Impact of Color Spaces and Feature Sets in Automated Plant Diseases Classifier: A Comprehensive Review Based on Rice Pla
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ORIGINAL PAPER
Impact of Color Spaces and Feature Sets in Automated Plant Diseases Classifier: A Comprehensive Review Based on Rice Plant Images Toran Verma1 · Sipi Dubey1 Received: 21 February 2019 / Accepted: 30 September 2019 © CIMNE, Barcelona, Spain 2019
Abstract Currently, researchers are developing numerous plant diseases recognition model using image processing and soft computing. The models are mainly based on the extraction of discolored features and applying in various soft computing approaches to automate plant diseases recognition process. The extracted features are statistical, frequency, spatial-frequency or hybrid features of captured images accessed in device-dependent or device-independent color spaces. The performance of diseases recognition system is significantly dependent upon the selection of color spaces and extracted features. This paper presents a comprehensive review of the impact of color spaces and feature sets on machine learning and rule base automated plant diseases classifier. The review performed with six categories of rice plant images with two machine learning and two rule base classifiers. Initially, a thorough literature review performed on the previous investigation based on color spaces and used feature sets for designing diseases recognition model. Then common conditions created to extract feature sets in different color spaces, and applied machine learning and rule base classifier to analyze the impact of color spaces with feature sets. The review presents a detailed discussion on the correlation between color spaces, feature sets, and performance of diseases recognition system. The review results reveal the most relevant features on specific color space for machine learning and rule base classifier. It also deduces that the performance of plant diseases classifier highly dependent upon used color space and extracted features.
1 Introduction Automated plant disease recognition is an essential component of Integrated Plant Disease Management (IPDM). The automation process of plant disease identification is a combination of man, vision system and intelligent machine. It includes creating a database about diseases symptoms and automatic identification of the diseases by image processing and machine vision for identification of plant diseases. Models developed using image processing and soft computing by researchers to automate the diseases recognition process.
feature extraction. Digitally acquired images or spectral images preprocessed directly but the captured video converted in the frame and preprocessed. The preprocessed images directly applied for plant disease recognition using image retrieval techniques or deep learning method. However, many researchers applied segmentation before feature extraction. The extracted features are statistical, frequency, spatial-frequency or hybrid features. These features applied in various soft computing approaches to recognize diseases. Finally, the performance of the model evaluated using many parameters.
1.1 Background
1.2 Moti
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