# Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection
## 1 Introduction  
Agriculture is one of the most important research topics globally nowadays [1]. Agriculture is a significant source of income and the economy of a country is based on the quality and yields of crops [2]. Cucumber is an important vegetable and during the year 2020, the global cucumber planting area was around 2.25 million hectares and the globalyield was 90.35 million tons [3]. The production of crops is highly threatened by diseases and failure to identify and prevent cucumber diseases causes a reduction of cucumber vegetable yield and quality. The failure in early diagnosis causes significant economic losses to growers. Therefore, the rapid diagnosis of crops diseases helps to increase the quality and yield and also increases the national economy [4]. Mostly, identification is accomplished using typical methods like seeing through naked eyes or through a microscope [5]. The results of the manual visual estimation are generally unreliable while the microscopic assessments are generally time-consuming and costly. Most of the agriculturalists of underdeveloped countries are illiterate [6]. They are compelled to return those charges along with other expenses like pesticides and fertilizer. The cucumber diseases like anthracose, powdery mildew, downy mildew, and cucumber mosaic can destroy a large number of crops and the result will be a huge loss and vegetable deficiency [7]. Significant work has been done to accomplish a method that can boost the fastness and accuracy of the process. The methods necessarily contained some sort of computerization [8]. A large number of techniques presented until now are based on digital image processing and machine learning to identify the crops' diseases and achieve the desired output [9]. Image processing has many applications in the domain of computer vision such as medical imaging [10], agriculture [11], and named a few more. Agriculture is a hot application of image processing for the identification and classification of crops and plant diseases [12]. Although detection of cucumber abnormalities and then classifying them using image processing techniques is a critical task due to some sequence of steps [13]. A computerized method consists of some important steps such as preprocessing of original leaf images, detection of the infected region, feature extraction using handcrafted methods, and finally reduction and classification. Recognition of diseased portions in images is the key factor as it can influence the design and performance of the classification algorithms [14]. However, the error in the detection of the infected region extracted the irrelevant features that reduces the recognition accuracy. Deep learning (DL) [15] is a hot research topic nowadays [16] and is employed everywhere for the detection and recognition tasks for several applications [17] such as biometric [18], image classification [19], surveillance [20], medical [21], and agriculture [22]. The researcher of computer vision introduced many techniques using machine learning and deep learning for plants diseases recognition [23]. [PERSON] et al. [24] introduced a deep learning technique for the identification of multiple cucumber leaf diseases. They extract deep learning features through two fine-tuned deep models including VGG19 and Inception V3. Both fine-tuned models were trained on the selected dataset using the transfer learning approach. The main advantage of training through TL is to save memory and time. The extracted features were fused by implementing the parallel maximum fusion technique to get the maximum information of each trained image. In the end, a whale optimization algorithm (WOA) was applied to select the robust features and perform classification. The purpose of feature selection is to get the best features because, in the fusion process, a few redundant features were also added. They achieved a maximum of 96.5% accuracy on the selected leaf dataset. In [13], researchers built an automated detection classification model for cucumber leaf diseases.