Coffee Leaf Diseases Classification Using Deep Learning Approach

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Author(s): Sudhir Kumar Mohapatra (Sri Sri University, India), Anbesaw Belete (Werabe University, Ethiopia), Ali Hussen (Werabe University, Ethiopia), Abdelah Behari (Werabe University, Ethiopia), Seid Huseen (Werabe University, Ethiopia)and Srinivas Prasad (GITAM University, India)
Copyright: 2024
Pages: 21
Source title: Machine Learning Algorithms Using Scikit and TensorFlow Environments
Source Author(s)/Editor(s): Puvvadi Baby Maruthi (Dayananda Sagar University, India), Smrity Prasad (Dayananda Sagar University, India)and Amit Kumar Tyagi ( National Institute of Fashion Technology, New Delhi, India)
DOI: 10.4018/978-1-6684-8531-6.ch005

Keywords: Engineering Science Reference / Knowledge Management and Library Science / Machine Learning / Systems and Software Engineering

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Abstract

Agricultural production is among the key techniques for alleviating extreme poverty, boosting economic stability, and feeding the 9.7 billion people expected to live by 2050. However, crop diseases are major obstacles to agriculture production. The most prevalent diseases that reduce production are late diseases which attack the leaves, which are particularly prevalent in coffee crops. To solve the issue, a suitable approach for identifying and categorizing these illnesses in this crop's leaf is required. Particularly in coffee crops, rust, coffee wilt, and brown spot are the most common diseases. Therefore, automatic identifying of these diseases through the system is critical. Thus, the main objective of this study is to design an automated system that can recognize and classify coffee leaf diseases' severity levels. Design science research methodology will follow. Accordingly, the required images have been collected from the SNNP.

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Coffee Leaf Diseases Classification Using Deep Learning Approach

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