Discrimination of Rice Diseases Using Image Features for Web Based Diagnosis

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  • Web診断のための画像特徴を利用したイネ病気の判別・分類に関する研究
  • Web診断のための画像特徴を利用したイネ病気の判別・分類に関する研究--色及び形状特徴を用いたパターン判別分析法の精度
  • Web シンダン ノ タメ ノ ガゾウ トクチョウ オ リヨウ シタ イネ ビョウキ ノ ハンベツ ブンルイ ニ カンスル ケンキュウ イロ オヨビ ケイジョウ トクチョウ オ モチイタ パターン ハンベツ ブンセキホウ ノ セイド
  • ——Accuracy of Pattern Discrimination Analysis Methods Using Shape and Color Features——
  • ——色及び形状特徴を用いたパターン判別分析法の精度——

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Abstract

The overall objective of this research is to develop a supporting system for web based diagnosis of rice diseases using the automatic analysis of image features. In order to establish an online discrimination method, the relationship between the discrimination conditions and the accuracy of 6 types of pattern discrimination analysis methods (Support Vector Machine (SVM), Ensemble Learning, Tree-Based Model, Neural Network, Linear Discriminant Analysis, and Quadratic Discriminant Analysis) was examined with 4 classes of 3 rice diseases (Leaf blast, Sheath blight, and Brown spot) based on the shape and color features of the disease lesions. In the case when 7 variables were used, the accuracy results of each analysis method in the discrimination of 4 classes at once were 86%, 81%, 76%, 80%, 78% and 81%, respectively. Furthermore, the accuracy of SVM was 94% in pair-wise discrimination. Hence, SVM was considered to have good potential for discriminating rice diseases.

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