Research Article IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms Ashay Rokade , 1 Manwinder Singh , 1 Sandeep Kumar Arora , 1 and Eric Nizeyimana 2 1 School of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India 2 College of Science and Technology, University of Rwanda, Rwanda Correspondence should be addressed to Manwinder Singh; manwinder.25231@lpu.co.in, Sandeep Kumar Arora; sandeep.16930@lpu.co.in, and Eric Nizeyimana; nizerik@yahoo.fr Received 3 July 2022; Revised 9 August 2022; Accepted 16 August 2022; Published 30 August 2022 Academic Editor: Muhammad Asghar Copyright © 2022 Ashay Rokade et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In the farming industry, the Internet of Things (IoT) is crucial for boosting utility. Innovative agriculture practices and medical informatics have the potential to increase crop yield while using the same amount of input. Individuals can benefit from the Internet of Things in various ways. The intelligent farms require the creation of an IoT-based infrastructure based on sensors, actuators, embedded systems, and a network connection. The agriculture sector will gain new advantages from machine learning and IoT data analytics in terms of improving crop output quantity and quality to fulfill rising food demand. This paper described an intelligent medical informatics farming system with predictive data analytics on sensing parameters, utilizing a supervised machine learning approach in an intelligent agricultural system. The four essential components of the proposed approach are the cloud layer, fog layer, edge layer, and sensor layer. The primary goal is to enhance production and provide organic farming by adjusting farming conditions as per plant needs that are considered in experimentation. The use of machine learning on acquired sensor data from a prototype embedded model is investigated for regulating the actuators in the system. Then, an analytics and decision-making system was built at the fog layer, employing two supervised machine learning approaches including classification and regression algorithms using a support vector machine (SVM) and artificial neural network (ANN) for effective computation over the cloud layer. The experimental results are evaluated and analyzed in MATLAB software, and it is found that the classification accuracy using SVM is much better as compared to ANN and other state of art methods. 1. Introduction Data gathering and using data to inform practical farming decisions is undergoing a significant agricultural revolution. Intelligent culture is the request of modern news and ideas of technology (ICT) in farming, to degree machine intelligence algorithms, and the rationalization of raw material use, as a capital-located system and state-of-the-art electronics in drink farming in tenable and environmentally intimate habits. Innovative technologies are helping the plurality of people everywhere the experience in a type of ways. The Internet of Things (IoT) and dossier data, such as grown dossier data and data learning, are immediately playing a more and more critical duty in people’s everyday lives, admitting them to change their environment more surely [1–3]. In general, IoTs and data reasoning are secondhand in the agromodern and environmental subdivisions for two together diagnostics and control of brilliant culture arrange- ments, to provide essential facts to the final laborer and ser- vices about the footing and properties of agroproduction and structures [4–6]. Figure 1 shows the overview of IoT-based smart agriculture factors. Machine learning is being used to regulate actuators’ intelligence. The algorithm uses data acquired about the plants’ climatological and soil conditions to advise the farmer on what should be done efficiently. IoT is also Hindawi Computational and Mathematical Methods in Medicine Volume 2022, Article ID 8434966, 15 pages https://doi.org/10.1155/2022/8434966