In-field detection of Alternaria solani in potato crops using hyperspectral imaging

Vijver, Ruben Van De; Mertens, Koen; Heungens, Kurt; Somers, Ben; Nuyttens, David; Borra‐Serrano, Irene; Lootens, Peter; Roldán‐Ruiz, Isabel; Vangeyte, Jürgen; Saeys, Wouter

doi:10.1016/j.compag.2019.105106

Cited by 61 publications

(40 citation statements)

References 28 publications

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“…The model TOM‐CAST was also derived from FAST as a weather‐timed fungicide spray forecast, and its potential to reduce spray applications has been tested. Many papers on the detection of EB by hyperspectral images have been published 133–137 . The wavelengths of 715 133 and 750 nm, 133 which belong to the range of near‐infrared, are the most discriminative range of the spectrum for disease classification.…”

Section: Practical Strategiesmentioning

confidence: 99%

“…Many papers on the detection of EB by hyperspectral images have been published. 133 , 134 , 135 , 136 , 137 The wavelengths of 715 133 and 750 nm, 133 which belong to the range of near‐infrared, are the most discriminative range of the spectrum for disease classification. Different algorithms based on deep‐learning and machine‐learning are applied for image processing techniques, and the accuracy of the models for disease detection of EB and LB in tomato leaves varies between 76% and 98%.…”

Section: Practical Strategiesmentioning

confidence: 99%

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Review: Holistic pest management against early blight disease towards sustainable agriculture

Jindo

Evenhuis

Kempenaar

et al. 2021

Pest Management Science

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Alternaria species are well‐known aggressive pathogens that are widespread globally and warmer temperatures caused by climate change might increase their abundance more drastically. Early blight (EB) disease, caused mainly by Alternaria solani, and brown spot, caused by Alternaria alternata, are major concerns in potato, tomato and eggplant production. The development of EB is strongly linked to varieties, crop development stages, environmental factors, cultivation and field management. Several forecasting models for pesticide application to control EB were created in the last century and more recent scientific advances have included modern breeding technology to detect resistant genes and precision agriculture with hyperspectral sensors to pinpoint damage locations on plants. This paper presents an overview of the EB disease and provides an evaluation of recent scientific advances to control the disease. First of all, we describe the outline of this disease, encompassing biological cycles of the Alternaria genus, favorite climate and soil conditions as well as resistant plant species. Second, versatile management practices to minimize the effect of this pathogen at field level are discussed, covering their limitations and pitfalls. A better understanding of the underlying factors of this disease and the potential of novel research can contribute to implementing integrated pest management systems for an ecofriendly farming system. © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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Section: Practical Strategiesmentioning

confidence: 99%

Section: Practical Strategiesmentioning

confidence: 99%

Review: Holistic pest management against early blight disease towards sustainable agriculture

Jindo

Evenhuis

Kempenaar

et al. 2021

Pest Management Science

View full text Add to dashboard Cite

show abstract

“…The reviewed works prove the possibility of detecting oil palm [36,[50][51][52][53][54][55][56][57][63][64][65][66], citrus [73][74][75][76][77][78], Solanaceae family crops [91][92][93][94][95][96][97][98][99][100][101][102][103] and wheat [24,[124][125][126][127][128][129][130][131][132][140][141][142][143][144][145] diseases using HRS.…”

Section: Discussionmentioning

confidence: 99%

“…Griffel et al used PLS-DA and SVM classification method to achieve 89.9% accuracy in PVY detection [94]. Van De Vijver et al studied early blight caused by Alternaria solani in the Bintje potatoe variety with spectral analysis and reached up to 92% accuracy [95]. Abdulridha et al studied yellow leaf curl, target spot and bacterial spot in tomato leaves of the Charger and FL-47 cultivars in field and laboratory conditions, using different vegetation indexes, and obtained accuracies of 94-100% for determining different diseases from each other and 98-100% for determining healthy from diseased plants [96,97].…”

Section: Hyperspectral Remote Sensing Of Solanaceae Plant Diseasesmentioning

confidence: 99%

Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review

Terentev

Долженко

Fedotov

et al. 2022

Sensors

129

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The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants’ disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.

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“…usando a técnica de imagem hiperespectral e algoritmos de aprendizado de máquina para detecção de sintomas do vírus Tomato spotted wilt virus (TSWV) em tabaco, mostraram que a região espectral do infravermelho próximo foi importante para a diferenciação de folhas infectadas e folhas saudáveis. Da mesma forma queVijver et al (2019), também evidenciaram que foi a mais discriminativa para detecção das lesões de Alternaria solani em culturas de batata, por plataforma de detecção proximal.2.4 Aprendizado de MáquinaO Aprendizado de Máquina (AM) é o campo científico que atribui às máquinas a capacidade de aprender sem ser estritamente programado(SAMUEL, 2000). É uma ferramenta importante e frequentemente aplicada para a interpretação e análise de dados de Sensoriamento Remoto (SR), com metodologias diversas (SCHEUNDERS; TUIA; MOSER,2018).As tarefas de AM são normalmente classificadas em diferentes categorias, como o tipo de aprendizagem (ex.…”

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Detecção de podridão mole em alface por Pectobacterium carotovorum subsp. carotovorum por algoritmos de aprendizado de máquina a partir de imagens multiespectrais

Carmo¹

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Detecção de podridão mole em alface por Pectobacterium carotovorum subsp. carotovorum por algoritmos de aprendizado de máquina a partir de imagens multiespectrais [recurso eletrônico] / Glecia Júnia dos Santos Carmo. -2021. Referência: Processo nº 23117.046444/2021-12 SEI nº 2905285 AGRADECIMENTOS À Deus Uno e Trino, à Santíssima Virgem Maria e ao meu Anjo da Guarda. Aos meus pais, Geni dos Santos Carmo e Fernando Aparecido do Carmo que me auxiliaram como puderam. À Professora Dr a . Renata Castoldi pela orientação e dedicação, que não mediu esforços para a realização deste trabalho. Ao Professor Dr. George Deroco Martins pela orientação e ensinamentos para o desenvolvimento da pesquisa. À Professora Dr a . Nilvanira Donizete Tebaldi por fornecer o isolado Pectobacterium carotovorum subsp. carotovorum UFU A7. Ao servidor e Ms. Renan Zampiroli pela realização dos voos. Ao grupo de pesquisa NUPOL (Núcleo de Pesquisa em Olericultura) que me auxiliou na condução do experimento. Ao senhor José Marques Vilela da Estação Experimental de Hortaliças da UFU e ao operador de máquinas Ivan dos Santos Vieira, pela prestatividade. Aos técnicos dos laboratórios LAMIF, LBIO, LAFIT e ENGAS pela disponibilidade de equipamentos e/ou espaço. Gratidão a todos!

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In-field detection of Alternaria solani in potato crops using hyperspectral imaging

Cited by 61 publications

References 28 publications

Review: Holistic pest management against early blight disease towards sustainable agriculture

Review: Holistic pest management against early blight disease towards sustainable agriculture

Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review

Detecção de podridão mole em alface por Pectobacterium carotovorum subsp. carotovorum por algoritmos de aprendizado de máquina a partir de imagens multiespectrais

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