Article
Environmental Sciences
Junichi Kurihara, Voon-Chet Koo, Cheaw Wen Guey, Yang Ping Lee, Haryati Abidin
Summary: This study successfully detected early-stage basal stem rot (BSR) disease in oil palm trees using hyperspectral imaging from an unmanned aerial vehicle (UAV) and machine learning algorithms. The results showed that specific tree crown segments and a few spectral bands in the red-edge region were sufficient for classifying the infection categories. These findings will be beneficial for future UAV-based multispectral imaging to efficiently detect BSR disease in oil palm plantations.
Article
Chemistry, Analytical
Canh Nguyen, Vasit Sagan, Matthew Maimaitiyiming, Maitiniyazi Maimaitijiang, Sourav Bhadra, Misha T. Kwasniewski
Summary: Early detection of grapevine viral diseases is crucial to prevent the spread of disease, and hyperspectral remote sensing can help achieve this by identifying important spectral features between infected and healthy vines. Disease-centric vegetation indices play a key role in classification, especially in selecting the feature space size, where support vector machine (SVM) and random forest (RF) classifiers each have their strengths.
Article
Food Science & Technology
Dedong Min, Jiangsan Zhao, Gernot Bodner, Maratab Ali, Fujun Li, Xinhua Zhang, Boris Rewald
Summary: Although fruits have health-promoting properties and benefits, they are susceptible to pathogen infection, leading to deterioration of fruit quality and economic losses. Hyperspectral imaging has been recognized as an effective and non-destructive approach for assessing fruit quality, especially in detecting early stages of fungal infection. However, challenges and future research are needed for its implementation in industrial sorting processes.
Article
Plant Sciences
Eunsoo Park, Yun-Soo Kim, Mohammad Akbar Faqeerzada, Moon S. Kim, Insuck Baek, Byoung-Kwan Cho
Summary: In this study, a non-destructive method for early detection of root rot in ginseng was developed using crop phenotyping and HSI technique to analyze biochemical information. The results showed that root rot caused a decrease in nutrient absorption, photosynthetic activity, and levels of carotenoids, starch, and sucrose. This technique can be used for early and non-destructive detection of root rot in ginseng.
FRONTIERS IN PLANT SCIENCE
(2023)
Review
Horticulture
Marton Szabo, Anna Csikasz-Krizsics, Terezia Dula, Eszter Farkas, Dora Roznik, Pal Kozma, Tamas Deak
Summary: The aim of this review is to provide readers with a comprehensive knowledge on black rot of grapes, based on a critical survey of scientific studies. It presents the current state and perspectives of science, including genetic determinants of grapevine resistance, predictive models of epidemics, and the potential of metabolomics in exploring black rot-grape interactions. The review highlights complications of disease management and ambiguities in phenotype-classification, provides insights into key dilemmas and controversial findings, and suggests future research directions.
Article
Environmental Sciences
Catello Pane, Gelsomina Manganiello, Nicola Nicastro, Francesco Carotenuto
Summary: A machine learning model based on hyperspectral data was constructed to monitor the progression of wilting disease in wild rocket. The model was able to detect infected plants with an average accuracy of 0.8, even a few days after infection.
Review
Plant Sciences
Rijad Saric, Viet D. Nguyen, Timothy Burge, Oliver Berkowitz, Martin Trtilek, James Whelan, Mathew G. Lewsey, Edhem Custovic
Summary: Our ability to manipulate the genome exceeds our capacity to measure genetic changes on plant traits. Plant scientists have been using imaging approaches, specifically hyperspectral imaging, to define plant responses to environmental conditions and optimize crop management.
TRENDS IN PLANT SCIENCE
(2022)
Article
Genetics & Heredity
Wenyu Zhang, Zicheng Wang, Zhencuo Dan, Lixia Zhang, Ming Xu, Guofeng Yang, Maofeng Chai, Zhenyi Li, Hongli Xie, Lili Cong
Summary: In this study, the differential gene expression between resistant and susceptible alfalfa clonal lines was analyzed after inoculation with Fusarium proliferatum L1. Many differentially expressed genes were found to be involved in plant-pathogen immune responses. Additionally, several transcription factor families related to pathogen infection were identified. These findings provide important insights into the resistance regulatory network of alfalfa against Fusarium root rot.
Article
Agriculture, Multidisciplinary
Yu Tang, Jiepeng Yang, Jiajun Zhuang, Chaojun Hou, Aimin Miao, Jinchang Ren, Huasheng Huang, Zhiping Tan, Jitendra Paliwal
Summary: This study proposes an early citrus anthracnose detection method using hyperspectral imaging and machine learning techniques. By extracting global and local spectral features and fusing them, the method achieves satisfactory results using classifiers such as SVM.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Review
Biodiversity Conservation
P. Mangalraj, Byoung-Kwan Cho
Summary: This article reviews the application of hyperspectral imaging technique in plant phenotyping, focusing on the estimation of Solar-induced fluorescence (SIF), correlation with other functional traits, and the use of machine learning techniques for interpreting SIF traits in agricultural monitoring. These areas have become crucial in recent trends, and the article provides insights into the breakthroughs in hyperspectral imaging for SIF estimation, enabling readers to deepen their understanding in the field of plant phenotyping and explore future research directions.
ECOLOGICAL INDICATORS
(2022)
Review
Computer Science, Artificial Intelligence
Harshita Mangotra, Sahima Srivastava, Garima Jaiswal, Ritu Rani, Arun Sharma
Summary: Hyperspectral Imaging (HSI) is an important optical imaging modality with applications in various industries. It is non-invasive, making it useful in the medical domain, particularly for early disease diagnosis. HSI combines imaging and spectroscopy properties to interpret data quickly and accurately. This study provides a comprehensive overview of HSI's applications in the medical industry, focusing on fast disease detection and surgical assistance. It explores the use of HSI combined with machine learning, deep learning, genetic algorithms, and anomaly detection for treating disorders. The study also discusses pre-processing approaches, performance metrics, inferences, and future prospects of HSI in the medical domain. It serves as a platform for computer vision specialists, machine learning researchers, doctors, and scientists to improve existing treatment methods for the betterment of society.
Article
Plant Sciences
Wen Ze Go, Kit Ling Chin, Paik San H'ng, Mui Yun Wong, Chuah Abdullah Luqman, Arthy Surendran, Geok Hun Tan, Chuan Li Lee, Pui San Khoo, Wai Jern Kong
Summary: This study investigated the virulence of different Rigidoporus microporus isolates obtained from infected rubber trees in Malaysia, with isolate RL21 from Sarawak showing the highest level of pathogenicity. The in vitro tests revealed that RL21 prefers weakly acidic to neutral environments and grows optimally at 25-30 degrees C. The findings emphasize the importance of selecting the most virulent isolate for effective control measures against R. microporus.
Article
Microbiology
Xuhong Song, Pengying Mei, Tao Dou, Qundong Liu, Longyun Li
Summary: Root rot disease significantly reduces the medicinal quality of Coptis chinensis. Different strategies were observed in the fibrous and taproot of C. chinensis in response to rot pathogen infection. Diaporthe eres, Fusarium avenaceum, and Fusarium solani were identified as causing different degrees of C. chinensis root rot, providing insights for further research on the resistance mechanism.
MICROBIOLOGY SPECTRUM
(2023)
Article
Computer Science, Interdisciplinary Applications
Sameer Aryal, ZhiQiang Chen, Shimin Tang
Summary: The study developed a mobile hyperspectral imaging system that captures multiple spectral reflectance values in the visible and near-infrared portion, utilizing machine learning for identifying different surface objects. Hyperspectral pixels, when combined with dimensionality reduction techniques, showed outstanding potential for automating damage inspection.
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
(2021)
Article
Plant Sciences
M. Nandakumar, P. Malathi, A. R. Sundar, C. P. Rajadurai, Manuel Philip, R. Viswanathan
Summary: The study identified specific roles of sugarcane microRNAs during compatible and incompatible interactions with the red rot pathogen Colletotrichum falcatum, shedding light on how these miRNAs regulate their gene targets and elucidating the molecular mechanism of sugarcane defense response to C. falcatum for the first time.
PLANT CELL REPORTS
(2021)
Article
Agronomy
Maximilian M. Muellender, Anne-Katrin Mahlein, Gerd Stammler, Mark Varrelmann
Summary: This study identified mutations associated with reduced sensitivity in C. beticola isolates collected from different European countries. The experiment showed that some mutations were related to lower EC50 values, indicating a possible correlation between target site mutations and reduced sensitivity.
PEST MANAGEMENT SCIENCE
(2021)
Article
Microbiology
Elias Alisaac, Anna Rathgeb, Petr Karlovsky, Anne-Katrin Mahlein
Summary: The study found that F. graminearum grows downward within infected wheat spikes and DON accumulation is largely confined to the colonized tissue. Additionally, F. graminearum was able to infect wheat kernels and cause mycotoxin contamination even when inoculated 25 days after anthesis.
Article
Plant Sciences
David Bohnenkamp, Jan Behmann, Stefan Paulus, Ulrike Steiner, Anne-Katrin Mahlein
Summary: This study established a hyperspectral library of wheat foliar diseases, detected important turning points using spectral changes, and achieved high accuracy in disease detection and identification through machine learning methods.
Article
Agronomy
Alan Storelli, Sebastian Kiewnick, Matthias Daub, Anne-Katrin Mahlein, Mario Schumann, Werner Beyer, Andreas Keiser
Summary: In European sugar beet production, different populations of Ditylenchus dipsaci show varying levels of damage, with the Seeland population having the highest reproduction rate on sugar beets. The reproduction rate of D. dipsaci at 60 dpi is negatively correlated with the fresh biomass of sugar beets. These findings can guide breeding programs for sugar beets resistance.
EUROPEAN JOURNAL OF PLANT PATHOLOGY
(2021)
Article
Environmental Sciences
Helen Thompson, Sarah Vaughan, Anne-Katrin Mahlein, Erwin Ladewig, Christine Kenter
Summary: This study analyzed the residues of neonicotinoids in soil and succeeding crops, finding that residue levels decreased over time and with lower application frequency. Residues in pollen and nectar were detected to be lower than reported adverse effect concentrations in studies with honeybee and bumble bee individuals, indicating low risk to pollinators.
INTEGRATED ENVIRONMENTAL ASSESSMENT AND MANAGEMENT
(2022)
Article
Agriculture, Multidisciplinary
Stefan Thomas, Jan Behmann, Uwe Rascher, Anne-Katrin Mahlein
Summary: Previous studies on the performance of transmission and reflection datasets for disease detection yielded inconsistent results, with reflection imaging showing superior detection capabilities compared to transmission imaging. The disparity in results may be linked to the different interactions between the pathogens and host plants, as well as the way light interacts with plant tissue. The study offers new insights into the nature of transmission-based hyperspectral imaging and its application range.
JOURNAL OF PLANT DISEASES AND PROTECTION
(2022)
Editorial Material
Agriculture, Multidisciplinary
Anne-Katrin Mahlein, Rene Hans-Juergen Heim, Anna Brugger, Kaitlin Gold, Yang Li, Ali Kashif Bashir, Stefan Paulus, Matheus Thomas Kuska
JOURNAL OF PLANT DISEASES AND PROTECTION
(2022)
Article
Plant Sciences
Abel Barreto, Facundo Ramon Ispizua Yamati, Mark Varrelmann, Stefan Paulus, Anne-Katrin Mahlein
Summary: A pipeline based on machine learning methods was established for image data analysis and extraction of disease-relevant parameters from UAV data, providing a more precise and nondestructive assessment of plant diseases.
Article
Plant Sciences
Anna Brugger, Facundo Ispizua Yamati, Abel Barreto, Stefan Paulus, Patrick Schramowsk, Kristian Kersting, Ulrike Steiner, Susanne Neugart, Anne-Katrin Mahlein
Summary: Fungal infections trigger changes in plant metabolites, which can be detected using destructive or nondestructive methods. This study compared the effects of two diseases, Cercospora leaf spot disease (CLS) and sugar beet rust (BR), on plant metabolites using both destructive analyses and nondestructive hyperspectral measurements in the UV range. The results showed distinct reflectance patterns and spectral changes in response to the two diseases, allowing for differentiation and recognition using machine learning algorithms. The study highlights the utility of nondestructive UV-range hyperspectral imaging in investigating plant diseases.
Article
Agriculture, Multidisciplinary
Roxana Hossain, Facundo Ramon Ispizua Yamati, Abel Barreto, Francesco Savian, Mark Varrelmann, Anne-Katrin Mahlein, Stefan Paulus
Summary: This study determined the spectral reflectance of leaves following TuYV inoculation in Nicotiana benthamiana, predicting virus infection and virus content groups using machine learning. Specific areas in the spectral signature were identified as important indicators of virus infection. These findings can help characterize metabolic or cytochemical changes after virus infection and provide valuable information for non-destructive monitoring of virus spread in plants.
JOURNAL OF PLANT DISEASES AND PROTECTION
(2023)
Article
Agronomy
Stefan Paulus, Benjamin Leiding
Article
Biology
Maurice Guender, Facundo R. Ispizua Yamati, Jana Kierdorf, Ribana Roscher, Anne-Katrin Mahlein, Christian Bauckhage
Summary: In this work, a hands-on workflow for the automatized temporal and spatial identification and individualization of crop images from UAVs is presented, improving the analysis and interpretation of UAV data in agriculture significantly. The results show that the approach has similar accuracy to more complex deep learning-based recognition techniques and can automate the processing of large datasets.
Review
Plant Sciences
Clive H. Bock, Sarah J. Pethybridge, Jayme G. A. Barbedo, Paul D. Esker, Anne-Katrin Mahlein, Emerson M. Del Ponte
Summary: Phytopathometry is a critical branch of plant pathology that focuses on estimating and measuring the amount of plant disease, playing a crucial role in analyzing yield loss, breeding disease-resistant plants, evaluating disease control methods, understanding pathogen ecology, and more. It is essential for a unified cross-discipline approach to research and application of tools in phytopathometry.
TROPICAL PLANT PATHOLOGY
(2022)