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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农业是我们国家的主要生计来源。 当前面临水资源短缺、供需失控的成本以及天气不确定性等挑战，农民必须配备智能农业。 尤其需要解决由于气候变化不确定、灌溉设施差、土壤肥力下降和传统耕作技术而导致作物产量低的问题。 机器学习就是一种用于预测农业作物产量的技术。 各种机器学习技术如预测、分类、回归和聚类被用来预测作物产量。 人工神经网络、支持向量机、线性和逻辑回归、决策树、朴素贝叶斯是一些用于实现预测的算法。 然而，从可用算法池中选择合适的算法给研究人员带来了关于所选作物的挑战。 在本文中，对各种机器学习算法如何用于预测作物产量进行了调查。 已经提出了一种在大数据计算范式中使用机器学习技术预测作物产量的方法。
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
© IAEME Publication
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
0
norm, l
1
norm, l
2
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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