Abstract
The economy is exceptionally reliant on agricultural productivity. Therefore, in domain of agriculture, plant infection discovery is a vital job because it gives promising advance towards the development of agricultural production. In this work, a framework for potato diseases classification based on feed foreword neural network is proposed. The objective of this work is presenting a system that can detect and classify four kinds of potato tubers diseases; black dot, common scab, potato virus Y and early blight based on their images. The presented PDCNN framework comprises three levels: the pre-processing is first level, which is based on K-means clustering algorithm to detect the infected area from potato image. The second level is features extraction which extracts features from the infected area based on hybrid features: grey level run length matrix and 1st order histogram based features. The attributes that extracted from second level are utilized in third level using FFNN to perform the classification process. The proposed framework is applied to database with different backgrounds, totally 120 color potato images, (80) samples used in training the network and the rest samples (40) used for testing. The proposed PDCNN framework is very effective in classifying four types of potato tubers diseases with 91.3% of efficiency.
Highlights
High quality of agricultural crop is one of main factors in food, exportation, health and food industry
Hu Yh et al[10] developed least squares-support vector machine(LS-SVM) models that used to detect late blight disease, 60 potato leaves were obtained, preprocessing methods such as smoothing, normalization, derivative and baseline were used, region of interest (ROI) was extracted, spectroscopic transformation was performed, the results showed that their approach detected late blight disease effectively
120 colour potato images are used as image database with different background colour, potato images were captured using Sony Cyber-shot DSCRX100 digital camera; The objective of presented PDCNN framework is to detect and classify four kinds of potato tubers diseases, the framework includes three levels
Summary
High quality of agricultural crop is one of main factors in food, exportation, health and food industry. Potato is a substantial vegetable crop, it is an integral part of our food after rice, wheat, and maize. The nutritional value of traditional foods and the utilization of potato are expected to be improved by substituting wheat, rice or maize in traditional staple foods partly by potato[1]. All potato species are clones disseminated vegetative by tubers which are unprotected to a vast range of pathogenic organisms, which transmit from plant to another plant. Damages can occur when plants are growing, at hoisting and when tubers are stored. Some diseases do not devastate potato tubers, but they cause surface blotches which reduce sellable value[2]. Most of plant diseases result from complicated fundamental interactions among a pathogen, a vulnerable host plants and plants environment[3]. Diseases of plants can be classified into: bacterial diseases, viral diseases and fungal diseases[4]
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