Research Article Prediction Approaches for Smart Cultivation: A Comparative Study Amitabha Chakrabarty , 1 Nafees Mansoor , 2 Muhammad Irfan Uddin, 3 Mosleh Hmoud Al-adaileh, 4 Nizar Alsharif, 5 andFawazWaselallahAlsaade 6 1 Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh 2 Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka, Bangladesh 3 Institute of Computing, Kohat University of Science and Technology, Kohat, Pakistan 4 Deanship of E-Learning and Distance Education, King Faisal University, Hofuf, Saudi Arabia 5 Department of Computer Engineering and Science, Al-Baha University, Al Bahah, Saudi Arabia 6 College of Computer Sciences and Information Technology, King Faisal University, Hofuf, Saudi Arabia Correspondence should be addressed to Nafees Mansoor; nafees@nafees.info Received 27 January 2021; Revised 17 March 2021; Accepted 26 March 2021; Published 9 April 2021 Academic Editor: Furqan Aziz Copyright©2021AmitabhaChakrabartyetal.isisanopenaccessarticledistributedundertheCreativeCommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Crop cultivation is one of the oldest activities of civilization. For a long time, crop production was carried out based on knowledge passed from generation to generation. However, due to the rapid growth in the human population of the world, human knowledge-based cultivation is not enough to meet the demanding need. To address this issue, the usage of machine learning- based tools has been studied in this paper. An experiment has been carried out over 0.3 million data. is dataset identifies 46 prominent parameters for cultivation, which is collected from the Department of Agriculture Extension, Bangladesh. Comparison between neural networks and numbers of machine learning algorithms has been carried out in this research. It is observed that the neural network outperforms the other methods by maintaining an average prediction accuracy of 96.06% for six different crops. Other contemporary machine learning algorithms, namely, support vector machine, random forest, and logistic regression, have average prediction accuracy of around 68.9%, 91.2%, and 62.39%, respectively. 1.Introduction To feed the rapidly growing global population, modern-day agriculture faces the demand for rising production of food. Hence, the latest technologies are transpiring in the agri- cultural sector to enhance net productivity by gathering and processing information. Besides, the distressing climate changes have also been hinted at the inevitable demand for modernizing the agriculture domain with the latest tools and technologies. erefore, in the modern era, agricultural and farming domains are adapting and applying state-of-the-art technologies, namely, machine learning and the Internet of things (IoT), as agents for booming the net productivity and utilizing agricultural resources efficiently [1–4]. is has also coined the concept of smart agriculture which has also exposed a new direction of innovative research in the ag- ricultural sector. On the other hand, agriculture remains the single most important avenue for mankind, and therefore in most countries, the largest part of the workforce is in some way involved in this sector [5]. Being one of the most densely populated countries and one of the fastest-growing econ- omies in the world, smart agriculture can have a profound impact on Bangladesh [6]. is in turn can contribute to as much as 17% of the country’s GDP and almost half of the working population who are involved in the agriculture sector in Bangladesh [7]. Bangladesh has around 70% of the agricultural land among its total area where the major local crops are considered to be rice, jute, and wheat [8]. However, the age-old cultivation process is still in practice in Hindawi Complexity Volume 2021, Article ID 5534379, 16 pages https://doi.org/10.1155/2021/5534379