Crop Yield Prediction using Machine Learning Techniques

Medar, Ramesh; Rajpurohit, Vijay S.; Shweta, Shweta

doi:10.1109/i2ct45611.2019.9033611

Cited by 105 publications

(22 citation statements)

References 2 publications

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“…ree different statistical models, namely, time series, panel, and crosssectional models, were used to predict climate change based on the given data. Similar proposals have been presented in [18,19].…”

Section: Background Studysupporting

confidence: 66%

Prediction Approaches for Smart Cultivation: A Comparative Study

et al. 2021

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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. This 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.

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Section: Background Studysupporting

confidence: 66%

Prediction Approaches for Smart Cultivation: A Comparative Study

et al. 2021

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“…(Ami & Vinita, 2016) states that yield prediction is a very important agricultural problem that remains to be solved and it can be solved by employing data mining techniques like clustering and classification via selecting the most appropriate method for the task. (Medar et al, 2019) analyzed result of Multiple Linear Regression, Regression Tree, K-nearest Neighbor and Artificial Neural Network on Groundnut data of previous 8 years and they have done prediction based on soil, environmental and abiotic attributes. KNN algorithm had been given the best result compared to other algorithms for Groundnut crop yield prediction.…”

Section: Applications Of Data Mining (Rq2)mentioning

confidence: 99%

A Survey of Finding Trends in Data Mining Techniques for Social Media Analysis

Nanayakkara

Kumara

Rathnayaka

2021

SL J. Soc. Sci. Hum.

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Social media have become very popular in the last few decades. Users rely on social network sites like Twitter, Facebook, YouTube, and LinkedIn for both information and entertainment needs. Social media analytics with data mining technology could be an analysis axis centered on extracting trends, patterns, and rules from the social media pool, to serve the people and organizations to have optimum choices concerning many disciplines. The traditional media analytical techniques appear obsolete and inadequate to gratify this immense array of unstructured social media knowledge characterized by three key problems namely; size, noise, and dynamism, predominantly shifting from the batch scale to the streaming one. The objective of this study is to investigate the data mining techniques that were used by social media networks during the years 2010 and 2020. The effort is a systematic review of content analysis in studies within the field of social media analytics that was published in principal databases. 125 articles were reviewed in this paper. Content analysis was implemented based on their approach, tools utilized, language, the dataset used, country, year, and nature of the experiment. The review discovered that 22 data mining techniques were employed with social media data while frequently used in Artificial Neural Network (ANN), Bayesian networks (BN) and Support Vector Machine (SVM), K-means Clustering, and Neuro-Fuzzy Logic Approach. The study has focused to assist the involved analyzers and educators to capture the research trends and problems associated with the Social media analytics process with future research initiatives.

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“…The prediction made by machine learning algorithms will help the farmer to decide which crop to grow to get the maximum yield by considering factors like temperature, rainfall, area etc. [3] In this paper, predicts the yield of almost all kinds of crops that are planted in India. This script makes noval by the usage of simple parameters like state ,district ,season, area and the user can predict the yield of the crop in which year he or she wants to the paper uses advanced regration techniques like Kernel Ridge, Lasso and ENet algorithms to predict the yield and uses the concept of Stacking Regression for enhancing the algorithms to give a better prediction.…”

Section: Related Workmentioning

confidence: 99%

Agro Expert Using Google API

Hiralal¹,

Ashok²,

Sunil³

et al. 2021

IJCSMC

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Agriculture is the backbone of Indian economy. Mostly crops are loss due to the erroneous selection of the crop, climate change. The farmers are generally not aware of the requirements of the crops. i.e. The minerals, soil moisture & other soil requirements one more problem that a farmer generally encounters is the pest & diseases that can affect the crops they grow which they are generally unaware of in an early stage. This are the problems of farmer is addressed in our paper and we have tried to solve it with the help of Agro Expert using Google API system by the help of our model. We predict the suitable crop to the farmers and also detect the pest and may affect as suggest the pest control technique. In this paper we have applied collaborative filtering algorithm, aprori algorithm, linear regration prediction technique.

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Crop Yield Prediction using Machine Learning Techniques

Cited by 105 publications

References 2 publications

Prediction Approaches for Smart Cultivation: A Comparative Study

Prediction Approaches for Smart Cultivation: A Comparative Study

A Survey of Finding Trends in Data Mining Techniques for Social Media Analysis

Agro Expert Using Google API

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Crop Yield Prediction using Machine Learning Techniques

Cited by 105 publications

References 2 publications

Prediction Approaches for Smart Cultivation: A Comparative Study

Prediction Approaches for Smart Cultivation: A Comparative Study

A Survey of Finding Trends in Data Mining Techniques for Social Media Analysis

Agro Expert Using Google API﻿

Contact Info

Product

Resources

About

Agro Expert Using Google API