Soybean yield prediction by machine learning and climate

Torsoni, Guilherme Botega; Aparecido, Lucas Eduardo de Oliveira; Santos, Gabriela Marins dos; Chiquitto, Alisson Gaspar; Moraes, José Reinaldo da Silva Cabral de; Rolim, Glauco de Souza

doi:10.1007/s00704-022-04341-9

Cited by 15 publications

(6 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These models used infection rates as the dependent variable and meteorological data as independent variables (Table 2) to understand the impact of weather on disease spread. A standard method from the ‘Scikit‐learn’ Python library, ‘train_test_split,’ was used to divide the data, allocating 70% for training and 30% for testing 54 . This approach helps in training the models effectively without overfitting and allows for accurate prediction of disease outbreaks, aiding better crop protection and resource management.…”

Section: Methodsmentioning

confidence: 99%

“…A standard method from the 'Scikit-learn' Python library, 'train_test_split,' was used to divide the data, allocating 70% for training and 30% for testing. 54 This approach helps in training the models effectively without overfitting and allows for accurate prediction of disease outbreaks, aiding better crop protection and resource management.…”

Section: Forecasting Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Climate and disease: tackling coffee brown‐eye spot with advanced forecasting models

de Oliveira Aparecido,

de Lima,

Torsoni

et al. 2024

J Sci Food Agric

Self Cite

View full text Add to dashboard Cite

BACKGROUNDClimate significantly influences the interaction between pathogens and their hosts, and this is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown eye spot, can drastically reduce yields. Our study focuses on forecasting coffee brown eye spot using various models that incorporate agrometeorological data, allowing for predictions at least one week prior to disease occurrence. Data was gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais State. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown eye spot, identifying one with potential for advanced decision‐making. The top‐performing models were then employed in the next stage to forecast and spatially project the severity of brown eye spot across 2,681 key Brazilian coffee‐producing municipalities. Meteorological data were sourced from NASA's POWER platform, and the Penman‐Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather (1955) water balance calculation. Six ML models — K‐Nearest Neighbors (KNN), Artificial Neural Network Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forests (RF), Extreme Gradient Boosting (XGBoost), and Gradient Boosting Regression (GradBOOSTING) — were employed, considering a 7‐day disease latency to define input variables.RESULTSThese models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high‐yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low‐yielding scenarios. The incidence of brown eye spot varied noticeably between high and low‐yield conditions, with significant regional differences observed. The accuracy of predicting brown eye spot severity in coffee plantations depended on the biennial production cycle. High‐yielding trees showed superior results with the XGBoost model (R2 = 0.77, RMSE = 10.53), while under low‐yielding conditions, the Support Vector Machine (SVM) model performed better (precision 0.76, RMSE = 12.82).CONCLUSIONThe study's application of agrometeorological variables and ML models successfully predicted the incidence of brown eye spot in coffee plantations with a seven‐day lead time, illustrating a valuable tool for managing this significant agricultural challenge.This article is protected by copyright. All rights reserved.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Forecasting Modelingmentioning

confidence: 99%

Climate and disease: tackling coffee brown‐eye spot with advanced forecasting models

de Oliveira Aparecido,

de Lima,

Torsoni

et al. 2024

J Sci Food Agric

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, data mining techniques and machine learning (ML) algorithms have shown great promise in predicting maize yield. Accurate yield prediction employing various machine and deep learning algorithms is widely reported by researchers [69][70][71][72][73][74]. In this context, we tested the performance of four ML algorithms including BG, DT, RF and ANN-MLP (Figures 4-7).…”

Section: Machine Learning Algorithms For Better Optimization Of Maize...mentioning

confidence: 99%

Data Mining and Machine Learning Algorithms for Optimizing Maize Yield Forecasting in Central Europe

et al. 2023

View full text Add to dashboard Cite

Artificial intelligence, specifically machine learning (ML), serves as a valuable tool for decision support in crop management under ongoing climate change. However, ML implementation to predict maize yield is still limited in Central Europe, especially in Hungary. In this context, we assessed the performance of four ML algorithms (Bagging (BG), Decision Table (DT), Random Forest (RF) and Artificial Neural Network-Multi Layer Perceptron (ANN-MLP)) in predicting maize yield based on four different input scenarios. The collected data included both agricultural data (production (PROD) (ton) and maize cropped area (AREA) (ha)) and climate data (annual mean temperature °C (Tmean), precipitation (PRCP) (mm), rainy days (RD), frosty days (FD) and hot days (HD)). This research adopted four scenarios, as follows: SC1: AREA+ PROD+ Tmean+ PRCP+ RD+ FD+ HD; SC2: AREA+ PROD; SC3: Tmean+ PRCP+ RD+ FD+ HD; and SC4: AREA+ PROD+ Tmean+ PRCP. In the training stage, ANN-MLP-SC1 and ANN-MLP-SC4 outperformed other ML algorithms; the correlation coefficient (r) was 0.99 for both, while the root mean squared errors (RMSEs) were 107.9 (ANN-MLP-SC1) and 110.7 (ANN-MLP-SC4). In the testing phase, the ANN-MLP-SC4 had the highest r value (0.96), followed by ANN-MLP-SC1 (0.94) and RF-SC2 (0.94). The 10-fold cross validation also revealed that the ANN-MLP-SC4 and ANN-MLP-SC1 have the highest performance. We further evaluated the performance of the ANN-MLP-SC4 in predicting maize yield on a regional scale (Budapest). The ANN-MLP-SC4 succeeded in reaching a high-performance standard (r = 0.98, relative absolute error = 21.87%, root relative squared error = 20.4399% and RMSE = 423.23). This research promotes the use of ANN as an efficient tool for predicting maize yield, which could be highly beneficial for planners and decision makers in developing sustainable plans for crop management.

show abstract

“…Determining soil water storage together with climate data can help estimate the regional or local water balance (FUZZO et al, 2019;FERINA et al, 2021;AMIRI et al, 2022). The climate data can be estimated by remote sensing (TORSONI et al, 2023) using satellite images, such as those from the MERRA-2 satellite with a spatial resolution of 55 km (GMAO, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Various studies have shown the importance of alternatives, such as reflectance at certain wavelengths (e.g. NIR), for the same purpose (LIU et al, 2014;ALABI et al, 2022) SZABÓ et al, 2019;FILGUEIRAS et al, 2020;AMORIM et al, 2022;TORSONI et al, 2023). Research should be carried out on a local scale, as one of the main challenges is increasing both the accuracy of these learning models and their applicability (MURUGANANTHAM et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Management zones and space-time prediction of soybean yield variability: machine learning techniques applied to soil physical quality parameters

Pereira

View full text Add to dashboard Cite

show abstract

Soybean yield prediction by machine learning and climate

Cited by 15 publications

References 53 publications

Climate and disease: tackling coffee brown‐eye spot with advanced forecasting models

Climate and disease: tackling coffee brown‐eye spot with advanced forecasting models

Data Mining and Machine Learning Algorithms for Optimizing Maize Yield Forecasting in Central Europe

Management zones and space-time prediction of soybean yield variability: machine learning techniques applied to soil physical quality parameters

Contact Info

Product

Resources

About