Foliar disease is common problem in plants; it appears as an abnormal change in the plant’s characteristics, such as the presence of lesions and discolorations, among others. These problems may be related to plant growth, which causes a decrease in crop production, impacting the agricultural economy. The causes of leaf damage can be variable, such as bacteria, viruses, nutritional deficiencies, or even consequences of climate change. Motivated to find a solution for this problem, we aim that using image processing and machine learning algorithms (MLA), these symptomatic characteristics of the leaf can be used to classify diseases. Then, contributions of this research are (i) the use of image processing methods in the feature extraction (characteristics), and (ii) the combination of assembled algorithms with deep learning to classify foliar features of Valencia orange (Citrus Sinensis) tree leaves. Combining these two classification approaches, we get optimal rates in binary datasets and highly competitive percentages in multiclass sets. This, using a database of images of three types of foliar damage of local plants. Result of combination of these two classification strategies is an exceptional reliable alternative for leaf damage identification of orange and other citrus plants.

叶病是植物的普遍问题。它表现为植物特性的异常变化，例如存在损害和变色等。这些问题可能与植物生长有关，导致植物产量下降，影响了农业经济。叶片受损的原因可以多种多样，例如细菌，病毒，营养不足，甚至是气候变化的后果。为了找到解决该问题的方法，我们的目标是通过使用图像处理和机器学习算法（MLA），可以将叶子的这些症状特征用于疾病分类。然后，这项研究的贡献是（i）在特征提取（特征）中使用图像处理方法，（ii）将组合算法与深度学习相结合，以对瓦伦西亚橙（Citrus Sinensis）树叶的叶特征进行分类。结合这两种分类方法，我们可以在二进制数据集中获得最佳比率，并在多类集中获得极具竞争力的百分比。这使用了本地植物的三种类型的叶面损害的图像数据库。这两种分类策略相结合的结果是一种非常可靠的替代方法，可用于鉴定橙和其他柑橘类植物的叶片损伤。