Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases.

Disease and pest alert models are able to generate information for agrochemical applications only when needed, reducing costs and environmental impacts. With machine learning algorithms, it is possible to develop models to be used in disease and pest warning systems as a function of the weather in order to improve the efficiency of chemical control of pests of the coffee tree. Thus, we correlated the infection rates with the weather variables and also calibrated and tested machine learning algorithms to predict the incidence of coffee rust, cercospora, coffee miner, and coffee borer. We used weather and field data obtained from coffee plantations in production in the southern regions of the State of Minas Gerais (SOMG) and from the region of the Cerrado Mineiro; these crops did not receive phytosanitary treatments. The algorithms calibrated and tested for prediction were (a) Multiple linear regression (RLM); (b) K Neighbors Regressor (KNN); (c) Random Forest Regressor (RFT), and (d) Artificial Neural Networks (MLP). As dependent variables, we considered the monthly rates of coffee rust, cercospora, coffee miner, and coffee tree borer, and the weather elements were considered as independent (predictor) variables. Pearson correlation analyses were performed considering three different time periods, 1-10 d (from 1 to 10 days before the incidence evaluation), 11-20 d, and 21-30 d, and used to evaluate the unit correlations between the weather variables and infection rates of coffee diseases and pests. The models were calibrated in years of high and low yields, because the biannual variation of harvest yield of coffee beans influences the severity of the diseases. The models were compared by the Willmott's 'd', RMSE (root mean square error), and coefficient of determination (R2) indices. The result of the more accurate algorithm was specialized for the SOMG and Cerrado Mineiro regions using the kriging method. The weather variables that showed significant correlations with coffee rust disease were maximum air temperature, number of days with relative humidity above 80%, and relative humidity. RFT was more accurate in the prediction of coffee rust, cercospora, coffee miner, and coffee borer using weather conditions. In the SOMG, RFT showed a greater accuracy in the predictions for the Cerrado Mineiro in years of high and low yields and for all diseases. In SOMG, the RMSE values ranged from 0.227 to 0.853 for high-yield and 0.147 and 0.827 for low-yield coffee in the coffee borer forecasting.

Neural networks in spatialization of meteorological elements and their application in the climatic agricultural zoning of bamboo.

Bamboo has an important role in international commerce due to its diverse uses, but few studies have been conducted to evaluate its climatic adaptability. Thus, the objective of this study was to construct an agricultural zoning for climate risk (ZARC) for bamboo using meteorological elements spatialized by neural networks. Climate data included air temperature (TAIR, °C) and rainfall (P) from 4947 meteorological stations in Brazil from the years 1950 to 2016. Regions were considered climatically apt for bamboo cultivation when TAIR varied between 18 and 35 °C, and P was between 500 and 2800 mm year-1, or PWINTER was between 90 and 180 mm year-1. The remainder of the areas was considered marginal or inapt for bamboo cultivation. A multilayer perceptron (MLP) neural network with a multilayered "backpropagation" training algorithm was used to spatialize the territorial variability of each climatic element for the whole area of Brazil. Using the overlapping of the TAIR, P, and PWINTER maps prepared by MLP, and the established climatic criteria of bamboo, we established the agricultural zoning for bamboo. Brazil demonstrates high seasonal climatic variability with TAIR varying between 14 and 30 °C, and P varying between < 400 and 4000 mm year-1. The ZARC showed that 87% of Brazil is climatically apt for bamboo cultivation. The states that were classified as apt in 100% of their territories were Mato Grosso do Sul, Goiás, Tocantins, Rio de Janeiro, Espírito Santo, Sergipe, Alagoas, Ceará, Piauí, Maranhão, Rondônia, and Acre. The regions that have restrictions due to low TAIR represent just 11% of Brazilian territory. This agroclimatic zoning allowed for the classification of regions based on aptitude of climate for bamboo cultivation and showed that 71% of the total national territory is considered to be apt for bamboo cultivation. The regions that have restrictions are part of southern Brazil due to low values of TAIR and portions of the northern region that have high levels of P which is favorable for the development of diseases.