## 2 Literature Review
\begin{table}
\begin{tabular}{c c} \hline
**Deep Learning Architecture/Image Database** & **Study Summary** \\ \hline \multirow{6}{*}{Alex Net, Alex Net OWTBn, Google Net, Over feta and VGG} & Convolutional neural network models were developed to perform classifications of plant disease using simple leaf images of healthy and diseased plants, through deep learning methodologies. Training of the models was performed with the use of an open database of 87,848 images, containing 25 different plants in a set of 58 distinct classes of [plant, disease] combinations, including healthy plants. Several model architectures were trained, with the best performance reaching a 99.53\% success rate in identifying the corresponding [plant, disease] combination (or healthy plant) [18]. \\ \hline \multirow{6}{*}{CNN model similar to standard Le Net architecture} & Images of apple leaves, covering various diseases as well as healthy samples, from the PlantVillage dataset were used to validate results. Image filtering, image compression, and image generation techniques were used to gain a large training set of images and tune the system perfectly. The trained model achieved high accuracy scores in all the classes, with a net accuracy of 98.54\% on the entire dataset, sampled and generated from 2561 labelled images [19]. \\ \hline \multirow{6}{*}{Alex Net and VGG-16} & In this study tomato-leaf images (6 diseases and a healthy class) obtained from the PlantVillage dataset were provided as inputs to two deep learning-based architectures, namely, Alex Net and VGG16 net. The accuracy of classification obtained using Alex Net and VGG16 net were 97.49\% and 97.23\%, respectively, for 13262 images [20]. \\ \hline \multirow{6}{*}{VGG 16, Inception V4, Res Net with 50, 101 and 152 layers and Dense Nets with 121 layers} & An empirical comparison of the Deep Learning architecture was done. The architectures evaluated included VGG 16, Inception V4, Res Net with 50, 101 and 152 layers and Dense Nets with 121 layers. The data used for the experiment were 38 different classes, including diseased and healthy leaf-images of 14 plants from PlantVillage. Fast and accurate models for plant disease identification were desired, so that accurate measures could be applied early. In the experiment, Dense Nets had a tendency to consistently improve in accuracy with the growing number of epochs, with no signs of overfitting and performance deterioration. Moreover, Dense Nets required a considerably less number of parameters and reasonable computing time to achieve state-of- the-art performances. It achieved a testing accuracy score of 99.75\% to beat the rest of the architectures [21]. \\ \hline \multirow{6}{*}{Google Net} & The work explored the use of individual lesions and spots for the task, rather than considering the entire leaf. Each region had its own characteristics, therefore the variability of the data was increased without the need for additional images. This also allowed the identification of multiple diseases affecting the same leaf. On the other hand, suitable symptom segmentation still needed to be performed manually, preventing full automation. The accuracies obtained using this approach were, on average, 12\% higher than those achieved using the original images. Additionally, no crop had accuracies below 75\%, even when as many as 10 diseases were considered. Although the database did not cover the entire range of practical possibilities, these results indicated that, as long as enough data were available, the deep learning technique is effective for plant disease detection and recognition [22]. \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._Fuzzy logic in AI provides valuable flexibility for reasoning. It is basically a method of reasoning that resembles human reasoning. This approach is similar to how humans perform decision-making, and it involves all intermediate possibilities between \"YES\" and \"NO\". Fuzzy logic reasoning provides acceptable reasoning and helps in dealing with the uncertainty in engineering.