Anthracnose is one of the primary diseases that affect olive production before and after harvest, causing severe damage and economic losses. The objective of this work is to detect this disease in the early stages, using hyperspectral images and advanced modelling techniques of Deep Learning (DL) and convolutional neural networks (CNN). The olives were artificially inoculated with the fungus. Hyperspectral images (450–1050 nm) of each olive were acquired until visual symptoms of the disease were observed, in some cases up to 9 days. The olives were classified into two classes: control, inoculated with water, and fungi composed of olives inoculated with the fungus. The ResNet101 architecture was chosen and adapted to process 61-band hyperspectral images with only two classes. The result showed that the applied model is very effective in detecting infected olives since the sensitivity of the method was very high from the beginning (85% on day 3 and 100% onwards). From a commercial point of view, these results align with the need to detect the maximum number of infected fruits.

炭疽病是影响橄榄收获前后生产的主要病害之一，造成严重破坏和经济损失。这项工作的目标是使用高光谱图像以及深度学习 (DL) 和卷积神经网络 (CNN) 的高级建模技术，在早期阶段检测这种疾病。橄榄被人工接种了真菌。获取每个橄榄的高光谱图像（450-1050 nm），直到观察到疾病的视觉症状，在某些情况下长达 9 天。橄榄分为两类：对照，用水接种，以及由接种真菌的橄榄组成的真菌。选择 ResNet101 架构并进行调整以处理只有两个类别的 61 波段高光谱图像。结果表明，应用模型在检测受感染的橄榄方面非常有效，因为该方法的灵敏度从一开始就非常高（第 3 天为 85%，以后为 100%）。从商业角度来看，这些结果符合检测受感染水果的最大数量的需要。