# Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection
## 1 Introduction
In the first phase, pre-processing was performed to enhance the local contrast of images and to make the infected region more visible. This step makes the infected region clearer that later helped in the accurate segmentation using a novel Sharif saliency-based (SHSB) technique. Then researchers fused the proposed saliency method with active contour segmentation to improve the segmentation accuracy that later extracts the relevant features. In the feature extraction phase, they utilized VGG-19 and VGG-M pre-trained models. The extracted features were refined through three parameters including local entropy, local interquartile range, and local standard deviation. In the final classification, the best accuracy of 98.08% was achieved on multi-class SVM. The strength of this work was less computational time that can be useful for a real-time computerized system. [PERSON] et al. [3] introduced a deep learning-based technique for the recognition of cucumber leaf diseases under complex backgrounds. They fused DeepLabV3+ and U-Net models instead of a single network. In the first step, DeepLabV3+ was used to segment the leaves from the images. Then the diseased area was segmented using U-Net. The fused models give better accuracy than the accuracy reported by the individual models. Researchers in [25], introduced a model for the identification of crop diseases in real-world images. The proposed trilinear convolutional neural network utilized bilinear pooling. In the laboratory environment, the proposed technique achieved 99.99% accuracy and in the real-world environment, the obtained accuracy is 84.11%. [PERSON] et al. [7] proposed a hybrid system for the recognition of cucumber diseases. In the pre-processing step, the data augmentation was applied using different angles to increase the image count in the dataset. In this step, contrast stretching was also performed to visually improve the images. The features were extracted from binary robust invariant scalable keypoints (BRISK), histogram of gradient (HOG), and features from the accelerated segmented test (FAST). Initially, the irrelevant features were eliminated by utilizing the probability distribution-based entropy (PDBe) technique. Then features were fused using the serial-based method and implemented Manhattan distance-controlled entropy (MDCE) method was to select the robust features. The proposed model achieved maximum accuracy of 93.5%. These techniques faced a major challenge of irrelevant feature extraction that were tried to be resolved through feature selection techniques [26]. Visual inspection of crops was carried out by farmers and agriculture experts. This evaluation process is exhausting, time-consuming, and highly subjective. The development of computer vision systems to identify, recognize, and classify disease-affected crops will keep humans out of the equation, allowing for unbiased, accurate disease-infection decisions [1]. An automatic classification system consists of various steps as mentioned above. Preprocessing is an important step, the aim is to remove noise and improve the quality of original images that later helps in important feature extraction. The extracted features from the refined images are used for the training of deep learning models that are further employed for feature extraction and classification. The key problems which are considered in this work are (i) training a deep learning model on an imbalanced dataset gives the high priority in the prediction to higher numbers of sample class; (ii) disease spots and background objects differ in appearance; (iii) changes in the shape, color, texture, and origin of the disease; (iv) irrelevant and redundant features extraction, and (v) choosing the superlative features for the classification. In this article, our major focus is to design an automated computerized method for cucumber leaf diseases recognition using deep learning and Entropy-ELM-based best feature selection.