【免费】使用机器学习和大数据技术预测作物产量的方法-研究论文

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http://www.iaeme.com/IJCET/index.asp 110 editor@iaeme.com

International Journal of Computer Engineering and Technology (IJCET)

Volume 10, Issue 03, May-June 2019, pp. 110-118, Article ID: IJCET_10_03_013

Available online at http://www.iaeme.com/ijcet/issues.asp?JType=IJCET&VType=10&IType=3

Journal Impact Factor (2019): 10.5167 (Calculated by GISI) www.jifactor.com

ISSN Print: 0976-6367 and ISSN Online: 0976–6375

AN APPROACH FOR PREDICTION OF CROP

YIELD USING MACHINE LEARNING AND BIG

DATA TECHNIQUES

Kodimalar Palanivel

Department of Computer Science,

Bharathidasan University Constituent Arts & Science College,

Navalurkuttapattu, Tiruchirappalli, TamilNadu, India

Chellammal Surianarayanan

Department of Computer Science,

Bharathidasan University Constituent Arts & Science College,

Navalurkuttapattu, Tiruchirappalli, TamilNadu, India

*Corresponding Author

ABSTRACT

Agriculture is the primary source of livelihood which forms the backbone of our country. Current

challenges of water shortages, uncontrolled cost due to demand-supply, and weather

uncertainty necessitate farmers to be equipped with smart farming. In particular, low

yield of crops due to uncertain climatic changes, poor irrigation facilities, reduction

in soil fertility and traditional farming techniques need to be addressed. Machine

learning is one such technique employed to predict crop yield in agriculture. Various machine learning

techniques such as prediction, classification, regression and clustering are utilized to forecast crop

yield. Artificial neural networks, support vector machines, linear and logistic regression, decision

trees, Naïve Bayes are some of the algorithms used to implement prediction. However, the selection of

the appropriate algorithm from the pool of available algorithms imposes challenge to the researchers

with respect to the chosen crop. In this paper, an investigation has been performed on how various

machine learning algorithms are useful in prediction of crop yield. An approach has been proposed for

prediction of crop yield using machine learning techniques in big data computing paradigm.

Key words: ISTA, IISTA, image restoration, inverse problems, l

norm, l

data fidelity term, regularization function, total variation.

Cite this Article: Kodimalar Palanivel and Chellammal Surianarayanan, An

Approach for Prediction of Crop Yield Using Machine Learning and Big Data

Techniques, International Journal of Computer Engineering and Technology 10(3),

2019, pp. 110-118.

http://www.iaeme.com/IJCET/issues.asp?JType=IJCET&VType=10&IType=3

Electronic copy available at: https://ssrn.com/abstract=3555087

An Approach for Prediction of Crop Yield Using Machine Learning and Big Data Techniques

http://www.iaeme.com/IJCET/index.asp 111 editor@iaeme.com

1. INTRODUCTION

Agriculture proves to be a major factor of Indian economy and it involves production of

crops. Crops may be either food crops or commercial crops. Food crops include paddy, wheat,

maize, grams, millets, etc., whereas commercial crops are sugarcane cotton, groundnut,

cashew, etc. The productivity of the crops is significantly influenced by weather conditions

[1]. Hence, accurate yield prediction is a major problem that ought to be addressed. Early

prediction of yield would facilitate the farmers to make precautionary actions to improve

productivity. Early prediction is possible through collection of previous experience of the

farmers, weather conditions and other influencing factors and; store it in a large database.

The common input parameters are rainfall, temperature, humidity, solar radiation, crop

population density, fertilizer application, irrigation, tillage, type of soil, depth, farm capacity,

and soil organic matter. By applying data mining techniques such as prediction, classification

and clustering, early decisions are possible.

1.1. Need for prediction

Estimating agricultural yield prior to harvest is an Estimating agricultural yield prior to

harvest is an important issue in agriculture, as the changes in crop yield from year to year

influence international business, food supply, and global market prices. Also, early prediction

of crop yield provides useful information to policy planners. Appropriate prediction of crop

productivity is required for efficient planning of land usage and economic policy. In recent

times, forecasting of crop productivity at the within-field level has increased. The most

influencing factor for crop productivity is weather conditions. If the weather based prediction

is made more precise, then farmers can be alerted well in advance so that the major loss can

be mitigated and would be helpful for economic growth. The prediction will also aid the

farmers to make decisions such as the choice of alternative crops or to discard a crop at an

early stage in case of critical situations. Further, predicting crop yield can facilitate the

farmers to have a better vision on cultivation of seasonal crop and its scheduling. Thus, it is

necessary to simulate & predict the crop yield before cultivation for efficient crop

management and expected outcome. As there exists a non-linear relationship between crop

yield and the factors influencing crop, machine learning techniques might be efficient for

yield predictions.

2. MACHINE LEARNING TECHNIQUES

Machine Learning involves problems in which the input and output relationship is not known.

Learning specifies the automatic acquirement of structural descriptions. In contrast to

traditional statistical methods, machine learning does not make assumptions about the exact

construct of the data model, which describes the data. This feature is very helpful to describe

complex non-linear behaviors such as a crop yield prediction. Machine learning is a part of

artificial intelligence employed to build an intelligent system [2]. By utilizing the training

samples, the test samples can be identified. The accuracy of the system can be measured using

metrics such as mean square error, root mean square error, precision, recall, sensitivity

specificity etc. Further, machine learning can be employed to address a variety of applications

including crop yield prediction [3] through supervised, unsupervised and reinforcement

learning methods. Classification, clustering, regression, prediction are some of the techniques

involved to attain the intelligent system. In this study, prediction is considered and the

methods used for prediction are elaborated in the following subsection.

Meteorological conditions, such as precipitation, temperature, soil conditions, topography

and socio-economic factors are responsible for about 30% growth of the crops. Several works

were proposed in the literature for predicting the yield of crop using expert systems,

Electronic copy available at: https://ssrn.com/abstract=3555087

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