# Sesame Plant Disease Classification Using Deep Convolution Neural Networks  
[PERSON]  
1 Department of Information Technology, University of Gondar, Gondar 196, Ethiopia; azamunrilong@69 mail.com (A.M.M.); [EMAIL_ADDRESS] (A.M.A.)  
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3 Department of Computer Science, University of Murcia, 30100 Murcia, Spain; [EMAIL_ADDRESS]  
4 Center for Machine Vision and Signal Analysis, University of Oulu, 90014 Oulu, Finland  
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of sesame plants [6]. Such diseases have major impacts on the production of sesame, including reductions in seed quality, yield, oil content, and plant health. Since disease decreases the quality of seeds as well as the content of the product, income from exports and local sales decrease. It affects the economy of the country, including everyone who directly or indirectly benefits from sesame cultivation [7]. Monitoring sesame plant health and detecting the disease early is essential to facilitate effective management practices and reduce disease spread [8]. The identification of sesame diseases by observation is time-consuming, tedious, and requires human labor, and this may lead to incorrect disease identification and also the inappropriate usage of pesticides [8]. This traditional method is limited due to the low capacity of human labor to cover large areas and the lack of local experts, and so on. Hence, it is required to develop computational methods that make the process of disease detection and classification easier [9]. To this end, researchers in [10] presented the CLIPS expert system, which is used to identify sesame diseases. But the developed system requires careful identification of the symptoms by users to identify the disease correctly. Also, the system is only capable of doing what it is designed for and what the user has selected. It cannot provide creative responses for different cases.  
For the detection of crop diseases, different techniques are used, such as machine learning [11]. Machine learning is a sub-area of artificial intelligence that is able to find solutions to problems independently by identifying patterns in databases [12]. The types and symptoms of diseases that commonly affect different plants can be different. The literature has shown that since crops' morphology, color, and texture features are different, a model proposed for one agricultural crop cannot be directly applied to others [9].  
In this study, a model is developed to detect diseases in sesame crop of three classes; bacterial blight-infected, phyllody-infected, and healthy sesame crops. Some sesame diseases show no clear symptoms and exhibit a wide range of colors in healthy leaves. This makes it difficult to detect sesame diseases. As a result, to improve image quality and recognition accuracy, we use median filtering to remove noise, and contrast and brightness enhancement are performed using a contrast stretch technique. The SegNet semantic segmentation technique is used to segment leaves from unwanted backgrounds. Feature extraction and classification are performed using a deep convolution neural network architecture with improved layers and parameters.  
The remaining parts of this paper are organized as follows. Section 2 presents a review of related works. Section 3 describes the methodology used in the development of the proposed sesame disease classifier, while the results and discussion are pointed out in Section 4. Finally, some conclusions are put forward in Section 5.