Prediction model for assessing powdery mildew disease in common Oat (Avena sativa L.)

Bhardwaj, Nitish Rattan; Banyal, D. K.; Roy, A. K.

doi:10.1016/j.cropro.2021.105677

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…Air temperature is decisive for plant development and productivity, acting as a catalyst for biological processes, which is why plants require a minimum and maximum temperature for normal physiological activities (Marolli et al, 2017). Air temperature, together with relative humidity, directly affects the infection and development of the main foliar diseases that affect oat cultivation (Bhardwaj, Banyal and Roy, 2021).…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in the Simulation of Fungicide Management Scenarios for Satisfactory Yield and Food Safety in oat Crops

Rosa¹,

Dornelles²,

Silva³

et al. 2023

RGSA

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Purpose: The objective of the study is to analyze the management of the fungicide that seeks a longer interval between the last application and the harvest of oat grains, with an indication of the cultivars with the highest satisfactory productivity without application in the grain filling. Validate artificial neural network models in the expectation of satisfactory productivity with food security, through the interaction between management, pathogen, genotype, and environment. Method/design/approach: The study was conducted in 2015, 2016, 2017, in a randomized block design, in a 22 x 4 factorial scheme, for 22 white oat cultivars (recommended and no longer present in the current Brazilian recommendation) and 4 fungicide application conditions (no application; one application at 60 days after emergence (DAE); two applications, 60 and 75 DAE; and three applications, at 60, 75, 90 DAE), with three repetitions. Results and conclusion: The oat cultivars URS Altiva, URS, Guará, URS Charrua, FAEM 4 Carlasul, IPR Aphrodite, and UPFPS Farroupilha showed satisfactory yields in absence of fungicide applications. URS Altiva, FAEM 4 Carlasul, and IPR Aphrodite showed significant yield increases with fungicide application before grain filling. Artificial neural networks are efficient in predicting productivity and are a reliable alternative for simulating scenarios to validate more sustainable management practices. Originality/value: An unprecedented study seeking more sustainable managements to reduce the use of fungicides in the control of oat diseases and development of simulation models by artificial intelligence, providing opportunities for analysis of different scenarios of the complex interaction plant, pathogen, and environment.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in the Simulation of Fungicide Management Scenarios for Satisfactory Yield and Food Safety in oat Crops

Rosa¹,

Dornelles²,

Silva³

et al. 2023

RGSA

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“…Em virtude do acelerado progresso dos patógenos, as doenças foliares não são satisfatoriamente controladas pela resistência genética, sendo a utilização de fungicida a forma mais ecaz [3,9]. No cultivo da aveia, a variação das condições meteorológicas e o manejo do fungicida são fatores que afetam o desenvolvimento das principais doenças foliares e o rendimento da cultura [5]. As redes neurais articias podem contribuir na simulação eciente do progresso de doenças foliares da aveia e contribuir em estratégias à redução de uso de agrotóxicos na aveia direcionada a alimentação humana.…”

Section: Introductionunclassified

Simulação do Progresso de doenças Foliares da Aveia por Redes Neurais Artificiais à Redução de Uso de Agrotóxicos

Peter,

Alessi,

Rosa

et al. 2023

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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Resumo. Uma ferramenta que possui aplicabilidade em diversas áreas e representa uma possibilidade eciente de simulação e otimização é a modelagem computacional. As redes neurais articias podem contribuir na simulação eciente do progresso de doenças foliares da aveia e contribuir em estratégias à redução de uso de agrotóxicos na aveia direcionada a alimentação humana. O objetivo do estudo é simular por redes neurais articias o progresso de doenças foliares de aveia envolvendo o ciclo de desenvolvimento, variáveis meteorológicas e o número de aplicações de fungicida. O delineamento experimental foi de blocos casualizados em esquema fatorial 3 x 5 para 3 cultivares de aveia branca e 5 condições de aplicação de fungicida, respectivamente, com três repetições. As redes neurais articiais mostraram alta capacidade de aprendizado na expressão da área foliar necrosada e simulam com eciência o progresso das doenças foliares ao longo do ciclo da aveia, oportunizando direcionar manejos que reduzam o uso do agrotóxico fungicida na garantia de produtividade com segurança alimentar.Palavras-chave. Avena sativa L., segurança alimentar, modelagem, inteligência articial

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“…Based on the random forest (RF) and Maxent algorithms, a forecasting accuracy of 76% is achieved. Besides, Bhardwaj et al [4] established a forecasting model between temperature, relative humidity, sunshine, rainfall, and the incidence of powdery mildew on oats with the LR method.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Alternaria Leaf Spot in Apple with Spatial-Temporal Meteorological and Mobile Internet-Based Disease Survey Data

Huang

Zhang

et al. 2022

Agronomy

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Early warning of plant diseases and pests is critical to ensuring food safety and production for economic crops. Data sources such as the occurrence, frequency, and infection locations are crucial in forecasting plant diseases and pests. However, at present, acquiring such data relies on fixed-point observations or field experiments run by agricultural institutions. Thus, insufficient data and low rates of regional representative are among the major problems affecting the performance of forecasting models. In recent years, the development of mobile internet technology and conveniently accessible multi-source agricultural information bring new ideas to plant diseases’ and pests’ forecasting. This study proposed a forecasting model of Alternaria Leaf Spot (ALS) disease in apple that is based on mobile internet disease survey data and high resolution spatial-temporal meteorological data. Firstly, a mobile internet-based questionnaire was designed to collect disease survey data efficiently. A specific data clean procedure was proposed to mitigate the noise in the data. Next, a sensitivity analysis was performed on the temperature and humidity data, to identify disease-sensitive meteorological factors as model inputs. Finally, the disease forecasting model of the apple ALS was established using four machine learning algorithms: Logistic regression(LR); Fisher linear discriminant analysis(FLDA); Support vector machine(SVM); and K-Nearest Neighbors (KNN). The KNN algorithm is recommended in this study, which produced an overall accuracy of 88%, and Kappa of 0.53. This paper shows that through mobile internet disease survey and a proper data clean approach, it is possible to collect necessary data for disease forecasting in a short time. With the aid of high resolution spatial-temporal meteorological data and machine learning approaches, it is able to achieve disease forecast at a regional scale, which will facilitate efficient disease prevention practices.

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Prediction model for assessing powdery mildew disease in common Oat (Avena sativa L.)

Cited by 5 publications

References 21 publications

Artificial Intelligence in the Simulation of Fungicide Management Scenarios for Satisfactory Yield and Food Safety in oat Crops

Artificial Intelligence in the Simulation of Fungicide Management Scenarios for Satisfactory Yield and Food Safety in oat Crops

Simulação do Progresso de doenças Foliares da Aveia por Redes Neurais Artificiais à Redução de Uso de Agrotóxicos

Forecasting Alternaria Leaf Spot in Apple with Spatial-Temporal Meteorological and Mobile Internet-Based Disease Survey Data

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