ARTIFICIAL INTELLIGENCE IN THE SIMULATION OF FUNGICIDE MANAGEMENT SCENARIOS FOR SATISFACTORY YIELD AND FOOD SAFETY IN OAT CROPS

Abstract

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.