Identification of vegetable diseases using neural network

Jiacai Zhang; Jianjun Tang; Yao Li

doi:10.1117/12.706710

27 February 2007 Identification of vegetable diseases using neural network

Jiacai Zhang, Jianjun Tang, Yao Li

Proceedings Volume 6497, Image Processing: Algorithms and Systems V; 649717 (2007) https://doi.org/10.1117/12.706710
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

Abstract

Vegetables are widely planted all over China, but they often suffer from the some diseases. A method of major technical and economical importance is introduced in this paper, which explores the feasibility of implementing fast and reliable automatic identification of vegetable diseases and their infection grades from color and morphological features of leaves. Firstly, leaves are plucked from clustered plant and pictures of the leaves are taken with a CCD digital color camera. Secondly, color and morphological characteristics are obtained by standard image processing techniques, for examples, Otsu thresholding method segments the region of interest, image opening following closing algorithm removes noise, Principal Components Analysis reduces the dimension of the original features. Then, a recently proposed boosting algorithm AdaBoost. M2 is applied to RBF networks for diseases classification based on the above features, where the kernel function of RBF networks is Gaussian form with argument taking Euclidean distance of the input vector from a center. Our experiment performs on the database collected by Chinese Academy of Agricultural Sciences, and result shows that Boosting RBF Networks classifies the 230 cucumber leaves into 2 different diseases (downy-mildew and angular-leaf-spot), and identifies the infection grades of each disease according to the infection degrees.

Citation Download Citation

Jiacai Zhang, Jianjun Tang, and Yao Li "Identification of vegetable diseases using neural network", Proc. SPIE 6497, Image Processing: Algorithms and Systems V, 649717 (27 February 2007); https://doi.org/10.1117/12.706710

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KEYWORDS

Image segmentation

Neural networks

Image processing

Feature extraction

Agriculture

Databases

Digital cameras

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