Rice Crop Yield Prediction using Data Mining Techniques: An Overview

Patil, D.S.; Shirdhonkar, M .S

doi:10.23956/ijarcsse/sv7i5/0135

Cited by 20 publications

(8 citation statements)

References 5 publications

(4 reference statements)

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“…Hence, based on the structure, we may also get to know in what land crops yield most. e soil type is also a very dominant factor in farming to efficiently harvest the crop [5]. We have considered 19 essential soil types which are categorized into calcareous alluvium, noncalcareous alluvium, acid basin clay, floodplain calcareous brown, floodplain calcareous grey, floodplain calcareous dark grey, floodplain noncalcareous grey floodplain soil, noncalcareous dark grey floodplain soil, peat, made land, noncalcareous brown, shallow terrace red-brown, deep terrace red-brown, terrace mottled brown, terrace shallow grey, terrace deep grey, valley grey, and hill brown for the research [22].…”

Section: Descriptions Of the Datasetmentioning

confidence: 99%

“…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 economies in the world, smart agriculture can have a profound impact on Bangladesh [6].…”

Section: Introductionmentioning

confidence: 99%

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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: Descriptions Of the Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction Approaches for Smart Cultivation: A Comparative Study

et al. 2021

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“…Their results showed the proposed selection tree is capable of classifying all varieties of agriculture records. A yield prediction version became proposed in one in the entire take a glance at [11] which makes use of mining techniques for category and prediction. This model worked on entering parameters crop name, land location, soil type, soil ph, pest information, climate, water stage, seed type, and this model anticipated the plant boom and plant diseases and so enabled to pick the good crop supported climate information and required parameters.…”

Section: Related Workmentioning

confidence: 99%

Predictive model construction for prediction of soil fertility using decision tree machine learning algorithm

Jayalakshmi

2021

krj

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Agriculture sector is recognized as the backbone of the Indian economy that plays a crucial role in the growth of the nation’s economy. It imparts on weather and other environmental aspects. Some of the factors on which agriculture is reliant are Soil, climate, flooding, fertilizers, temperature, precipitation, crops, insecticides, and herb. The soil fertility is dependent on these factors and hence difficult to predict. However, the Agriculture sector in India is facing the severe problem of increasing crop productivity. Farmers lack the essential knowledge of nutrient content of the soil, selection of crop best suited for the soil and they also lack efficient methods for predicting crop well in advance so that appropriate methods have been used to improve crop productivity. This paper presents different Supervised Machine Learning Algorithms such as Decision tree, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) to predict the fertility of soil based on macro-nutrients and micro-nutrients status found in the dataset. Supervised Machine Learning algorithms are applied on the training dataset and are tested with the test dataset, and the implementation of these algorithms is done using R Tool. The performance analysis of these algorithms is done using different evaluation metrics like mean absolute error, cross-validation, and accuracy. Result analysis shows that the Decision tree is produced the best accuracy of 99% with a very less mean square error (MSE) rate.

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“…Timely and economic agricultural observance is essential to attain these goals. In this context, crop yield estimation is crucial for checking and making higher cognitive processes like crop insurance, money market foretelling, and addressing food security problems ( Patil and Shirdhonkar, 2017 ). With the drastic improvement in technology, the objective of the present study is to use the machine learning algorithms ( Medar et al, 2019 ) and control systems to change the procedure and enhance the productivity ( Sriram et al, 2019 ) of crops ( Zingade et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms

Anbananthen

Subbiah²,

Deisy³

et al. 2021

F1000Res

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Background: In recent times, digitization is gaining importance in different domains of knowledge such as agriculture, medicine, recommendation platforms, the Internet of Things (IoT), and weather forecasting. In agriculture, crop yield estimation is essential for improving productivity and decision-making processes such as financial market forecasting, and addressing food security issues. The main objective of the article is to predict and improve the accuracy of crop yield forecasting using hybrid machine learning (ML) algorithms. Methods: This article proposes hybrid ML algorithms that use specialized ensembling methods such as stacked generalization, gradient boosting, random forest, and least absolute shrinkage and selection operator (LASSO) regression. Stacked generalization is a new model which learns how to best combine the predictions from two or more models trained on the dataset. To demonstrate the applications of the proposed algorithm, aerial-intel datasets from the github data science repository are used. Results: Based on the experimental results done on the agricultural data, the following observations have been made. The performance of the individual algorithm and hybrid ML algorithms are compared using cross-validation to identify the most promising performers for the agricultural dataset. The accuracy of random forest regressor, gradient boosted tree regression, and stacked generalization ensemble methods are 87.71%, 86.98%, and 88.89% respectively. Conclusions: The proposed stacked generalization ML algorithm statistically outperforms with an accuracy of 88.89% and hence demonstrates that the proposed approach is an effective algorithm for predicting crop yield. The system also gives fast and accurate responses to the farmers.

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Rice Crop Yield Prediction using Data Mining Techniques: An Overview

Cited by 20 publications

References 5 publications

Prediction Approaches for Smart Cultivation: A Comparative Study

Prediction Approaches for Smart Cultivation: A Comparative Study

Predictive model construction for prediction of soil fertility using decision tree machine learning algorithm

An intelligent decision support system for crop yield prediction using hybrid machine learning algorithms

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