A Review on the efficiency and accuracy of localization of moisture distributions sensing in agricultural silos | Zendy

Abd Alazeez Almaleeh | Zendy; Ammar Zakaria | Zendy; Syed Muhammad Mamduh Syed Zakaria | Zendy; Latifah Munirah Kamarudin | Zendy; Mohd Hafiz Fazalul Rahiman | Zendy; Abdul Syafiq Abdull Sukor | Zendy; Yassir Abdul Rahim | Zendy; Abdul Hamid Adom | Zendy

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A Review on the efficiency and accuracy of localization of moisture distributions sensing in agricultural silos

Author(s) -

Abd Alazeez Almaleeh,

Ammar Zakaria,

Syed Muhammad Mamduh Syed Zakaria,

Latifah Munirah Kamarudin,

Mohd Hafiz Fazalul Rahiman,

Abdul Syafiq Abdull Sukor,

Yassir Abdul Rahim,

Abdul Hamid Adom

Publication year - 2019

Publication title -

iop conference series materials science and engineering

Language(s) - English

Resource type - Journals

eISSN - 1757-899X

pISSN - 1757-8981

DOI - 10.1088/1757-899x/705/1/012054

Subject(s) - information silo , silo , generalization , process (computing) , moisture , computer science , artificial neural network , water content , computational intelligence , environmental science , data mining , machine learning , artificial intelligence , agricultural engineering , mathematics , engineering , meteorology , geography , geotechnical engineering , mechanical engineering , mathematical analysis , operating system

The moisture distribution in the silos depends upon various seeds parameters such as type and size of seeds, amount of storage, external weather, and storage period as well as structural and environmental factors. It is very difficult to predict moisture distribution in silos effectively while taking all the above aspects into consideration. This study aims to investigate the efficiency and accuracy of localization of moisture distributions sensing in agricultural silo. The work is mainly focussed on three major elements: Radio Frequency (RF), tomographic imaging and classification process using machine learning. In particular, RF-based signal and volume tomographic images are used to predict the moisture distribution. Furthermore, computational intelligence techniques such as artificial neural network (ANN) is applied to develop models based on previous data. The generalization of these models towards new set of data is discussed in making sure a successful application of a model. A detailed study of the relative performance of computational intelligence techniques has been carried out based on different statistical performance criteria.

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