Plant Leaf Disease Recognition Based On Image Analysis

The image analysis-based research on plant leaf disease diagnosis recognitiontechnology has a great significance to prevente the occurrence of crop disasters effectively,reduce the impact of the crops diseased, increase crop yield and reduce pesticides onagricultural products and environmental pollution. On the basis of adequate summary aboutthe advanced achievements at home and aboard, in this paper systematical research has beenmade on the key technology of image segmentation, feature extraction and disease diagnosisrecognition, which relyed firmly on the goal of improving recognition accuracy and efficiencyof crop diseases and its cases study of apple, cucumber and capsicum disease. The chief workand major innovations are as follows:(1) In view of the characteristics of noise and blurred edges for plant lesion colorimages, a novel C-V model named WCCV based on level set and weighted color informationwas proposed in this paper and applied to plant lesion image segmentation.The WCCVsegmentation model is suited to different disease identification and can identify lesion diseaseautomatically. Experimental results show that the proposed model has better property than thetraditional C-V model, and have many advantages such as anti-noise and scalability propertieson3R-B image model for capsicum water shortages disease and3R-G image model for applerust disease.(2) According to the flaw of WCCV model, an improved C-V model was proposed inthis paper and applied to plant leaf lesion image segmentation. At first, a point in lesion areais selected, and the mean value calculated from its3×3territory is used to compute thesimilarity between this point and other points in the image, and the foreground andbackground are determined. Then the ration of foreground and background mean pixels fromR,G, B channels is used as weighted value of these three channels, respectively. Finally, thelevel set function is solved iteratively to gain the segmentation contour, while let the distancevalue less than the average distance value of external energy region be zero in the signeddistance function.Experimental results show that the average segmentation accuracy of theproposed method is0.42%and43.55%higher than that of WCCV model and the traditionalC-V model. For large pixel pictures, the average running time of the proposed method is lessthan1/1000of WCCV model and the traditional C-V model. It reduces the running time andimproves the efficiency of algorithm execution greatly.(3) Aiming at the shortcoming of the long running time and not high classification rate of color moments, the weighted feature extraction method based on color moments andwavelet decomposition was proposed. At first, the original image is transformed into H, S andV-channel images to get the wavelet decomposition of each channel subgraphs. Secondly, thedecomposition subgraphs of the integration color and wavelet features are gained and thefirst-order, second-order and third-order moments features of the decomposition subgraphsare calculated. And the energy coefficients are as the weighted features value of the colormoments of the subgraphs and finally the feature vector is gained. The experimental resultsdemonstrate that when the polynomial kernel function-based SVM(support vector machine)and the bior2.4-based wavelet are used, the proposed method recognition correct ratios are88.57%,88.46%, and92.31%for apple mosaic virus, apple rust and apple alternaria leaf spot,respectively, and the average recognition ratio is89.78%. The proposed approachsignificantly improves the recognition correct ratio compared to the25.67%and80.14%recognition correct ratios for color moments-based and wavelet-based feature extractionmethods, respectively.(4) A YUV and wavelet packet-based multi-channel feature extraction algorithm wasproposed. Due to the drawback of the wavelet decomposition-used the feature extractionmethod is normally only using the low-frequency sub-band information of the image, so theuse of wavelet packet decomposition for all frequency channels is a comprehensive analysisof images texture features. Firstly, the input image is converted into the Y, U, V channelssubgraph, respectively. Then the wavelet packet energy features of the Y, U andV-component images are calculated. Finally the wavelet packet energy features among thesub-quantum images are obtained as the disease image feature vector. The experimentalresults demonstrate that when using the polynomial kernel function-based SVM and the haar-based wavelet packet, the average recognition correct ratio is89.10%.Compared with thefeature extraction method based on wavelet transforming, the proposed approach boosts12.16percent recognition correct ratio.(5) The genetic algorithm (GA) is used to select the parameters of the SVM methodautomatically and the orthogonal method is utilized to determine the best GA parameters.Firstly, the color moments and wavelet-based features of apple disease leaf images areextracted as feature vectors. Then the proposed GA-SVM method is used to classify appleleaf disease images.The orthogonal experimental results demonstrate that the proposedGA-SVM model recognition correct ratios are94.47%,91.44%, and91.26%for apple mosaicvirus, apple rust and apple alternaria leaf spot, respectively. And the average recognitioncorrect ratio is92.39%.Compared with the recognition method based on SVM, the proposedapproach shows5.21%higher recognition correct ratio than that of SVM.(6) Considering the difficulty of parameter determination in the original SVM and thecomplexity of genetic algorithm, the particle swarm optimization algorithm (PSO) is used toselect the parameters of the SVM automatically and obtain an optimized function. The PSO algorithm has no crossover and mutation operation, less parameters and easier to use.And itsprinciple is more simple than that of GA. Firstly, the color moments and wavelet-basedfeatures of apple disease leaf images are extracted as feature vectors. Then the proposedPSO-SVM model is used to classify apple leaf disease images. The orthogonal experimentalresults show that the recognition correct ratios are90.24%,87.26%, and85.23%for applemosaic virus, apple rust and apple alternaria leaf spot, respectively. Compared with therecognition method of GA-based, the proposed approach costs10.86%less processing timethan that of GA-SVM.