early identification of PWD, providing a new technical guidance for early monitoring and control of PWD. Full article

(This article belongs to the Special Issue Crop Disease Detection Using Remote Sensing Image Analysis II)

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22 pages, 5710 KiB

Open AccessArticle

A Modified Temperature Vegetation Dryness Index (mTVDI) for Agricultural Drought Assessment Based on MODIS Data: A Case Study in Northeast China

by Rui Dai, Shengbo Chen, Yijing Cao, Yufeng Zhang and Xitong Xu

Remote Sens. 2023, 15(7), 1915; https://doi.org/10.3390/rs15071915 - 03 Apr 2023

Cited by 5 | Viewed by 2004

Abstract

Satellite-based drought indices have been shown to be effective and convenient in detecting drought conditions. The temperature vegetation dryness index (TVDI) is one of the most frequently used drought indices; however, it is not suitable for areas with high fractional vegetation cover (FVC). In this study, a modified temperature vegetation dryness index (mTVDI) was constructed by using the multispectral vegetation dryness index (MVDI) proposed by a PROSAIL simulation and water stress experiments which was based on the theory of the TVDI and utilized MODIS data. Compared with the TVDI, the mTVDI presents a more triangular feature space, as well as obviously increased R² values for dry and wet edges (from 0.37–0.90 to 0.53–0.91 for dry edges and from 0.00–0.77 to 0.24–0.80 for wet edges). The mTVDI was evaluated using standardized precipitation evapotranspiration indices (SPEIs), precipitation, potential evapotranspiration (PET), and the crop water deficit index (CWDI), and the results confirmed that the mTVDI can better reflect the actual spatial changes, compared to the TVDI, under high FVC, as well as presenting an increased Pearson correlation coefficient (by 0.06–0.10) when compared with SPEIs. Moreover, the good performance of the mTVDI in major drought events indicates its reliability and accuracy for drought monitoring. Overall, the mTVDI is a reliable and accurate satellite-based dryness index suitable for high FVC conditions. Full article

(This article belongs to the Special Issue Crop Disease Detection Using Remote Sensing Image Analysis II)

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19 pages, 3570 KiB

Open AccessArticle

Estimation of Anthocyanins in Leaves of Trees with Apple Mosaic Disease Based on Hyperspectral Data

by Zijuan Zhang, Danyao Jiang, Qingrui Chang, Zhikang Zheng, Xintong Fu, Kai Li and Haiyang Mo

Remote Sens. 2023, 15(7), 1732; https://doi.org/10.3390/rs15071732 - 23 Mar 2023

Cited by 4 | Viewed by 1636

Abstract

Anthocyanins are severity indicators for apple mosaic disease and can be used to monitor tree health. However, most of the current studies have focused on healthy leaves, and few studies have estimated the anthocyanin content in diseased leaves. In this study, we obtained the hyperspectral data of apple leaves with mosaic disease, analyzed the spectral characteristics of leaves with different degrees of Mosaic disease, constructed and screened the spectral index sensitive to anthocyanin content, and improved the estimation model. To improve the conciseness of the model, we integrated Variable Importance in Projection (VIP), Partial Least Squares Regression (PLSR), and Akaike Information Criterion (AIC) to select the optimal PLSR model and its independent variables. Sparrow Search Algorithm-Random Forest (SSA-RF) was used to improve accuracy. Results showed the following: (1) anthocyanin content increased gradually with the aggravation of disease. The reflectance of the blade spectrum in the visible band increased, the red edge moved to short wave, and the phenomenon of “blue shift of spectrum” occurred. (2) The VIP-PLSR-AIC selected 17 independent variables from 21 spectral indices. (3) Variables were used to construct PLSR, Back Propagation (BP), Support Vector Machine (SVM), Random Forest (RF), and SSA-RF to estimate anthocyanin content. Results showed the estimation accuracy and stability of the SSA-RF model were better than other models. The model set determination coefficient (

R^{2}

) was up to 0.955, which is 0.047 higher than that of the RF model and 0.138 higher than that of the SVM model with the lowest accuracy. The model was constructed at the leaf scale and can provide a reference for other scale studies, including a theoretical basis for large-area, high-efficiency, high-precision anthocyanin estimation and monitoring of apple mosaics using remote sensing technology. Full article

(This article belongs to the Special Issue Crop Disease Detection Using Remote Sensing Image Analysis II)

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Review

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29 pages, 1496 KiB

Open AccessReview

Recent Advances in Crop Disease Detection Using UAV and Deep Learning Techniques

by Tej Bahadur Shahi, Cheng-Yuan Xu, Arjun Neupane and William Guo

Remote Sens. 2023, 15(9), 2450; https://doi.org/10.3390/rs15092450 - 06 May 2023

Cited by 28 | Viewed by 8106

Abstract

Because of the recent advances in drones or Unmanned Aerial Vehicle (UAV) platforms, sensors and software, UAVs have gained popularity among precision agriculture researchers and stakeholders for estimating traits such as crop yield and diseases. Early detection of crop disease is essential to prevent possible losses on crop yield and ultimately increasing the benefits. However, accurate estimation of crop disease requires modern data analysis techniques such as machine learning and deep learning. This work aims to review the actual progress in crop disease detection, with an emphasis on machine learning and deep learning techniques using UAV-based remote sensing. First, we present the importance of different sensors and image-processing techniques for improving crop disease estimation with UAV imagery. Second, we propose a taxonomy to accumulate and categorize the existing works on crop disease detection with UAV imagery. Third, we analyze and summarize the performance of various machine learning and deep learning methods for crop disease detection. Finally, we underscore the challenges, opportunities and research directions of UAV-based remote sensing for crop disease detection. Full article

(This article belongs to the Special Issue Crop Disease Detection Using Remote Sensing Image Analysis II)

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24 pages, 3787 KiB

Open AccessReview

Plant Disease Diagnosis Using Deep Learning Based on Aerial Hyperspectral Images: A Review

by Lukas Wiku Kuswidiyanto, Hyun-Ho Noh and Xiongzhe Han

Remote Sens. 2022, 14(23), 6031; https://doi.org/10.3390/rs14236031 - 28 Nov 2022

Cited by 21 | Viewed by 5621

Abstract

Plant diseases cause considerable economic loss in the global agricultural industry. A current challenge in the agricultural industry is the development of reliable methods for detecting plant diseases and plant stress. Existing disease detection methods mainly involve manually and visually assessing crops for visible disease indicators. The rapid development of unmanned aerial vehicles (UAVs) and hyperspectral imaging technology has created a vast potential for plant disease detection. UAV-borne hyperspectral remote sensing (HRS) systems with high spectral, spatial, and temporal resolutions have replaced conventional manual inspection methods because they allow for more accurate cost-effective crop analyses and vegetation characteristics. This paper aims to provide an overview of the literature on HRS for disease detection based on deep learning algorithms. Prior articles were collected using the keywords “hyperspectral”, “deep learning”, “UAV”, and “plant disease”. This paper presents basic knowledge of hyperspectral imaging, using UAVs for aerial surveys, and deep learning-based classifiers. Generalizations about workflow and methods were derived from existing studies to explore the feasibility of conducting such research. Results from existing studies demonstrate that deep learning models are more accurate than traditional machine learning algorithms. Finally, further challenges and limitations regarding this topic are addressed. Full article

(This article belongs to the Special Issue Crop Disease Detection Using Remote Sensing Image Analysis II)

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Other

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13 pages, 3033 KiB

Open AccessTechnical Note

Use of Images Obtained by Remotely Piloted Aircraft and Random Forest for the Detection of Leaf Miner (Leucoptera coffeella) in Newly Planted Coffee Trees

by Luana Mendes dos Santos, Gabriel Araújo e Silva Ferraz, Nicole Lopes Bento, Diego Bedin Marin, Giuseppe Rossi, Gianluca Bambi and Leonardo Conti

Remote Sens. 2024, 16(4), 728; https://doi.org/10.3390/rs16040728 - 19 Feb 2024

Viewed by 646

Abstract

Brazil is the largest producer and exporter of coffee beans in the world. Given this relevance, it is important to monitor the crop to prevent attacks by pests. This study aimed to detect leaf miner (Leucoptera coffeella) infestation in a newly planted crop based on vegetation indices (VI) derived from aerial images obtained by a multispectral camera embedded in a remotely piloted aircraft (RPA) using random forest (RF). The study was conducted on the Cafua farm in the municipality of Lavras in southern Minas Gerais. The images were collected using a multispectral camera attached to a remotely piloted aircraft (RPA). Collections were carried out on 30 July 2019 (infested crop) and 16 December 2019 (post chemical control). The RF package in R software was used to classify the infested and healthy plants. The t test revealed significant differences in band means between healthy and infested plants, favouring higher means in healthy plants. VI also exhibited significant differences, with EXR being higher in infested plants and GNDVI, GOSAVI, GRRI, MPRI, NDI, NDRE, NDVI and SAVI showing higher averages in healthy plants, indicating distinct spectral responses and light absorption patterns between the two states of the plant. Due to the spectral differences between the classes, it was possible to classify the infested and healthy plants, and the RF algorithm performed very well. Full article

(This article belongs to the Special Issue Crop Disease Detection Using Remote Sensing Image Analysis II)

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