A leaf image localization based algorithm for different crops disease classification

• Color transformation based seed points initialization provides precise start for the leaf region extraction. • Energyand level-set based leaf region refinement offers an improvement in accuracy. • It offers high accuracy for the complex leaf region segmentation on multiple crops image datasets. • Combination of conventional algorithms with MLP performs outstandingly for crop disease image classification. Agricultural crop production is a major contributing element to any country’s economy. To maintain the economic growth of any country plants disease detection is a leading factor in agriculture. The contribution of the proposed algorithm is to optimize the extracted information from the available resources for the betterment of the result without any additional complexity. The proposed technique basically localizes the leaf region prior to the image classification into healthy and diseased. The novelty of this work is to fuse the information extracted from the available resources and optimize it to enhance the expected outcome. The leaf colors are analyzed using color transformation for the seed region identification. The mapping of a low-dimensional RGB color image into L*a*b color space provides an expansion of the spectral range. The neighboring pixels-based leaf region growing is applied on the initial seeds. In order to refine the leaf boundary and the disease-affected areas, we employed a random sample consensus (RANSAC) for suitable curve fitting. The feature sets using bag of visual words, Fisher vectors, and handcrafted features are extracted followed by classification using logistic regression, multilayer perceptron model, and support vector machine. The performance of the proposal is analyzed through PlantVillage datasets of apple, bell pepper, cherry, corn, grape, potato, and tomato. The simulation-based analysis of the proposed contextualization-based image categorization process outperforms as compared with the state of arts. The proposed approach provides average accuracy and area under the curve of 0.932 and 0.903, respectively.