Sistema de visão e inteligência computacional em ambiente de nuvem para gestão de risco da ferrugem asiática na cultura da soja

Abstract

Controlling Asian Soybean Rust (Phakopsora pachyrhizi) in soybeans (Glycine max (L.) Merril) often requires high fungicide use, which can lead to resistance. Thus, new control solutions are needed for mitigation. This work presents an intelligent computer vision system for assessing the presence and severity of this disease in crop areas. It involves pattern recognition and machine learning techniques, enabling diagnostic actions for prognosis and control. It considers a decision-support model using random variables related to climate, plants, and characteristics recognized in digital images of monitored soybean leaves. For feature extraction, it uses scale-invariant feature transform, histogram of oriented gradients, and Hu’s invariant moments techniques. It uses cloud-based computational infrastructure and intelligent network processing, as well as principal component analysis for dimensionality reduction of features classified by support vector machines. Additionally, a hidden Markov model is used to fuse random variables, offering robustness, effectiveness, and efficiency, as confirmed by expert cross-correlation. To evaluate data quality at various system stages, metric sets such as peak signal-to-noise ratio, mean squared error, structural similarity index, missing values, accuracy, precision, F1-score, and recall are considered. This solution prevents and reduces fungicide use, enhancing production and guiding future early spatio-temporal monitoring of the disease on an agricultural scale.