# FF-PCA-LDA: Intelligent Feature Fusion Based PCA-LDA Classification System for Plant Leaf Diseases
## 5 Discussion
most dominant image features at the 'avg_pool' layer of the network, as shown in Figure 4. \(PCA-LDA_{DF}\) classification is performed on deep features, and an average accuracy of 97.96% is achieved for potato crop leaf disease identification, as shown in Table 2. The high average accuracy indicates that TL-ResNet50 has successfully learned the image features followed by efficient LDA classification. It improved performance by 6% compared to the \(LDA_{HH}\) model, as shown in Table 1. Visually, Figure 5b shows the low interclass scatter of the deep features using the first two principal components, i.e., PC1 and PC2. In this research, the fusion of handcrafted hybrid and deep features is performed, followed by a dimension reduction using PCA and plant disease classification with LDA. A fused feature vector of dimension 2485 for every input image is obtained and fed to PCA for dimension reduction. Visual representation of reduced features with PC1 and PC2 is shown in Figure 5c. It is observed that PCA has efficiently mapped the features with low intraclass and high interclass separation. An average accuracy of 98.20% is achieved by the \(PCA-LDA_{FF}\) model for the identification of potato leaf diseases, as shown in Table 2. Sufficient improvement is observed over handcrafted hybrid and deep-features-based classification models. The average performance offered by the developed approach in terms of sensitivity, specificity, and F-score is 98.79%, 92.67%, and 95.33%, respectively, and shown in Figure 6. It is observed that \(PCA-LDA_{FF}\) successfully identified all samples of 'Healthy' and 'Early Blight' classes with 100% sensitivity values, with 96.0% for 'Late Blight' class. The high values of these performance parameters indicate the effectiveness of the FF-PCA-LDA approach. Overall, it is deduced that the feature-fusion-based \(PCA-LDA_{FF}\) model enhanced the classification accuracy by 1.45% over the TL-ResNet50 model for potato crop leaf diseases. Similarly, the proposed \(PCA-LDA_{FF}\) model also performed better over the handcrafted-hybrid-based \(PCA-LDA_{HH}\) and \(LDA_{HH}\) models and the deep-features-based \(PCA-LDA_{DF}\) models. This fact can be observed from Tables 1 and 2. One of the objectives of this study is to improve the performance of the proposed FF-PCA-LDA crop leaf disease diagnosis system. It is achieved through the fusion of deep and handcrafted hybrid features followed by PCA-LDA classification. Similarly, class-wise performance in terms of ROC curves of potato crop leaf diseases are shown in Figure 7. ROC curves for all classes are very close to the vertical axis, which depicts the efficacy of the developed model. The average value of the AUC is obtained as 98.46% by the FF-PCA-LDA approach. The high value of the AUC shows the usefulness of the approach; hence, it can be used for plant leaf disease classification models. For comparison purposes, we have developed various classifiers such as MLBPNN, KNN, and PNN using a five-fold cross validation technique on the same dataset. The performance of the proposed approach with different classifiers is presented in Table 3. The analysis shows that the proposed approach offers superior performance compared to other classifiers. Moreover, the developed model is compared with some of the existing potato leaf disease detection techniques, as shown in Table 4. The proposed FF-PCA-LDA approach offers high performance as compared to other state-of-the-art techniques. The high performance is evident because the fused features are used to design the \(PCA-LDA_{FF}\) classification model. In this study, we have developed an efficient approach using feature fusion followed by \(PCA-LDA_{FF}\) classification and achieved 98.20% accuracy for Potato leaf disease detection. It does not required any specialized image acquisition equipment. RGB images are utilized that are acquired by a smartphones or an ordinary camera to monitor crop health.