Journal of Advances in Information Technology, Vol. 14, No. 1, February 2023 122 doi: 10.12720/jait.14.1.122-129 Identification of Leaf Disease Using Machine Learning Algorithm for Improving the Agricultural System Keerthi Kethineni 1,2 and G. Pradeepini 3, * 1 Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, India 2 Department of CSE, V. R. Siddhartha Engineering College, Vijayawada, India; Email: keerthi.kethineni@gmail.com 3 Dept. of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, India *Correspondence: Pradeepini_cse@kluniversity.in Abstract—Diagnosing plant disease is the foundation for effective and accurate plant disease prevention in a complicated environment. Smart farming is one of the fast- growing processes in the agricultural system, with the identification of disease in plants being a major one to help farmers. The processed data is saved in a database and used in making decisions in advance support, analysis of plants, and helps in crop planning. Plants are one of the essential resources for avoiding global warming. However, diseases such as blast, canker, black spot, brown spot, and bacterial leaf damage the plants. In this paper, image processing integration is developed to identify the type of disease and help automatically inspect all the leaf batches by storing the processed data. In some places, farmers are unaware of the experts and do not have proper facilities. In such conditions, one technique can be beneficial in keeping track and monitoring more crops. This technique makes it much easier and cheaper to detect disease. Machine learning can provide a method and algorithm to detect the disease. There should be training in images of all types of leaves, including healthy and disease leaf images. Five-stage detection processes are done in this paper. The stages are preprocessing, segmentation using k-Mean, feature extraction, features optimization using Firefly optimization Algorithm (FA), and classification using Support Vector Machine (SVM). The accuracy rate achieved using the proposed technique, i.e., GA-SVM is 91.3%, sensitivity is 90.72%, specificity 91.88, and precision is 92%. The results are evaluated using the matlab software tool. Keywords—leaf diseases, k-mean, firefly optimization algorithm, support vector machine I. INTRODUCTION Many crops get destroyed due to lack of technical knowledge. One of the important sources of income for people in India is agriculture. A variety of crops are grown by farmers, but one reason for the destruction of crops diseases. Plant disease is the primary cause of crop damage in India. Different plants suffer from different diseases. The central part is the leaf of a plant which is Manuscript received July 11, 2022; revised August 21, 2022; accepted September 20, 2022; published February 22, 2023. used to examine the disease with the help of agriculture experts. However, this kind of detection of diseases in plants was costly and time-consuming. Hence, a better method was required to detect diseases in the leaf. Computer and software play an important role in the identification and classification of leaf diseases. For leaf disease detection, there are lots of image processing and pattern recognition techniques that can be used. The key to prevent agricultural loss is to identify the disease at the early stage. For every disease there should be a remedy which should be stored in the database for early prevention of damage. Plant illness can straightforwardly prompt hindered development causing terrible impacts on yields [1]. A financial loss of up to $20 billion every year is assessed all around the world [2]. Various conditions are the most troublesome test for specialists because of the geographic contrasts that might prevent the exact distinguishing proof [3]. Also, customary strategies chiefly depend on subject matter experts, experience, an-d manuals [4], yet most of them are costly, time consuming, and work concentrated with trouble identifying exactly [5]. Consequently, a fast and exact way to deal with recognizes plant infections appear to be so pressing to assist business and nature to agribusiness. In rural harvests, leaves assume a crucial part to give data about the sum and nature of agricultural yield. A few elements influence food creation, for example, environmental change, presence of weed, and infertility of soil. Aside from that, plant or leaf sickness is a worldwide danger to the development of a few horticultural items and a wellspring of monetary misfortunes [6]. The inability to analyze contaminations or microscopic organisms in plants drives consequently to inadequate pesticide/fungicide use. Consequently, plant sicknesses have been generally thought to be in mainstream researchers, with an attention on the organic provisions of illnesses. To resolve these issues, it is important to recognize plant infections by the techniques which are advanced and intelligent. The advanced techniques to detect the disease facing noise related issues while capturing the images. Filtering of noise is used to delete the incorrect instances in the data reduction of noise and