A Crop Growth Prediction Model Using Energy Data Based on Machine Learning in Smart Farms

Abstract

In the recent past, the agricultural industry has rapidly digitalized in the form of smart farms through the broad usage of data analysis and artificial intelligence. Commonly, high operating costs in a smart farm are primarily due to inefficient energy usage. Therefore, accurate estimation of agricultural energy usage and environmental factors is considered as one of the significant tasks for crop growth control. The growth sequences of crops in agricultural environments like smart farms are related to agricultural energy usage and consumption. This study aims to develop and validate an algorithm that can interpret the crop growth rate response to environmental and solar energy factors based on machine learning, and to evaluate the algorithm's accuracy compared to the base model. The proposed model was determined through a comparative experiment of three representative machine learning techniques, which are random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM), considering the energy usage for environmental control is highly associated with the paprika crop growth. Through the experiment performance with real data gathered from a paprika smart farm in South Korea, the multi-level RF can effectively predict paprika growth with an accuracy of 0.88, considering data analysis of factors that use solar energy. As a result of the experiment with the suggested model, the growth factors such as leaf length, leaf width, and environmental factors were found. Furthermore, the proposed algorithm can contribute to the development of applications through analysis of the crop growth big data for various plants in agricultural environments such as a smart farm.

Plant phenotyping relevance

機械学習によりパプリカの成長（葉長・葉幅）を推定するアルゴリズムを開発・検証しており、植物形質の抽出が中心的な方法論的貢献である。

Code and data availability

The article describes paprika greenhouse sensor/growth data and RF/SVM/GBM analyses, but contains no public data deposit, repository, or code availability statement. The only URLs present are author ORCIDs and the CC-BY license link, none of which are paper-specific phenotyping assets. The paper is also retracted, and

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