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Wavelets, Principal Component Analysis (PCA), Statistical Features, Powdery Mildew, Anthracnose, Neural Network, Mean Square Error (MSE)
Lack of apparent shape and texture features in disease recognition (Powdery Mildew and Anthracnose) of crop is a key challenge of Agriculture domain in the last few decades. The various soft computing techniques exists in computer vision system still there is need of most efficient methods to meet accuracy. In this work An enhanced Wavelet-PCA based Statistical Feature Extraction technique along with Modified Rotation Kernel Transformation (MRKT) based directional features is proposed in order to address the issues arising in different methodologies for plant disease recognition. This enhanced scheme extracts twenty wavelet features in addition to twelve direction features for different plant parts mango flower, fruit and leaf. This research work is an extended part presents in reference 1 by the authors. The feature set of total 32 features is used to train with Artificial Neural Network to diagnose both Powdery Mildew and Anthracnose disease which occur in the form of Fungus and black spots respectively on different parts of mango plant. The results obtained are found with accuracy of 98.50%, 98.75%, and 98.70% respectively for flower, fruit and leaf
S. B. Ullagaddi, S.Viswanadha Raju,"An Enhanced Feature Extraction Technique for Diagnosis of Pathological Problems in Mango Crop", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.9, pp.28-39, 2017. DOI: 10.5815/ijigsp.2017.09.04
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