Abstract
Automated agriculture processing system are the need of today as food manufacturing industries are suffering great loss on part of defective vegetables and fruits. Many researches are working to develop an automated system to segregate defective or low quality agricultural products from non-defective and good products. In this paper we have proposed a Neural Network based model which converges on the advantages of Artificial Neural Network and the Convolutional Neural Network. The performance of the proposed model is compared against the traditional ANN and CNN classifiers. As an input to the system, thermal images of Potato are used to generate a dataset. The thermal images are captured using TiS45 Thermal Imager by FLUKE Corporation, USA. The model was simulated using Python. The Thermal data set generated using the Pulse Phase Thermography consisted of 1000 images captured from 200 potato. 50% images were of defective potato and non-defective potatoes respectively. Classification accuracy achieved by implementing the proposed model is 98.7%
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