### Related Work  
In previous research, various ML and DL algorithms have already been proposed for crop leaf disease detection [11], and some of them are explored in the following section. Among these studies, [PERSON] et al. [12] established a brief survey of methods of identifying crop diseases using ML and DL techniques, in particular SVM, ANN, CNN, and RNN, to help researchers recognize the disadvantages and advantages of the different techniques used for plant disease detection. Today, a growing trend to replace machine learning algorithms with those based on deep learning methods for leaf image classification is gaining momentum. In fact, [PERSON] et al. [13] deployed an approach to the distance metric within few-short learning that relied on triplet loss as well as a shallow classifier at class limits that proved efficient for classifying plant leaf diseases with little training images annotated using deep learning. Their approach showed greater than 90% accuracy using only 80 images per class for six diseases. In contrast, [PERSON] et al. [14] suggested a technique for identifying diseases (downy mildew, powdery mildew, healthy, and black rot) on grape leaves by applying class classification and the local binary pattern to extract features and the algorithm Grab cut to segment the images. The novelty in their model is the high generalization capacity, which has been proven and tested on different leaf samples from diverse plant species, reaching an accuracy of 95%. Specifically, 44 combinations of the plant diseases tested out of 46 were classified perfectly, resulting in a 95% total success rate. The resolution of conflicts between classifiers into one class is crucial. This enables correct identification when ambulatory data belong to single or multiple conditions, i.e., more than 50% of the cases have reached 100% identification capacity. [PERSON] et al. [15] applied a Deep Forest algorithm for maize leaf disease classification from an image set containing three classes of diseased leaves and one class of healthy leaves. Their approach has surpassed deep neural networks in terms of accuracy, as it combines both the robustness of assembled decision trees and the architecture of neural networks. Ubiquitous, automatic detection via mobile devices has become a trend in the area of disease diagnosis. [PERSON] et al. [16] introduce a hybrid lightweight CNN-based approach for identifying grape diseases, such as black measles, and black rot, by leveraging channel-wise attention (CA) mechanisms. Thus, they enhanced the ShuffleNet architectures by using the compression and excitation blocks as the channel-wise attention mechanism as well as the ShuffleNet v1 and v2 architectures as the backbone. Their model was compressed from 227.5 MB to 4.2 MB and tested on a dataset of 4062 grape leaf images that gave a high accuracy of 99.14% in real-time. Another special type of SNN is the ELM algorithm. In this perspective, [PERSON] et al. [17] implemented the Extreme Learning Machine (ELM) based on a TPMD dataset (Tomato Powdery Mildew Disease) collected in real-time for leaf disease prediction, stating that this TPMD dataset was very unbalanced, so they used many resampling techniques such as Random Under Sampling (RUS), Random Over Sampling (ROS) [18], Synthetic Minority Over-sampling (SMOTE) [19], and Importance Sampling (IMPS) to balance the dataset before feeding it into the selected predictive model. Subsequently, ELM algorithms were developed for the unbalanced dataset as well as for the balanced dataset acquired using resampling techniques. The performance analysis of these ELM algorithms is performed using two predictive accuracy measures, namely Area Under the Curve (AUC) and Classification Accuracy (CA). Therefore, the findings indicated that the ELM algorithm works much better for the dataset obtained from resampling techniques. In particular, the better outcome was attained using the IMPS technique, with the maximum values for AUC and CA, or 88.57% and 89.19%, respectively.