Apple scab is a fungal disease caused by Venturia inaequalis. Disease is of particular concern for growers, as it causes significant damage to fruit and leaves, leading to loss of fruit and yield. This article examines the ability of deep learning and hyperspectral imaging to accurately identify an apple symptom infection in apple trees. In total, 168 image scenes were collected using conventional RGB and Visible to Near-infrared (VIS-NIR) spectral imaging (8 channels) in infected orchards. Spectral data were preprocessed with an Artificial Neural Network (ANN) trained in segmentation to detect scab pixels based on spectral information. Linear Discriminant Analysis (LDA) was used to find the most discriminating channels in spectral data based on the healthy leaf and scab infested leaf spectra. Five combinations of false-colour images were created from the spectral data and the segmentation net results. The images were trained and evaluated with a modified version of the YOLOv5 network. 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 using multispectral imaging proved to be a difficult task. The high-light environment of the open field made it difficult to collect a balanced spectrum from the multispectral camera, since the infrared channel and the visible channels needed to be constantly balanced so that they did not overexpose in the images.

翻译：苹果黑星病是由苹果黑星菌（Venturia inaequalis）引起的真菌病害。该病害对种植者尤为关注，因其对果实和叶片造成严重损害，导致果实减产及产量损失。本文研究了深度学习与高光谱成像技术准确识别苹果树感染症状的能力。研究在感染果园中采集了168幅场景图像，采用传统RGB成像与可见光至近红外（VIS-NIR）光谱成像（8通道）技术。利用人工神经网络（ANN）进行分割训练，基于光谱信息预处理光谱数据以检测黑星病像素。通过线性判别分析（LDA）基于健康叶片与黑星病侵染叶片的光谱特征，确定光谱数据中区分度最高的通道。基于光谱数据与分割网络结果生成了五种假彩色图像组合。采用改进版YOLOv5网络对图像进行训练与评估。尽管基于RGB图像的深度学习取得了显著效果（P=0.8, mAP@50=0.73），但利用多光谱成像检测苹果树黑星病仍是一项艰巨任务。野外强光环境导致多光谱相机难以采集平衡光谱，因为红外通道与可见光通道需持续平衡以避免图像过曝。