The reduction of the production cost and negative environmental impacts by pesticide application to control cotton diseases depends on the infection patterns spatialized in the farm scale. Here, we evaluate the potential of three-band multispectral imagery from a multi-rotor unmanned airborne vehicle (UAV) platform for the detection of ramularia leaf blight from different flight heights in an experimental field. Increasing infection levels indicate the progressive degradation of the spectral vegetation signal, however, they were not sufficient to differentiate disease severity levels. At resolutions of ~5 cm (100 m) and ~15 cm (300 m) up to a ground spatial resolution of ~25 cm (500 m flight height), two-scaled infection levels can be detected for the best performing algorithm of four classifiers tested, with an overall accuracy of ~79% and a kappa index of ~0.51. Despite limited classification performance, the results show the potential interest of low-cost multispectral systems to monitor ramularia blight in cotton.

The potential applications of computational tools, such as anomaly detection and incongruence, for analyzing data attract much attention from the scientific research community. However, there remains a need for more studies to determine how anomaly detection and incongruence applied to analyze data of static images from remote sensing will assist in detecting water pollution. In this study, an incongruence-based anomaly detection strategy for analyzing water pollution in images from remote sensing is presented. Our strategy semi-automatically detects occurrences of one type of anomaly based on the divergence between two image classifications (contextual and non-contextual). The results indicate that our strategy accurately analyzes the majority of images. Incongruence as a strategy for detecting anomalies in real-application (non-synthetic) data found in images from remote sensing is relevant for recognizing crude oil close to open water bodies or water pollution caused by the presence of brown mud in large rivers. It can also assist surveillance systems by detecting environmental disasters or performing mappings.