Indonesian Journal of Electrical Engineering and Computer Science Vol. 28, No. 3, December 2022, pp. 1676~1683 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v28.i3.pp1-1x  1676 Journal homepage: http://ijeecs.iaescore.com Detection and classification of plant diseases in leaves through machine learning Hasan Ahmed, Md Alomgir Hossain, Ismail Hossain, Sharmin Sultana Akhi, Ifrat Jahan Lima Department of Computer and Software Engineering, College of Engineering and Technology (CEAT), International University of Business Agriculture and Technology (IUBAT), Dhaka, Bangladesh Article Info ABSTRACT Article history: Received Feb 9, 2022 Revised Aug 26, 2022 Accepted Sep 8, 2022 Plant diseases cause significant productivity and economic losses, as well as a reduction in agricultural product quality and quantity. One principal impact on low crop yield is sickness due to bacteria, virus and fungus It is possible to avoid it by employing plant disease detection and categorization procedures. We used machine learning to detect and classify diseases in plant leaves because it evaluates data from several perspectives and categorizes it into one of several predefined classifications. In this research we create a model for the classification task which is sequential model. We trained a convolutional neural network (CNN) with help of the plant village dataset, which have 55,000 images divided into 39 completely distinct categories of each healthy and effected leaves. We trained data by using Adam optimization technique because it almost constantly plays quicker and higher global minimal convergence in comparison to the alternative optimization techniques. We achieved a validation accuracy of 98.74% using the architecture of CNN containing optimized parameters. CNNs, as can be observed, have a high-stop overall performance, making them surprisingly suitable for computerized identification of plant illnesses using simple plant leaf images. The experiment effects completed are similar with different current strategies in literature. Keywords: Convolutional neural network Machine learning Plant disease Plant village Sequential model This is an open access article under the CC BY-SA license. Corresponding Author: Hasan Ahmed Department of Computer Science and Engineering, College of Engineering and Technology International University of Business Agriculture and Technology (IUBAT) Dhaka 1230, Dhaka, Bangladesh Email: hasanjab14@gmail.com 1. INTRODUCTION Harvest diseases, on the other hand, may appear to be a minor issue, but they have the ability to create famine and plant diseases are one the primary reason of worldwide food insecurity. In developing countries, where facilities and access to plant-disease control methods are restricted, the repercussions are more severe. A huge number of samples are examined to determine crop disease. Microscopy and DNA sequencing-based techniques that provide detailed data about pathogens that cause disease, like as micro- organisms, viruses, and fungal, among others [1]. However, the majority of farmers do not have access to these resources, access to these procedures of diagnosis. According to the World Bank's research, in 2016, mobile communication was widespread in 7 out of 10 of the world's poorest 20% of countries, while 45% of the world's population has internet access [2]. 2 Many challenges in agriculture and vegetation have been overcome as a result of recent breakthroughs and extensive research in the fields of deep learning and machine intelligence (AI), such as agricultural disease detection, yield identification, and smart farming. Rastogi [3] have noted, If an automatic system can detect the type of disease that a crop is suffering from, in