Application of prediction models of asian soybean rust in two crop seasons, in Londrina, Pr

Igarashi, Wagner; França, José Alexandre de; Silva, Marcelo Augusto de Aguiar e; Igarashi, Seiji; Saab, Otavio Jorge Grigoli Abi

doi:10.5433/1679-0359.2016v37n5p2881

Cited by 11 publications

(10 citation statements)

References 8 publications

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“…The best models for Paraná state describes were represented by the average quarterly values of the climate variability index SST Niño 3.4 Season (R 2 = 0.87). The 3D models are useful to explore the relationship between variables and to define predictive variables of major importance (Canteri & Godoy 2005;Igarashi, França, Aguiar e Silva, Igarashi, & Abi Saab, 2016).…”

Section: Statistics and Data Analysismentioning

confidence: 99%

Predicting Asian Soybean Rust Epidemics Based on Off-Season Occurrence and El Niño Southern Oscillation Phenomenon in Paraná and Mato Grosso States, Brazil

Minchio¹,

Fantin²,

Caviglione³

et al. 2018

JAS

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The study aimed to propose models to predict Asian soybean rust epidemics based on both the occurrence of the disease in the period between seasons and the climate variability index, which is influenced by the El Niño Southern Oscillation (ENSO) phenomenon. The data used to develop these models were obtained from 11 crop seasons, distributed among six regions of Paraná and twelve regions of Mato Grosso which was determined by the National Institute for Space Research (INPE). The three-dimensional model was obtained from linear and quadratic polynomial regression analyses, considering the following climatic variables as independent (Y axis): Rainfall (PP), Standardized Precipitation Index (SPI), Southern Oscillation Index (SOI) and Temperature on the sea surface (SST Niño 3.4). The independent variable (X axis) was the number of occurrences of rust in the off-season, and the dependent variable (Z axis) was defined as rust occurrences during the season, which were reported by the Anti-rust Consortium. The best model that explains the epidemic of the disease during the season in Paraná state was composed by Rainfall or SST Niño 3.4 variable as the Y axis. The best model for Mato Grosso state used SST Niño 3.4 or SOI variable. The variable number of occurrences in the off-season significantly influenced the model, indicating the potential use of this variable and meteorological variables on a macro scale to predict epidemics even before the start of the season.

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Section: Statistics and Data Analysismentioning

confidence: 99%

Predicting Asian Soybean Rust Epidemics Based on Off-Season Occurrence and El Niño Southern Oscillation Phenomenon in Paraná and Mato Grosso States, Brazil

Minchio¹,

Fantin²,

Caviglione³

et al. 2018

JAS

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“…The scientific evaluation of the performance and value of warning systems for SBR are scarce compared to research aimed at model development (Del Ponte et al 2006b;Megeto et al 2014;Minchio et al 2016;Tao et al 2009). Very few studies evaluated warning systems based on rainfall and the leaf wetness-temperature (LWD-T) interaction targeting SBR control (Igarashi et al 2016;Kelly et al 2015), which may be limited by the lack of validated systems. In fact, most of the SBR epidemiological models were originally developed for risk assessment and scenario analysis (Del Ponte et al 2006b, 2011Tao et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Performance and Profitability of Rain-Based Thresholds for Timing Fungicide Applications in Soybean Rust Control

et al. 2020

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Soybean rust (SBR), caused by the fungus Phakopsora pachyrhizi, is the most damaging disease of soybean in Brazil. Effective management is achieved by means of calendar-timed sprays of fungicide mixtures, which do not explicitly consider weather-associated disease risk. Two rain-based action thresholds of disease severity values (DSV50 and DSV80) were proposed and compared with two leaf wetness duration-temperature thresholds of daily values of infection probability (DVIP6 and DVIP9) and with a calendar program, with regard to performance and profitability. An unsprayed check treatment plot was included for calculating relative control. Disease severity and yield data were obtained from 29 experiments conducted at six sites across four states in Brazil during the 2012–13, 2014–15, and 2015–16 growing seasons, which represented different growing regions and climatic conditions. The less conservative rainfall action threshold (DSV80) resulted in fewer fungicide sprays compared with the other treatments, and the more conservative one (DSV50) resulted in fewer sprays than the DVIP thresholds. Yield was generally higher with the increase in spray number, but the economic analysis showed no significant differences in the risk of not offsetting the costs of fungicide sprays regardless of the system. Therefore, based on the simplicity and the profitability of the rain-based model, the system is a good candidate for incorporating into the management of SBR in soybean production fields in Brazil.

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“…Following the detection of the first urediniospores, incidence and disease severity were assessed and compared with the predictions made by the models. The premonitory symptoms of rust in the first and second harvest seasons were observed only when using the model of Reis et al [16].…”

Section: Disease Predictionmentioning

confidence: 94%

A Self-Predictable Crop Yield Platform (SCYP) Based On Crop Diseases Using Deep Learning

et al. 2019

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This paper proposes a self-predictable crop yield platform (SCYP) based on crop diseases using deep learning that collects weather information (temperature, humidity, sunshine, precipitation, etc.) and farm status information (harvest date, disease information, crop status, ground temperature, etc.), diagnoses crop diseases by using convolutional neural network (CNN), and predicts crop yield based on factors such as climate change, crop diseases, and others by using artificial neural network (ANN). The SCYP consists of an image preprocessing module (IPM) to determine crop diseases through the Google Vision API and image resizing, a crop disease diagnosis module (CDDM) based on CNN to diagnose the types and extent of crop diseases through photographs, and a crop yield prediction module (CYPM) based on ANN by using information of crop diseases, remaining time until harvest (based on the date), current temperature, humidity and precipitation (amount of snowfall) in the area, sunshine amount, ground temperature, atmospheric pressure, moisture evaporation in the ground, etc. Four experiments were conducted to verify the efficiency of the SCYP. In the CDMM, the accuracy and operation time of each model were measured using three neural network models: CNN, region-CNN(R-CNN), and you only look once (YOLO). In the CYPM, rectified linear unit (ReLU), Sigmoid, and Step activation functions were compared to measure ANN accuracy. The accuracy of CNN was about 3.5% higher than that of R-CNN and about 5.4% higher than that of YOLO. The operation time of CNN was about 37 s less than that of R-CNN and about 72 s less than that of YOLO. The CDDM had slightly less operation time, but in this paper, we prefer accuracy over operation time to diagnose crop diseases efficiently and accurately. When the activation function of the ANN used in the CYPM was ReLU, the accuracy of the ANN was 2% higher than that of Sigmoid and 7% higher than that of Step. The CYPM prediction was about 34% more accurate when using multiple diseases than when not using them. Therefore, the SCYP can predict farm yields more accurately than traditional methods.

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Application of prediction models of asian soybean rust in two crop seasons, in Londrina, Pr

Cited by 11 publications

References 8 publications

Predicting Asian Soybean Rust Epidemics Based on Off-Season Occurrence and El Niño Southern Oscillation Phenomenon in Paraná and Mato Grosso States, Brazil

Predicting Asian Soybean Rust Epidemics Based on Off-Season Occurrence and El Niño Southern Oscillation Phenomenon in Paraná and Mato Grosso States, Brazil

Performance and Profitability of Rain-Based Thresholds for Timing Fungicide Applications in Soybean Rust Control

A Self-Predictable Crop Yield Platform (SCYP) Based On Crop Diseases Using Deep Learning

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