{"title":"Comparative approach on crop detection using machine learning and deep learning techniques","authors":"V. Nithya, M. S. Josephine, V. Jeyabalaraja","doi":"10.1007/s13198-024-02483-9","DOIUrl":null,"url":null,"abstract":"<p>Agriculture is an expanding area of study. Crop prediction in agriculture is highly dependent on soil and environmental factors, such as rainfall, humidity, and temperature. Previously, farmers had the authority to select the crop to be farmed, oversee its development, and ascertain the optimal harvest time. The farming community is facing challenges in sustaining its practices due to the swift alterations in climatic conditions. Therefore, machine learning algorithms have replaced traditional methods in predicting agricultural productivity in recent years. To guarantee optimal precision through a specific machine learning approach. Authors extend their approach not limited to Machine Learning but also with Deep Learning Techniques. We use machine and deep learning algorithms to predict crop outcomes accurately. In this proposed model, we utilise machine learning algorithms such as Naive Bayes, decision tree, and KNN. It is worth noting that the decision tree algorithm demonstrates superior performance compared to the other algorithms, achieving an accuracy rate of 83%. In order to enhance the precision, we have suggested implementing a deep learning technique, specifically a convolutional neural network, to identify the crops. Achieving an accuracy of 93.54% was made possible by implementing this advanced deep-learning model.</p>","PeriodicalId":14463,"journal":{"name":"International Journal of System Assurance Engineering and Management","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of System Assurance Engineering and Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13198-024-02483-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Agriculture is an expanding area of study. Crop prediction in agriculture is highly dependent on soil and environmental factors, such as rainfall, humidity, and temperature. Previously, farmers had the authority to select the crop to be farmed, oversee its development, and ascertain the optimal harvest time. The farming community is facing challenges in sustaining its practices due to the swift alterations in climatic conditions. Therefore, machine learning algorithms have replaced traditional methods in predicting agricultural productivity in recent years. To guarantee optimal precision through a specific machine learning approach. Authors extend their approach not limited to Machine Learning but also with Deep Learning Techniques. We use machine and deep learning algorithms to predict crop outcomes accurately. In this proposed model, we utilise machine learning algorithms such as Naive Bayes, decision tree, and KNN. It is worth noting that the decision tree algorithm demonstrates superior performance compared to the other algorithms, achieving an accuracy rate of 83%. In order to enhance the precision, we have suggested implementing a deep learning technique, specifically a convolutional neural network, to identify the crops. Achieving an accuracy of 93.54% was made possible by implementing this advanced deep-learning model.
期刊介绍:
This Journal is established with a view to cater to increased awareness for high quality research in the seamless integration of heterogeneous technologies to formulate bankable solutions to the emergent complex engineering problems.
Assurance engineering could be thought of as relating to the provision of higher confidence in the reliable and secure implementation of a system’s critical characteristic features through the espousal of a holistic approach by using a wide variety of cross disciplinary tools and techniques. Successful realization of sustainable and dependable products, systems and services involves an extensive adoption of Reliability, Quality, Safety and Risk related procedures for achieving high assurancelevels of performance; also pivotal are the management issues related to risk and uncertainty that govern the practical constraints encountered in their deployment. It is our intention to provide a platform for the modeling and analysis of large engineering systems, among the other aforementioned allied goals of systems assurance engineering, leading to the enforcement of performance enhancement measures. Achieving a fine balance between theory and practice is the primary focus. The Journal only publishes high quality papers that have passed the rigorous peer review procedure of an archival scientific Journal. The aim is an increasing number of submissions, wide circulation and a high impact factor.