Indonesian Journal of Electrical Engineering and Computer Science Vol. 33, No. 3, March 2024, pp. 1748~1759 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v33.i3.pp1748-1759  1748 Journal homepage: http://ijeecs.iaescore.com Virtual analysis of machine learning models for diseases prediction in muskmelon Deeba Kannan 1 , Balakrishnan Amutha 1 , Sattianadan Dasarathan 2 , Daniel Rosy Salomi Victoria 3 , Vikas Maheshkar 4 , Ravindran Ramkumar 5 , Dhandapani Karthikeyan 2 1 Department of CTECH, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India 2 Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, kattankulathur, Chennai, India 3 Department of Computer Science and Engineering, St. Joseph's College of Engineering, Chennai, India 4 Department of Information Technology, Netaji Subhas University of Technology, New Delhi, India 5 Department of Electrical and Electronics Engineering, School of Engineering and Technology, Dhanalakshmi Srinivasan University, Tiruchirapalli, India Article Info ABSTRACT Article history: Received Oct 24, 2023 Revised Nov 27, 2023 Accepted Jan 3, 2024 Muskmelon, a crop prized for its economic potential, has a relatively brief growth cycle. Disease susceptibility during this period can have a profound impact on yields, posing challenges for farmers. Environmental conditions are pivotal in disease occurrence. Unfavorable conditions reduce the likelihood of pathogens infecting vulnerable host plants as temperature and humidity influence pathogen behavior, including toxin synthesis, virulence protein production, and reproduction. Pathogens can lie dormant in the soil until suitable conditions activate them. When the right environment and host plants align, these dormant pathogens can cause outbreaks. Disease prediction becomes possible by analyzing environmental variables. Real- time data collected via strategically placed sensors focused on viral, fungal, and bacterial infections. Results indicated that the extreme gradient boosting (XGBoost) algorithm, with a maximum tree depth of 4 and 30 trees per iteration, achieved remarkable performance, yielding an accuracy of 97%. For comparison, the XGBoost model outperformed an 8-layer Backpropagation network with 7 nodes per layer, which achieved 95% accuracy. These findings underscore XGBoost's efficacy in forecasting and mitigating muskmelon plant diseases, offering the potential for improved crop yields and agricultural sustainability. Keywords: Back propagation network Disease forecast Machine learning models Plant diseases XGBoost This is an open access article under the CC BY-SA license. Corresponding Author: Dhandapani Karthikeyan Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology Kattankulathur, 603203, Chennai, Tamil Nadu, India Email: karthikd@srmist.edu.in 1. INTRODUCTION Muskmelon is a remunerative crop with a total cultivation time of around 65 to 75 days. The cultivation of muskmelon, however, faces significant challenges due to various diseases that can affect the crop, leading to substantial yield losses and economic implications for growers. Timely and accurate disease prediction and management are essential for maintaining crop health and ensuring a bountiful harvest. Here time is precious and the disease attack at this time duration will be a greater loss for the farmers [1]. Hence forecasting the possibility of a disease attack can alert the farmers to be prepared for any pathogen attack based on environmental conditions. Climatic changes have a direct impact on pathogen development in plants [2]. Major climatic factors considered are temperature, humidity, leaf wetness and soil moisture. Climate change is a predominant factor behind the spread of pests and pathogens. Climate change may have