Combining SMOTE Sampling and Machine Learning for Forecasting Wheat Yields in France

Chemchem, Amine; Alin, François; Krajecki, Michaël

doi:10.1109/aike.2019.00010

Cited by 21 publications

(12 citation statements)

References 14 publications

(12 reference statements)

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“…A classification task begins with build training data for which the target values (or class assignments) are known. Many classification algorithms use different techniques for finding relations between the predictor attributes values and the target attributes values in the build data [14] [15]. In the following subsections, a summarised overview of the implemented machine learning algorithms is reported.…”

Section: The Proposed Strategymentioning

confidence: 99%

Towards a Hierarchical Deep Learning Approach for Intrusion Detection

Alin¹,

Chemchem²,

Nolot³

et al. 2020

Lecture Notes in Computer Science

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Nowadays, it is almost impossible to imagine our daily life without Internet. This strong dependence requires an effective and rigorous consideration of all the risks related to computer attacks. However traditional methods of protection are not always effective, and usually very expensive in treatment resources. That is why this paper presents a new hierarchical method based on deep learning algorithms to deal with intrusion detection. This method has proven to be very effective across traditional implementation on four public datasets, and meets all the other requirements of an efficient intrusion detection system.

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Section: The Proposed Strategymentioning

confidence: 99%

Towards a Hierarchical Deep Learning Approach for Intrusion Detection

Alin¹,

Chemchem²,

Nolot³

et al. 2020

Lecture Notes in Computer Science

Self Cite

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“…Chemchem, Alin, Michael [9] created a classifier model using random forest in order to forecast the wheat yields. They used SMOTE as a pre-treatment in order to boost the accuracy of the model.…”

Section: Crop Yield Prediction Using Gradient Boosting Regressionmentioning

confidence: 99%

Crop Yield Prediction using Gradient Boosting Regression

Mishra*,

Khan,

Baranidharan

2020

IJITEE

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Achieving greater crop yields remains a pressing challenge for both farmers and governments. This research examines the use and implementation of Gradient Boosting Regression in predicting crop yields for numerous districts in France. XGBoost, an efficient, optimized and flexible distributed gradient boosting library was used. Agricultural data was sourced from the CLAND Institute’s ‘Crop Data Challenge 2018’, which contains approximately 38 years of maize data compiled by various departments from the months of January to September from regions in France. Average monthly climatic parameters such as evapotranspiration, maximum and minimum temperature, cumulative precipitation, yield anomaly, solar radiation levels and an irrigation coefficient were used as input variables. The best result obtained was an RMSE value of 0.755 and MAE error of 0.54 was obtained using a tuned XGBoost Regressor trained on original variables. This paper aims to compare various regression techniques in order to improve yield prediction thus giving farmers a chance to improve their cultivation with better insights as well as enabling them to harness the power of predictive analytics.

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“…That is, agricultural data are often imbalanced, sparse, and riddled with noise [16]- [23]. For example, in disease prediction, data are often imbalanced leading to poorer classification models, and sometimes overly optimistic model performance estimates [24], [25]. Further, the commentary on critical aspects such as model trustworthiness is very limited.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Precision Agriculture: A Survey on Trends, Applications and Evaluations Over Two Decades

et al. 2022

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Precision agriculture represents the new age of conventional agriculture. This is made possible by the advancement of various modern technologies such as the internet of things. The unparalleled potential for data collection and analytics has resulted in an increase in multi-disciplinary research within machine learning and agriculture. However, the application of machine learning techniques to agriculture seems to be out of step with core machine learning research. This gap is further exacerbated by the inherent challenges associated with agricultural data. In this work, we conduct a systematic review of a large body of academic literature published between 2000 and 2022, on the application of machine learning techniques to agriculture. We identify and discuss some of the key data issues such as class imbalance, data sparsity and high dimensionality. Further, we study the impact of these data issues on various machine learning approaches within the context of agriculture. Finally, we identify some of the common pitfalls in the machine learning and agriculture research including the misapplication of machine learning evaluation techniques. To this end, this survey presents a holistic view on the state of affairs in the cross-domain of machine learning and agriculture and proposes some suitable mitigation strategies to address these challenges.

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Combining SMOTE Sampling and Machine Learning for Forecasting Wheat Yields in France

Cited by 21 publications

References 14 publications

Towards a Hierarchical Deep Learning Approach for Intrusion Detection

Towards a Hierarchical Deep Learning Approach for Intrusion Detection

Crop Yield Prediction using Gradient Boosting Regression

Machine Learning in Precision Agriculture: A Survey on Trends, Applications and Evaluations Over Two Decades

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