An Ensemble Learning Model for Agricultural Irrigation Prediction

Chen, Yanan; Hsieh, Wen-Hao; Ko, Yu-Shuo; Huang, Nen-Fu

doi:10.1109/icoin50884.2021.9333852

Cited by 26 publications

(14 citation statements)

References 7 publications

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“…A technique to increase the classification accuracy is combining the decisions of multiple independent base classifiers to achieve a booster classifier, which is called ensemble classifier. Ensemble learning is a machine learning paradigm in which multiple learners are trained to solve the same problem [39]. A form of ensemble learning is known as boosting, in which a set of simple classifiers that are also called weaker learners, are combined to construct a relatively stronger classifier [40,41].…”

Section: Introductionmentioning

confidence: 99%

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Bakhshipour

2021

IEEE Access

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Crop and weeds identification is of important steps towards the development of efficient automotive weed control systems. The higher the accuracy of plant detection and classification, the higher the performance of the weeding machine. In this study, the capability of two popular boosting methods including Adaboost.M1 and LogitBoost algorithms was evaluated to enhance the plant classification performance of four classifiers, namely Multi-Layer Perceptron (MLP), k-Nearest Neighbors (kNN), Random Forest (RF), and Support Vector Machine (SVM). Four feature filtering techniques including Correlationbased Feature Selection (CFS), Information Gain (IG), Gain Ratio (GR), and OneR were applied to the imageextracted features and 10 of the most significant features were selected and fed into single and boosted classifiers. The RF model trained by IG selected features (IG-RF) was the most appropriate classifier among the evaluated models whether in single or boosted modes. It was also found that boosting of IG-RF by using Adaboost.M1 and LogitBoost algorithms improved the classification accuracy. Regarding the performance values, the LogitBoost-IG-RF structure, which provided a classification accuracy of 99.58%, a kappa (k) of 0.9948, and a Root Mean Squared Error (RMSE) of 0.0688 on training dataset, was selected as the most appropriate classifier for plant discrimination in peanut fields. The accuracy, k, and RMSE criteria of this combination on test dataset were 95.00%, 0.9375, and 0.1591, respectively. It was concluded that combination of boosting algorithms and feature selection methods can promote plant type discrimination accuracy, which is a crucial factor in the development of precision weed control systems.

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Section: Introductionmentioning

confidence: 99%

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Bakhshipour

2021

IEEE Access

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“…In a study by Chen et al [52], an ensemble learning model was used to predict the irrigation volume needed daily by crops, based on the agricultural IoT system. About four models, including linear SVR, support linear regression, Adaboost DT, and RF were trained to benchmark the performance of the intelligent irrigation system.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

“…These data are transferred over the gateway and then loaded into a cloud server database. Farmers may utilize mobile apps to regulate water valves, fans, and other controls remotely, depending on the trends of soil, plant, and weather data visualized [52]. The concept of using mobile technology to provide agricultural help has taken numerous shapes.…”

Section: Mobile Applications For Smart Irrigation Managementmentioning

confidence: 99%

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

et al. 2022

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Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.

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“…Предложена стратегия принятия решений на основе Q-обучения с подкреплением, основанная на прошлом опыте орошения и краткосрочном прогнозе погоды для полива рисового поля, и сопоставлена с обычным планированием орошения. Оценивалась прогнозируемая эффективность орошения для суточного количества осадков в течение 7 дней [28].…”

Section: цифровые технологии искусственный интеллект Digital Technolo...unclassified

Implementation of Artificial Intelligence in Agriculture to Optimize Irrigation

Fedosov

Menshikh

2022

S-h. maš. tehnol.

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Abstract. The relevance of artificial intelligence in agriculture is substantiated for irrigation optimization. (Research purpose) To report on the progress made over the past few years in the application of artificial intelligence to optimize crop irrigation. (Materials and methods) The review focuses on the most salient facts and important scientific information on the application of artificial intelligence in crop production. The review is based on Various databases (Google Scholar, PubMed, Science Direct, SciFinder, Web of Science, RSCI) and online sources (Research Gate, Springer Nature Open Access, Wiley Online Library). It is shown how the integration of machine learning models can provide intelligent irrigation management. The review reports on the research trends and applicability of machine learning methods, as well as the deployment of developed machine learning models for sustainable irrigation management. (Results and discussion) Mobile and web platforms are shown to be able to facilitate intelligent irrigation management. Machine learning proves to be one of the central areas of artificial intelligence helping researchers to work more creatively and efficiently. The review notes the problems of introducing artificial intelligence in crop production and specifies the future research areas in the machine learning implementation and digital farming solutions. (Conclusions) The relevance of the intelligent system in irrigation and water management is proved for sustainable agriculture. It is revealed that, despite the extensive literature available, machine learning modeling for crop irrigation management is still in its infancy. The countries leading in this area are China, the United States and Australia.

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An Ensemble Learning Model for Agricultural Irrigation Prediction

Cited by 26 publications

References 7 publications

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

Implementation of Artificial Intelligence in Agriculture to Optimize Irrigation

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