Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed.
Sensors (Basel)
; 20(24)2020 Dec 19.
Article
en En
| MEDLINE
| ID: mdl-33352649
This paper endeavors to evaluate rapeseed samples obtained in the process of storage experiments with different humidity (12% and 16% seed moisture content) and temperature conditions (25 and 30 °C). The samples were characterized by different levels of contamination with filamentous fungi. In order to acquire graphic data, the analysis of the morphological structure of rapeseeds was carried out with the use of microscopy. The acquired database was prepared in order to build up training, validation, and test sets. The process of generating a neural model was based on Convolutional Neural Networks (CNN), Multi-Layer Perceptron Networks (MLPN), and Radial Basis Function Networks (RBFN). The classifiers that were compared were devised on the basis of the environments Tensorflow (deep learning) and Statistica (machine learning). As a result, it was possible to achieve the lowest classification error of 14% for the test set, 18% classification error for MLPN, and 21% classification error for RBFN, in the process of recognizing mold in rapeseed with the use of CNN.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Brassica napus
/
Hongos
Idioma:
En
Revista:
Sensors (Basel)
Año:
2020
Tipo del documento:
Article
País de afiliación:
Polonia
Pais de publicación:
Suiza