ISSN 2348-1196 (print) International Journal of Computer Science and Information Technology Research ISSN 2348-120X (online) Vol. 8, Issue 3, pp: (253-263), Month: July - September 2020, Available at: www.researchpublish.com
PREDICTION OF CROP YIELD AND COST BY FINDING BEST ACCURACY USING MACHINE LEARNING 1
3
Varadharajan V, 2Rajasekaran S, 3Mr. Dr. P.Mohan kumar
1
Computer Science and Engineering, Jeppiaar SRR Engineering College, Padur, Chennai
2
Computer Science and Engineering, Jeppiaar SRR Engineering College, Padur, Chennai
Head of Department, Professor, Computer Science and Engineering, Jeppiaar SRR Engineering College, Padur, Chennai
Abstract: Among worldwide, agriculture has the major responsibility for improving the economic contribution of the nation. However, still the most agricultural fields are under developed due to the lack of deployment of ecosystem control technologies. Due to these problems, the crop production is not improved which affects the agriculture economy. Hence a development of agricultural productivity is enhanced based on the plant yield prediction. To prevent this problem, Agricultural sectors have to predict the crop from given dataset using machine learning techniques. The analysis of dataset by supervised machine learning technique(SMLT) to capture several information’s like, variable identification, uni-variate analysis, bi-variate and multi-variate analysis, missing value treatments etc. A comparative study between machine learning algorithms had been carried out in order to determine which algorithm is the most accurate in predicting the best crop. The results show that the effectiveness of the proposed machine learning algorithm technique can be compared with best accuracy with entropy calculation, precision, Recall, F1 Score, Sensitivityand Specificity. Keywords: dataset, Machine learning-Classification method.
I. INTRODUCTION In developing countries, farming is considered as the major source of revenue for many people. In modern years, the agricultural growth is engaged by several innovations, environments, techniques and civilizations. In addition, the utilization of information technology may change the condition of decision making and thus farmers may yield the best way. For decision making process, data mining techniques related to the agriculture are used. Data mining is a process of extracting the most significant and useful information from the huge amount of datasets. Nowadays, we used machine learning approach with developed in crop or plant yield prediction since agriculture has different data like soil data, crop data, and weather data. Plant growth prediction is proposed for monitoring the plant yield effectively through the machine learning techniques. RELATED WORKS: 1) ZHENG Guanghui1, Dongryeol RYU2,∗, JIAO Caixia1 and HONG Changqiao1: Estimation of Organic Matter Content in Coastal Soil Using Reflectance Spectroscopy Research, 2015. Rapid determination of soil organic matter (SOM) using regression models based on soil reflectance spectral data serves an important function in precision agriculture. ―Deviation of arch‖ (DOA)-based regression and partial least squares regression (PLSR) are two modeling approaches to predict SOM. However, few studies have explored the accuracy of the DOA-based regression and PLSR models. 2) Zhiqiang Cheng 1,2 ID , JihuaMeng 1,*, YanyouQiao 1, Yiming Wang 1,2, Wenquan Dong 1 and Yanxin Han 1,2, Preliminary Study of Soil Available Nutrient Simulation Using a Modified WOFOST(Model and Time-Series Remote Sensing Observations), 2017.
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