# FF-PCA-LDA: Intelligent Feature Fusion Based PCA-LDA Classification System for Plant Leaf Diseases
## 5 Discussion  
In smart agriculture, the automatic identification of plant leaf diseases is vital for yield, quality, and management control. It will not only reduce production costs but also reduce the excessive use of agro-chemicals. The advancement of digital technologies and machine learning algorithms may help to improve crop disease diagnosis with high precision. Deep learning is being used to solve real-world problems efficiently in medical imaging, computer vision, and object recognition. It is gaining the attention of researchers to use it for smart agriculture. It reduces the problems of conventional approaches such as user intervention, threshold and kernel size selection, and optimal feature extraction for segmentation and classification. These limitations make systems complex, and even a small variation in image acquisition or in parameter selection may reduce classification performance. To address these challenges, a novel approach is developed by the fusion of handcrafted hybrid and deep features followed by PCA-LDA (FF-PCA-LDA) classification. As a case study, we have considered a potato plant leaf disease identification problem using benchmark dataset. Classification is performed on images acquired by an ordinary camera without performing segmentation and data oversampling steps. Experiments are conducted to validate the efficacy of the proposed approach. In this research, for the performance comparison of the proposed approach, we designed following four types of models:  
* \(LDA_{HH}\) model using all handcrafted hybrid features without PCA;
* \(PCA-LDA_{HH}\) model using only handcrafted hybrid (HH) features;
* \(TL-ResNet50\) deep learning model without PCA-LDA;
* \(PCA-LDA_{DF}\) model using only Deep features (DF);
* \(PCA-LDA_{FF}\) model using only fused features. It has been observed from Table 1 that handcrafted hybrid features based \(LDA_{HH}\) models offer an average accuracy of 92.21%. However, it is possible that some features might be redundant and less contributing in model development. PCA has been employed for dimension reduction, and the most important 100 PCs are selected and fed to LDA for the development of the model. \(PCA-LDA_{HH}\) offers improved average accuracy by 4.32%. This indicates the existence of redundant and less important features, which may not have contributed in classification. The visual representation of features using PC1 and PC2 is shown in Figure 5a. It can be observed that except for the 'Late Blight' class, the other class features have low intraclass similarity. Although the \(PCA-LDA_{HH}\) model has improved, the average performance for potato disease detection. However, if we considered other crops, this case may not be true because every crop's leaves have their own feature representation. The current parameters setting of handcrafted hybrid features for potato crop may not offer such high performance for any other plant disease. Hence, it is highly desired of an automatic feature extraction technique to classify crop leaf images with high precision. To overcome the problems of handcrafted hybrid features mentioned previously, we develop TL-ResNet50 deep learning model for the classification of potato crop leaf diseases. From Tables 1 and 2, it is inferred that TL-ResNet50 model improves accuracy compared to \(LDA_{HH}\) and \(PAC-LDA_{HH}\) models. Furthermore, we extracted deep features by employing the modified ResNet50 deep learning model. It automatically mimicked and extracted the \begin{table}
\begin{tabular}{l l c} \hline \hline
**Techniques** & **Description** & **Accuracy (\%)** \\ \hline [PERSON] et al. [34] & Segmentation-based technique & 95.00 \\ [PERSON] et al. [12] & Hyper-spectral imaging and segmentation & 94.87 \\ [PERSON] et al. [35] & Segmentation-based technique & 94.00 \\
**The proposed approach** & **Feature fusion, PCA and LDA** & **98.20** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of the proposed and existing approaches for Potato leaf disease classification.