Automatic detection of fruits in coffee crops from aerial images

A big challenge in the precision agriculture is the detection of fruits in coffee crops on agricultural environments. This paper presents a comparison of four features set to detect the red fruits (mature) in Coffee plants. An Unmanned Aerial Vehicle (UAV) is used to obtain high-resolution RGB images of a coffee hall. The proposed methodology enables the extraction of visual features from image regions and uses supervised Machine Learning (ML) techniques to classify areas as coffee fruits and non-fruits (e.g. branches and leaves). Several ML methods were compared using the test data achieved from a Coffee plantation. Experimental results show that the ANN model is more reliable than other ML methods for accurately identifying coffee fruits.