Automatic Clustering and Classification of Coffee Leaf Diseases Based on an Extended Kernel Density Estimation Approach

Abstract

The current methods of classifying plan disease images are mainly affected by the training phase and the characteristics of the target dataset. Collecting plant samples during different leaf life cycle infection stages is time-consuming. However, these samples may have multiple symptoms that share the same features but with different densities. The manual labelling of such samples demands exhaustive labour work that may contain errors and corrupt the training phase. Furthermore, the labelling and the annotation consider the dominant disease and neglect the minor disease, leading to misclassification. This paper proposes a fully automated leaf disease diagnosis framework that extracts the region of interest based on a modified colour process, according to which syndrome is self-clustered using an extended Gaussian kernel density estimation and the probability of the nearest shared neighbourhood. Each group of symptoms is presented to the classifier independently. The objective is to cluster symptoms using a nonparametric method, decrease the classification error, and reduce the need for a large-scale dataset to train the classifier. To evaluate the efficiency of the proposed framework, coffee leaf datasets were selected to assess the framework performance due to a wide variety of feature demonstrations at different levels of infections. Several kernels with their appropriate bandwidth selector were compared. The best probabilities were achieved by the proposed extended Gaussian kernel, which connects the neighbouring lesions in one symptom cluster, where there is no need for any influencing set that guides toward the correct cluster. Clusters are presented with an equal priority to a ResNet50 classifier, so misclassification is reduced with an accuracy of up to 98%.

Deep learning implementation of image segmentation in agricultural applications: a comprehensive review

“…Disease symptoms look symmetric at different stages of infection, with the possibility of overlapping symptoms appearing on the same leaf. So, the wide variety of symptom characteristics in qualitative and quantitative terms makes it very challenging to collect disease samples (Hasan et al 2023). Li et al (2022a, b, c) proposed a lightweight network based on copy-paste and semantic segmentation for accurate disease region segmentation and severity assessment.…”

Enhanced Classification of Coffee Leaf Biotic Stress by Synergizing Feature Concatenation and Dimensionality Reduction

“…By combining the strength of different models, the stage wise ensemble was able to achieve improved accuracy and classification performance Some studies focused specifically on targeting class imbalances, such as the work of Hasan et al [14]. In their study, the authors used a kernel density estimation (KDE) approach for automated clustering and classification.…”

“…Approaches including Few Shot Learning, modifications to existing stateof-the-art architectures, ensemble learning and guided learning were used to achieve high accuracy on different datasets. While [14] worked on a deeper analysis of class overlapping and their segregation using a Kernel Density approach prior to using a convolutional network for a guided take on imbalanced classes, others focused entirely on pushing the capabilities of the convolutional networks achieving a maximum accuracies of 99.07 and 99.87% using concatenated and transfer learning models for 5 and 4 class classification. The computational costs and limited control over feature extraction pose significant challenges.While convolutions are indeed a powerful mechanism for feature extraction, hand crafted features provide a complementary interpretation of complex features and textures for better explainability of underlying decision making process if used alongside CNN.…”

“…Feature concatenation approaches were also presented for the pretrained architectures to augment the model's ability to learn on diverse multi-scale features [8]. There were studies that played with the class imbalance conundrum using some traditional or Machine Learning approaches before subjecting the results back to a transfer learning model [14], [16]. In conclusion, many alternate combinations were leveraged to tackle the problems associated with the classification.…”

A Plant Disease Classification Algorithm Based on Attention MobileNet V2

“…Clustering perspective: Models are built by an analysis of intra-class differences. Representative algorithms based on traditional features include: Yu et al [19] constructed a K-means model to analyze intra-class differences and achieve clustering; Padol et al [20] established an SVM to cluster different disease images; Rani et al [21] enhanced clustering accuracy by adding SVM on top of the K-means algorithm; Trivedi et al [22] established a model from the perspective of a color histogram for image analysis; Faithpraise et al [23] established a K-means model for disease classification from a clustering perspective; Tamilselvi et al [24] used unsupervised machine-learning algorithms to cluster based on color features; and Hasan et al [25] proposed an extended kernel-density-estimation approach to analyze disease morphology. On the other hand, representative algorithms based on deep feature extraction mainly include: Yadhav et al [26] obtained clustering features based on the CNN model with optimized activation functions; Bhimavarapu et al [27] fused PSO and CNN algorithms to extract multi-dimensional features; Hatuwal et al [28] experimentally demonstrated the capabilities of random forest, KNN, SVM, and CNN for clustering; Pareek et al [29] established a 1D-CNN model for clustering based on image segmentation; Mukti et al [30] achieved plant-disease detection based on multiple iterations of ResNet; Li et al [31] analyzed plant diseases through the construction of the model ensemble with inception module and cluster algorithm; Türko glu et al [32] used deep networks to extract disease image features and analyze differences between classes; and Ramesh et al [33] constructed a model from the perspective of image and machine learning to achieve disease classification.…”