Hybrid Approach of Neural Network and Genetic Algorithm to Recognize Black Mold Disease in Tomato
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 5
Abstract
Tomato is the eatable and red berry type fruit which belongs to the nightshade family, Solanaceae. Scientific name of tomato is Solanum lycopersicum, which is most popularly and widely grown vegetable around the world [1]. Tomatoes suffer from number of diseases which is caused by fungi, bacteria, viruses etc [2]. Black Mold disease occurs due to the fungus Alternaria Alternata and in High Plains this disease is commonly found. Black Mold affects the tomato fruit which is exposed to free moisture, heavy rain or excess of irrigation. Black mold symptoms appear on ripe fruit, although infection may occur on green fruit also [3]. The proposed work is a hybrid approach of genetic algorithm and neural network to recognize Black Mold disease in tomato. Initially tomato image is acquired with the help of good quality digital camera. Preprocessing will be performed to remove noises, resizing and cropping. Then Region growing segmentation algorithm is applied to segment the images. Color and texture features are extracted from the segmented image. These features are forwarded to the BackPropagation Neural Network (BPN).Genetic algorithm (GA) is applied for optimized feature selection. These optimized features are applied to BPN. Lastly, comparison of both the classifier will be done on the basis of accuracy and then optimized classifier will be obtained for detecting the Black Mold disease in tomato.
Authors and Affiliations
Mamta Yadav, Toran Verma
Keywords
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