Precise and Quantitative Chlorosis Severity Assessment Framework (PQCSAF) using evolutionary superpixels.

The increasing need for smart agriculture in the twenty-first century has increased demand for computer-vision-based disease recognition systems encompassing small and large application areas. In recent years, one of the most promising agricultural research areas has been the automated identification of plant diseases using computer vision. Chlorosis is one of the most common diseases of green leaves. It causes the leaves to turn yellow. The severity of chlorosis (chlorophyll decrease) can be perceived by observing the degree of yellowness in the leaves. One of the most challenging tasks in computer vision-based disease diagnosis is severity estimation with precision. This research has developed a novel approach with a high degree of accuracy to detect the disease-affected lesion areas and the degree of severity. The proposed method involves multiple steps, including initial optimization of superpixel algorithm parameters, feature extraction, feature selection, classification, and disease severity estimation. An evolutionary superpixel-based method has been proposed for grouping different colour patches on the leaf. To detect the presence of yellowness, texture features from several categories of superpixels are extracted using color-GLCM techniques. In this work, a multi-swarm Cuckoo search-based feature selection approach has been proposed and utilized to reduce the feature set designed using the color-GLCM measures. Subsequently, the reduced feature set has been employed to classify the superpixels into four distinct categories based on the degree of yellowness. The proposed PQCSAF has been tested with the chlorosis-affected images of Pongamia pinnata leaves. The proposed system has been trained using four classifiers: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and multi-layer perceptron (MLP). For categorization of the superpixels according to the four chlorosis stages, the DT, KNN, SVM, and MLP obtained average classification accuracies of [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Finally, the severity index of chlorosis for the whole leaf is reported based on the weighted scores of the superpixels based on their categories. The proposed method demonstrates applicability for its robustness and detection accuracy, as indicated by comparison studies with existing literature. The proposed method can be used to measure chlorosis severity for different types of plants and various leaf diseases. Due to its adaptive qualities, the proposed model has the potential to be applied to on-field AI edge devices in future.