Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed
| Autor: | Jolanta Wawrzyniak, Marzena Gawrysiak-Witulska, Krzysztof Przybył, Krzysztof Koszela, Franciszek Adamski |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
mould
Letter Rapeseed rapeseed storage Computer science lcsh:Chemical technology Machine learning computer.software_genre Biochemistry Convolutional neural network Analytical Chemistry Image (mathematics) 0404 agricultural biotechnology image analysis convolutional neural networks Image Processing Computer-Assisted lcsh:TP1-1185 Radial basis function Electrical and Electronic Engineering Instrumentation business.industry Deep learning Brassica napus Fungi Process (computing) 04 agricultural and veterinary sciences Perceptron 040401 food science Atomic and Molecular Physics and Optics machine learning Test set 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Neural Networks Computer Artificial intelligence business computer |
| Zdroj: | Sensors, Vol 20, Iss 7305, p 7305 (2020) Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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