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Remote Sensing for Plant Pathology

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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 2021) | Viewed by 19325

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Dr. Salvatore Filippo Di Gennaro

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Institute of BioEconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy
Interests: precision agriculture; remote sensing; UAS applied to agriculture and forestry; wireless sensors network; UAS in field high throughput phenotyping; viticulture
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Dear Colleagues,

The world’s population is expected to reach 9 billion of people by 2050, and farming systems face serious pressure to double agricultural production. Sustainable food production therefore is a tremendous challenge for plant science and crop improvement. Within that scenario, plant diseases are a major source of yield reduction. In addition, the increasing impact of climate change and climate variability represents a relevant driver for plant disease epidemiology, influencing the occurrence, prevalence, and severity of plant diseases. Monitoring plant health status plays a key role in controlling diseases in agricultural crops that can cause tremendous damage to agricultural production, compromising yield and quality and as a consequence causing significant economic loss for farmers. Traditional disease management practices often assume that pathogens are spread homogeneously over cultivation areas. A precision agriculture approach based on disease spatial spread investigation and site-specific practices to optimize pest management leads to a reduction in pesticide use, cost, and ecological impact. Since the end of the 1960s, remote sensing has been used in plant disease detection with increasing frequency. Remote sensing techniques are replacing traditional methods in field or laboratory analysis when repetitive large-scale measurements are required, representing the only feasible approach for obtaining these data. Advances in imaging sensor technologies provide increasing spatial and spectral resolution performances that allow not only the detection of foliar symptoms (RGB and multispectral sensor) and provide a disease incidence distribution but also investigate early detection methodologies aimed to identify alterations in leaf optical proprieties due to specific diseases, which are still not perceptible by the human eye (hyperspectral sensor). In recent years, many studies of disease scouting and detection using optical sensors mounted on various platforms have been proposed, from on-the-go solutions to satellite, aircraft, and extremely high potential unmanned aerial vehicles (UAVs) both for ground resolution and highly flexible multitemporal scouting. At the same time, continuous improvement in computational machine power allows for the effective application of recent supervised and unsupervised image analysis approaches based on deep learning techniques in computer vision to improve the performance of plant disease detection. This Special Issue aims to collate manuscripts showcasing recent applications of remote sensing technological advances within the plant pathology topic in agriculture.

Dr. Salvatore Filippo Di Gennaro
Guest Editor

Manuscript Submission Information

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Published Papers (3 papers)

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Research

18 pages, 3501 KiB

Open AccessArticle

Detecting Infected Cucumber Plants with Close-Range Multispectral Imagery

by Claudio I. Fernández, Brigitte Leblon, Jinfei Wang, Ata Haddadi and Keri Wang

Remote Sens. 2021, 13(15), 2948; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152948 - 27 Jul 2021

Cited by 11 | Viewed by 2967

Abstract

This study used close-range multispectral imagery over cucumber plants inside a commercial greenhouse to detect powdery mildew due to Podosphaera xanthii. It was collected using a MicaSense^® RedEdge camera at 1.5 m over the top of the plant. Image registration was performed using Speeded-Up Robust Features (SURF) with an affine geometric transformation. The image background was removed using a binary mask created with the aligned NIR band of each image, and the illumination was corrected using Cheng et al.’s algorithm. Different features were computed, including RGB, image reflectance values, and several vegetation indices. For each feature, a fine Gaussian Support Vector Machines algorithm was trained and validated to classify healthy and infected pixels. The data set to train and validate the SVM was composed of 1000 healthy and 1000 infected pixels, split 70–30% into training and validation datasets, respectively. The overall validation accuracy was 89, 73, 82, 51, and 48%, respectively, for blue, green, red, red-edge, and NIR band image. With the RGB images, we obtained an overall validation accuracy of 89%, while the best vegetation index image was the PMVI-2 image which produced an overall accuracy of 81%. Using the five bands together, overall accuracy dropped from 99% in the training to 57% in the validation dataset. While the results of this work are promising, further research should be considered to increase the number of images to achieve better training and validation datasets. Full article

(This article belongs to the Special Issue Remote Sensing for Plant Pathology)

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21 pages, 2802 KiB

Open AccessArticle

Hyperspectral Measurements Enable Pre-Symptomatic Detection and Differentiation of Contrasting Physiological Effects of Late Blight and Early Blight in Potato

by Kaitlin M. Gold, Philip A. Townsend, Adam Chlus, Ittai Herrmann, John J. Couture, Eric R. Larson and Amanda J. Gevens

Remote Sens. 2020, 12(2), 286; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12020286 - 15 Jan 2020

Cited by 81 | Viewed by 9722

Abstract

In-vivo foliar spectroscopy, also known as contact hyperspectral reflectance, enables rapid and non-destructive characterization of plant physiological status. This can be used to assess pathogen impact on plant condition both prior to and after visual symptoms appear. Challenging this capacity is the fact that dead tissue yields relatively consistent changes in leaf optical properties, negatively impacting our ability to distinguish causal pathogen identity. Here, we used in-situ spectroscopy to detect and differentiate Phytophthora infestans (late blight) and Alternaria solani (early blight) on potato foliage over the course of disease development and explored non-destructive characterization of contrasting disease physiology. Phytophthora infestans, a hemibiotrophic pathogen, undergoes an obligate latent period of two–seven days before disease symptoms appear. In contrast, A. solani, a necrotrophic pathogen, causes symptoms to appear almost immediately when environmental conditions are conducive. We found that respective patterns of spectral change can be related to these differences in underlying disease physiology and their contrasting pathogen lifestyles. Hyperspectral measurements could distinguish both P. infestans-infected and A. solani-infected plants with greater than 80% accuracy two–four days before visible symptoms appeared. Individual disease development stages for each pathogen could be differentiated from respective controls with 89–95% accuracy. Notably, we could distinguish latent P. infestans infection from both latent and symptomatic A. solani infection with greater than 75% accuracy. Spectral features important for late blight detection shifted over the course of infection, whereas spectral features important for early blight detection remained consistent, reflecting their different respective pathogen biologies. Shortwave infrared wavelengths were important for differentiation between healthy and diseased, and between pathogen infections, both pre- and post-symptomatically. This proof-of-concept work supports the use of spectroscopic systems as precision agriculture tools for rapid and early disease detection and differentiation tools, and highlights the importance of careful consideration of underlying pathogen biology and disease physiology for crop disease remote sensing. Full article

(This article belongs to the Special Issue Remote Sensing for Plant Pathology)

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13 pages, 1071 KiB

Open AccessArticle

Quantifying Citrus Tree Health Using True Color UAV Images

by Blanca N. Garza, Veronica Ancona, Juan Enciso, Humberto L. Perotto-Baldivieso, Madhurababu Kunta and Catherine Simpson

Remote Sens. 2020, 12(1), 170; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010170 - 03 Jan 2020

Cited by 22 | Viewed by 4600

Abstract

Huanglongbing (HLB) and Phytophthora foot and root rot are diseases that affect citrus production and profitability. The symptoms and physiological changes associated with these diseases are diagnosed through expensive and time-consuming field measurements. Unmanned aerial vehicles (UAVs) using red/green/blue (RGB, true color) imaging, may be an economic alternative to diagnose diseases. A methodology using a UAV with a RGB camera was developed to assess citrus health. The UAV was flown in April 2018 on a grapefruit field infected with HLB and foot rot. Ten trees were selected for each of the following disease classifications: (HLB-, foot rot–), (HLB+, foot rot–), (HLB-, foot rot+) (HLB+, foot rot+). Triangular greenness index (TGI) images were correlated with field measurements such as tree nutritional status, leaf area, SPAD (leaf greenness), foot rot disease severity and HLB. It was found that 61% of the TGI differences could be explained by Na, Fe, foot rot, Ca, and K. This study shows that diseased citrus trees can be monitored using UAVs equipped with RGB cameras, and that TGI can be used to explain subtle differences in tree health caused by multiple diseases. Full article

(This article belongs to the Special Issue Remote Sensing for Plant Pathology)

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