From spreadsheets to sugar content modeling: A data mining approach

Oliveira, Monique Pires Gravina de; Bocca, Felipe Ferreira; Rodrigues, Luiz Henrique Antunes

doi:10.1016/j.compag.2016.11.012

Cited by 20 publications

(12 citation statements)

References 32 publications

(45 reference statements)

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“…Some of these techniques, such as the kmeans, the k nearest neighbor, artificial neural networks and support vector machines, are discussed and an application in agriculture for each of these techniques is presented. In addition, the references included in brackets confirmed the ability and necessity of using data mining technique in agriculture (Raorane & Kulkarni, 2013;Kalpana et al, 2014;Geetha, 2015;Khedr et al, 2015;Raorane & Kulkarni, 2015;Almaliki et al, 2016;Almaliki, 2017;Oliveira et al, 2017;Thomas, 2017). Therefore, the energy used in wheat production was predicted and modelled using data mining techniques (Artificial Neural Network).…”

Section: Introductionmentioning

confidence: 87%

Energy Prediction of Wheat Production Using Data Mining Technique in Iran

Monjezi

2021

Basrah J. Agric. Sci.

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Wheat is considered as one of the most important products in Iran. Concerning high cultivation area of wheat in Khuzestan, an instrument is required to process stored data in order to give information resulted from such processing to managers of agricultural sectors. Data mining technique is able to give essential information and models to producers of wheat for modelling energy consumption. One of the most practical algorithms is an artificial neural network. The main aim of this research is to predict output energy of wheat farms using a multilayer perceptron neural network. This is an analytic research and its database consists of 1240 records. Data required for the research was obtained from wheat farm during 2014-2018. Data analysis was done via IBM SPSS modeller 14.2 and standard CRISP. Concerning the model used in the research, it was found that variables of chemical fertilizers, machinery & diesel fuel with coefficients of 0.2987, 0.2064 and 0.1527 respectively had the highest effect on output variable (productive energy). Amount of prediction precision in neural network algorithm, meaning ratio of correctly predicted records to total records was 93.08%. Also, linear correlation between actual values and predicted values were 0.92 and 0.88 respectively, for training data and testing data suggesting strong correlation. The results obtained can be effective for wheat farmers in direction of evaluation and optimization of energy consumption in process of wheat production and reduction of consumption of energy inputs.

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Section: Introductionmentioning

confidence: 87%

Energy Prediction of Wheat Production Using Data Mining Technique in Iran

Monjezi

2021

Basrah J. Agric. Sci.

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“…It is possible that in areas where management is standardized and industrialized, these cultural practices will have a weak effect. For example, in the model developed by Oliveira et al [25] in Brazil, where sugarcane production is more standardized and industrialized than in Ivory Coast, the importance of meteorological variables outweighs those due to cropping practices.…”

Section: Cropping Practicesmentioning

confidence: 99%

“…In those areas, mechanistic models such as APSIM [19] and CANEGRO [20] that simulate the biological characteristics of plant growth according to the climatic conditions (temperature, rainfall) have been used. Then, statistical models have been developed, ranging from ordinary least squares (OLS) regression [21,22] to more complex algorithms using machine learning such as the random forest method [23][24][25]. In parallel, new methods based on satellite data have emerged [26,27] enabling researchers to estimate yields over large areas at high resolution.…”

Section: Introductionmentioning

confidence: 99%

Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data

et al. 2021

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One way to use climate services in the case of sugarcane is to develop models that forecast yields to help the sector to be better prepared against climate risks. In this study, several models for forecasting sugarcane yields were developed and compared in the north of Ivory Coast (West Africa). These models were based on statistical methods, ranging from linear regression to machine learning algorithms such as the random forest method, fed by climate data (rainfall, temperature); satellite products (NDVI, EVI from MODIS Vegetation Index product) and information on cropping practices. The results show that the forecasting of sugarcane yield depended on the area considered. At the plot level, the noise due to cultivation practices can hide the effects of climate on yields and leads to poor forecasting performance. However, models using satellite variables are more efficient and those with EVI alone may explain 43% of yield variations. Moreover, taking into account cultural practices in the model improves the score and enables one to forecast 3 months before harvest in 50% and 69% of cases whether yields will be high or low, respectively, with errors of only 10% and 2%, respectively. These results on the predictive potential of sugarcane yields are useful for planning and climate risk management in this sector.

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“…Unlike our current work, the data is collected from the micro-level, that is, the farms. [29] used different data mining techniques to predict the sugar content of sugarcane. The performance of the models, like random forest support vector regression and regression trees, is compared on a micro-level data set.…”

Section: Data Mining In Agricultural Issuesmentioning

confidence: 99%

Applying Data Mining to China’s Swine Farming Industry: A Compromise Perspective of Economic, Environmental and Overall Performances

Huang

Feng

et al. 2018

Sustainability

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The economic and environmental performances of the swine farming industry have always resulted in heated discussions in developing countries. Exploring the relationship between these features and the producers' overall performance is the focus of this paper. For constructing multi-objective features that include the above features, a compromise approach for optimization is taken into consideration. For classifying the overall performance into different levels and detecting the effect of economic and environmental features on such features, an iteration scheme is developed in which the overall performance is treated as a target label. By neglecting this target label, a k-means clustering method is then used to help predict the producer's overall performance given their economic and environmental features. In data pre-processing, correlation analysis for feature selection shows that the producer's pollution emission and received regulation intensity largely affect its overall performance, while profit is found to be negatively correlated with pollution emission as regulation intensity is neglected. The classification result derived from the Silhouette Coefficient shows that the data set can be efficiently split into different groups in terms of the producer's overall performance. The average distance between the objects in the low-performance group is larger than that of the high-performance group. The threshold position between the two groups is found to be largely dependent on the features of pollution emission and regulation intensity. The clustering result obtained by the k-means method shows good effectiveness and efficiency in separating the objects into different groups based on various features other than the overall performance. In 2-and 3-cluster cases, the result also shows evidence of the impact of economic and environmental features on the clustering result. The cross-validation analysis under a set of randomly chosen splitting points shows an increasing out-of-sample prediction quality with increases in training sample size. As one of the by-products of this paper, the geographical distribution in the clustering result is found partially consistent with the official report from Chinas central government regarding advantageous regions within the industry. In addition to current research, the ease of using the knowledge obtained in this paper for transfer learning is discussed.

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From spreadsheets to sugar content modeling: A data mining approach

Cited by 20 publications

References 32 publications

Energy Prediction of Wheat Production Using Data Mining Technique in Iran

Energy Prediction of Wheat Production Using Data Mining Technique in Iran

Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data

Applying Data Mining to China’s Swine Farming Industry: A Compromise Perspective of Economic, Environmental and Overall Performances

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