## 1 Introduction  
Fungal diseases pose a major problem in agricultural environments, especially in orchard and horticulture crops production. When trees suffer from infections, the production of the crop diminishes substantially and the economic impact can be catastrophic [1]. As part of precision agriculture, automatic plant disease detection and classification can alleviate this problem, with unsupervised early detection systems that could identify the presence of disease on the leaves of the trees and classify them appropriately for planning any precautionary measure or action to be taken. The rise of machine learning (ML) in the past years has significantly boosted the progress in this research area [2; 3; 4; 5].  
Several studies can be found in the bibliography regarding machine learning approaches for image-based plant disease identification. Leaf disease detection in various types of leaves and diseases has been investigated by classic ML algorithms, such as, support vector machines (SVMs) [6] and back-propagation artificial neural networks (ANNs), achieving 88-92% accuracy [7].  
The feed forward neural network (FFNN), learning vector quantization (LVQ) and radial basis function networks (RBFs) with accuracy range 56.7-90.7% [8], advanced deep residual neural networksthat acquired 96% accuracy [9], as well as deep learning (DL) methods, such as convolutional neural networks (CNN), that were able to achieve 96.3% accuracy [10].  
In terms of advanced CNN architectures, a series of achievements have been reported, including the identification of 13 types of plant diseases with an accuracy range of 91-98% [11], the identification of 58 different diseases for 25 types of plants with accuracy up to 99.53% [12], and some meta-architectures of CNN (AlexNet, GoogleNet) also implemented resulting in the same accuracy over 14 crop species and 26 types of diseases [13]. The size and variety of the leaf-image dataset on advanced CNN structures has been investigated in [14], leading to the conclusion that large and diverse datasets critically improve the performance of the models. In some studies, alternative imaging approaches are investigated, such as triple channel convolutional networks [15], and the combination of visible, thermal and depth imaging fungus detection on tomato leaves [16]. Other studies focus on specific combinations of plants-disease, such as cucumbers-anthracnose, downy mildew, powdery mildew and target leaf spots [17], apple trees-mosaic, rust, brown spot, alternaria leaf spot [18], apple trees/black rot with four severity stages by [19], brinjal (aubergine)/bacterial wilt, cercospora leaf spot, tobacco mosaic virus by [20], banana trees-sigatoka and speckle [21], wheat/wheat stripe rust, wheat leaf rust, grape downy mildew, grape powdery mildew [22] and tomato leaves-gray mold, canker, leaf mold, plague, leaf miner, whitefly, powdery mildew [23].  
In our study, we aim for the anthracose disease identification on walnut tree leaves, with images that were taken in real conditions. An investigatory analysis takes place by testing and evaluating various architectures of CNNs, as well as the effect of color in images, and the use of fast Fourier transform (FFT) as an additional feature extractor.