An efficient IoT based smart farming system using machine learning algorithms

• Published in: Multimedia tools and applications Vol. 80; no. 1; pp. 773 - 797
• Main Authors: Rezk, Nermeen Gamal, Hemdan, Ezz El-Din, Attia, Abdel-Fattah, El-Sayed, Ayman, El-Rashidy, Mohamed A.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2021; Springer Nature B.V
• Subjects: Agriculture; Algorithms; Classification; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Datasets; Drought; Farming; Machine learning; Multimedia Information Systems; Productivity; Soybeans; Special Purpose and Application-Based Systems; Sugarcane; Internet of things; Feature selection; And; Machine learning; Prediction; Smart farming; Drought; Crop productivity
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-020-09740-6

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.