An efficient IoT based smart farming system using machine learning algorithms
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An efficient IoT based smart farming system using machine learning algorithms Nermeen Gamal Rezk 1 & Ezz El-Din Hemdan 2 & Abdel-Fattah Attia 1 & Ayman El-Sayed 2 & Mohamed A. El-Rashidy 2 Received: 12 May 2020 / Revised: 7 August 2020 / Accepted: 26 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
This paper suggests an IoT based smart farming system along with an efficient prediction method called WPART based on machine learning techniques to predict crop productivity and drought for proficient decision support making in IoT based smart farming systems. The crop productivity and drought predictions is very important to the farmers and agriculture’s executives, which greatly help agriculture-affected countries around the world. Drought prediction plays a significant role in drought early warning to mitigate its impacts on crop productivity, drought prediction research aims to enhance our understanding of the physical mechanism of drought and improve predictability skill by taking full advantage of sources of predictability. In this work, an intelligent method based on the blend of a wrapper feature selection approach, and PART classification technique is proposed for crop productivity and drought predicting. Five datasets are used for estimating the proposed method. The results indicated that the projected method is robust, accurate, and precise to classify and predict crop productivity and drought in comparison with the existing techniques. From the results, the proposed method proved to be most accurate in providing drought prediction as well as the productivity of crops like Bajra, Soybean, Jowar, and Sugarcane. The WPART method attains the maximum accuracy compared to the existing supreme standard algorithms, it is obtained up to 92.51%, 96.77%, 98.04%, 96.12%, and 98.15% for the five datasets for drought classification, and crop productivity respectively. Likewise, the proposed method outperforms existing algorithms with precision, sensitivity, and F Score metrics. Keywords Machine learning . Internet of things . Smart farming . Prediction . Drought . Crop productivity . And . Feature selection
* Ezz El-Din Hemdan [email protected] Extended author information available on the last page of the article
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1 Introduction Recently, Agriculture is considered among the key strengths of the global and country’s economy [17]. The practice of farming is one of the main occupations in the world and the product key the variety of crops. In recent times, agriculture is facing problems that may threaten its future, such as drought, crop quality, and productivity problems, Yield prediction problems [24]. The world’s population is growing by about three people per second, equivalent to 250,000 people per day, and by 2025 the world’s population will reach 8 billion, and the planet’s population is expected to reach about 9.6 billion in 2050, according to figures from the Food and Agriculture Organization (FAO) of the United Nations to keep pace with
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