Segmenting and Detecting Nematode in Coffee Crops Using Aerial Images

A challenge in precision agriculture is the detection of pests in agricultural environments. This paper describes a methodology to detect the presence of the nematode pest in coffee crops. An Unmanned Aerial Vehicle (UAV) is used to obtain high-resolution RGB images of a commercial coffee plantation. The proposed methodology enables the extraction of visual features from image regions and uses supervised machine learning (ML) techniques to classify areas into two classes: pests and non-pests. Several learning techniques were compared using approaches with and without segmentation. Results demonstrate the methodology potential, with an average for the f-measure of 63% for Convolutional Neural Network (U-net) with manual segmentation.

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