# FF-PCA-LDA: Intelligent Feature Fusion Based PCA-LDA Classification System for Plant Leaf Diseases
## 2 Related Work  
Several methods based on RGB, multi-spectral, and hyper-spectral imaging technologies are developed for plant leaf disease classification [5; 6; 7]. _[PERSON] and [PERSON]_ proposed an imaging-based leaf disease severity system by utilizing segmentation approach. It composed two stages: (i) segmentation and (ii) classification. The classification efficiency highly depends upon quality segmentation. A slight variation in segmentation may considerably decline the classification performance [8].  
_[PERSON] and [PERSON]_ proposed a plant leaf disease detection system by employing K-means algorithm for segregation of region-of-interest followed by feature extraction and SVM classification. Their approach mainly depends on precise segmentation of affected leaf areas [9]. Similarity, _[PERSON] and [PERSON]_ reported a cotton leaf disease detection technique, which utilizes edge identification for segmentation to identify the affected region. Their approach requires user intervention and may not be useful when applied on massive scale analysis [10].  
[PERSON] et al. [11] proposed image segmentation and performed statistical analysis for plant leaf disease identification. The acquired images are passed through a kernel and then compared with the original images. Statistical analysis of the results defined disease type. Although the process seems useful, however, the optimal selection of kernel size for various diseases may significantly affect the whole disease identification process.  
[PERSON] et al. [12] proposed a hyper-spectral-image-based potato leaf disease detection approach. This technique produced acceptable results; however, the technology for acquiring hyper-spectral imaging might be inaccessible for an ordinary grower.  
Recently, deep-learning-based approaches have gained the attention of researchers to solve real world problems in computer vision, object detection in videos and images, human disease diagnosis, and crop disease detection [13; 14; 15; 16]. It has an advantage of automatic image feature extraction by the stacking of convolution and pooling layers. On the other hand, conventional approaches utilized handcrafted features for classification.  
Several deep learning based approaches reported in the literature for classification of crop disease diagnosis [13]. [PERSON] et al. [17] proposed banana plant disease detection using deep learning and obtained an accuracy of 90.0%. In another study, [PERSON] and [PERSON] [18] proposed deep learning model based on self-attention for crop disease detection. [PERSON] et al. [19] used convolutional neural networks for plant disease classification and achieved F-score value of 97.0%. Several other researchers used deep learning to develop different types of models for crop disease predictions [20; 21; 22; 23].  
In summary, conventional approaches have some associated challenges such as: quality image segmentation, numbers and types of handcrafted features for classification, and user intervention at certain levels. To minimize these limitations, it is highly desired to develop an automatic, efficient, scalable approach for crop disease classification. To this end, we have proposed feature fusion and the most discriminant features-selection-basedPCA-LDA classifier for plant disease diagnosis. This will not only enhance accuracy but also reduce the computational complexity of the classifier.