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Apple scab is a fungal disease caused by Venturia inaequalis. Disease is ofparticular concern for growers, as it causes significant damage to fruit andleaves, leading to loss of fruit and yield. This article examines the abilityof deep learning and hyperspectral imaging to accurately identify an applesymptom infection in apple trees. In total, 168 image scenes were collectedusing conventional RGB and Visible to Near-infrared (VIS-NIR) spectral imaging(8 channels) in infected orchards. Spectral data were preprocessed with anArtificial Neural Network (ANN) trained in segmentation to detect scab pixelsbased on spectral information. Linear Discriminant Analysis (LDA) was used tofind the most discriminating channels in spectral data based on the healthyleaf and scab infested leaf spectra. Five combinations of false-colour imageswere created from the spectral data and the segmentation net results. Theimages were trained and evaluated with a modified version of the YOLOv5network. Despite the promising results of deep learning using RGB images(P=0.8, mAP@50=0.73), the detection of apple scab in apple trees usingmultispectral imaging proved to be a difficult task. The high-light environmentof the open field made it difficult to collect a balanced spectrum from themultispectral camera, since the infrared channel and the visible channelsneeded to be constantly balanced so that they did not overexpose in the images.

Apple scab detection in orchards using deep learning on colour and multispectral images