2023 IEEE World Conference on Applied Intelligence and Computing 979-8-3503-1006-1/23/$31.00 ©2023 IEEE 1008 DOI: 10.1109/AIC.2023.168 Markov Chain Model Used in Agricultural Yield Predictions Utilizing on Indian Agriculture Amit Gupta Deaprtment of AI & ML Nalla Malla reddy College of Engineering Telangana, India dramitguptacv@gmail.com Shashi Kant Dargar Department of ECE Kalasalingam Academy of Research & Education Tamilnadu, India drshashikant.dargar@ieee.org Abha Dargar Department of ECE Kalasalingam Academy of Research & Education Tamilnadu, India abha@klu.ac.in M. Senthil Kumar Deaprtment of ECE Nalla Malla reddy College of Engineering Telangana, India kathir_senthil@yahoo.co.in Abdul Majid Deaprtment of AI & DS Nalla Malla reddy College of Engineering Telangana, India dr.majid.wahab@gmail.com M. Raju Deaprtment of CSE Nalla Malla reddy College of Engineering Telangana, India raj.jntu9@gmail.com Abstract—A new problem is how to adjust farming operations to shifting climatic conditions while using cutting- edge agricultural technologies and novel seed varieties. IoT offers a fresh method for adjusting to future problems as a result. Historically, agricultural production was significantly influenced by the weather. The current climate catastrophe has dramatically changed this norm, though. Older seed kinds still generate superior yields when cared for correctly, whereas newer seed varieties have evolved in response to certain climatic and moisture requirements. In this study, we set out to train the machinery to recognize significant developmental stages in the life cycle of the crop, so that it can advise the farmer on how to best care for the machine depending on the circumstances under which the crop thrives. Instead of striving to create a system with predetermined thresholds, this improved agricultural initiative seeks to protect the environment while also meeting the crop’s shifting needs throughout the course of its whole life cycle. Depending onwhere the crop is in its lifecycle and how long it is expected to last, the farmer may search for new approaches. Any device having an internet connection and the capability to attach sensorsmay be utilized to carry out all of these procedures. Keywords—Crop Yield Prediction, Precision Agriculture, Weather Conditions, and Deep Learning. I. INTRODUCTION The farmers of India are the foundation of the nation. Since they produced the majority of their own food in prehistoric times, humans’ success in agriculture is largely responsible for their ability to adapt to environmental changes. Birds, animals, and humans all profit from the cultivation and use of organic grains. Drought causes enormous losses to farmers and gardeners every year. Water scarcity and drought have reduced agricultural crop production and caused serious problems for farmers [1]. The creature is as healthy and satisfied as ever, thanks to its diet of the abundant fruit available on Earth. It’s no secret that as more advanced technology and techniques have been accessible, the agriculture industry has shrunk. As a result of these multiple innovations, artificial items, which are hybrid commodities, are being made at an increasing rate, contributing to the already dismal situation of public health [2-4]. People nowadays frequently neglect the significance of producing crops at the correct time and in the proper location. Weather patterns are only one illustration of how certain farming practices contribute to food insecurity. They also jeopardize water, air, and land, which are all required for food production. Following a careful analysis of all issues and obstructions, including the environment, heat, and various other considerations, we have decided that there is no practical solution or technical innovation that may help us leave our situation. Several more methods might be used to help India’s agriculture business flourish. Various solutions exist for increasing agricultural output and efficiency. Forecasting future harvests is another application for data mining technologies [5]. Data mining is the process of extracting valuable information from enormous databases.Data mining software aims to analyze data by allowing for its discovery, categorization, and summary from many perspectives. Data mining is a technique for discovering hidden patterns and correlations in huge relational databases with numerous columns. It’s possible that some surprising conclusions might be drawn from the network of relationships and patterns in this data. Information may be mined to acquire a better understanding of historical trends as well as future predictions. Farmers, for example, might utilize agricultural production data summaries to discover the root reasons of crop failures and apply required treatments [6, 7]. Crop yields are difficult to predict, which a huge concern for the agricultural industry is. Every farmer has no notion what kind of harvest to anticipate. Farmers’ previous 2023 IEEE World Conference on Applied Intelligence and Computing (AIC) | 979-8-3503-1006-1/23/$31.00 ©2023 IEEE | DOI: 10.1109/AIC57670.2023.10263850 Authorized licensed use limited to: Indian Institute of Technology Indore. Downloaded on March 06,2024 at 06:51:18 UTC from IEEE Xplore. Restrictions apply.