# Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
## 5 Discussion and Conclusions  
In the collected literature, severity analysis has been largely based on qualitative assessment, with authors switching between nominal and ordinal scales of measurement, i.e., low-severity, medium-severity and high-severity. There is no quantitative framework for assigning a percentage to the problem of maize leaf disease quantification. In this paper, we present an important step towards simultaneous crop disease classification and quantification. We analyzed maize disease data using a variety of deep convolutional neural network models together with deep CNN feature visualization. The ultimate goal is to limit the number of data features and image processing stages while obtaining a discrete measure of disease severity. To this end, we employed a combinations of techniques from leaf image augmentation, transfer learning and CAM visualization (extracting the ROI) to applying an adaptive thresholding method for estimating the leaf area affected by the lesions. In comparison to other methods on the same classification problem, the proposed method obtained a competitive level of accuracy for classifying maize leaf diseases. The key motivation for our approach is that it can work in low-resource environments, e.g., when data are scarce or on low-computational-power systems, but most importantly, it can quantify crop diseases without the need of expert knowledge. Additionally, our study used the most-common data augmentation techniques and obtained good accuracies of over 99% for most CNN models. Lenet was the worst performing model, with an accuracy of 94%. Our proposed shallow CNN model that uses only three convolutional layers managed to obtain an accuracy of 95% without any regularization. This demonstrates that with a larger dataset, shallow CNNs can produce impressive accuracies comparable with some deeper CNNs. All the benchmark CNN models used \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Method** & **Class Label** & **Precision** & **Recall** & **F1-Score** &
\begin{tabular}{l} **Overall** \\ **Accuracy** \\ \end{tabular} \\ \hline \multirow{3}{*}{DenseNet 201} & Blight & 0.99 & 0.99 & 0.99 & \multirow{3}{*}{99\%} \\  & Common Rust & 1.00 & 1.00 & 1.00 & \\  & Gray Leaf Spot & 0.99 & 0.99 & 0.99 & \\  & Healthy & 1.00 & 1.00 & 1.00 & \\ \hline \multirow{3}{*}{MobileNet} & Blight & 0.98 & 0.98 & 0.98 & \multirow{3}{*}{99\%} \\  & Common Rust & 0.99 & 1.00 & 0.99 & \\  & Gray Leaf Spot & 0.98 & 0.98 & 0.98 & \\  & Healthy & 1.00 & 1.00 & 1.00 & \\ \hline \multirow{3}{*}{VGG16} & Blight & 0.99 & 0.97 & 0.98 & \multirow{3}{*}{99\%} \\  & Common Rust & 1.00 & 1.00 & 1.00 & \\  & Gray Leaf Spot & 0.97 & 0.99 & 0.98 & \\  & Healthy & 1.00 & 1.00 & 1.00 & \\ \hline \multirow{3}{*}{EfficientNetB0} & Blight & 0.98 & 0.98 & 0.98 & \multirow{3}{*}{99\%} \\  & Common Rust & 0.98 & 1.00 & 0.99 & \\  & Gray Leaf Spot & 0.98 & 0.97 & 0.98 & \\  & Healthy & 1.00 & 1.00 & 1000 & \\ \hline \multirow{3}{*}{LeNet} & Blight & 0.94 & 0.87 & 0.90 & \multirow{3}{*}{94\%} \\  & Common Rust & 0.90 & 0.98 & 0.93 & \\  & Gray Leaf Spot & 0.93 & 0.90 & 0.92 & \\  & Healthy & 0.98 & 1.00 & 0.99 & \\ \hline \multirow{3}{*}{Custom CNN (One dropout layer)} & Blight & 0.92 & 0.92 & 0.92 & \multirow{3}{*}{95\%} \\  & Common Rust & 0.96 & 0.96 & 0.96 & \\  & Gray Leaf Spot & 0.93 & 0.90 & 0.92 & \\  & Healthy & 0.97 & 1.00 & 0.99 & \\ \hline \multirow{3}{*}{Custom CNN (Batch Normalization)} & Blight & 0.93 & 0.95 & 0.94 & \multirow{3}{*}{96\%} \\  & Common Rust & 0.96 & 0.98 & 0.97 & \\ \cline{1-1}  & Gray Leaf Spot & 0.97 & 0.92 & 0.94 & \\ \cline{1-1}  & Healthy & 0.98 & 1.00 & 0.99 & \\ \hline \multirow{3}{*}{Custom CNN (No Regularization)} & Blight & 0.92 & 0.91 & 0.92 & \multirow{3}{*}{95\%} \\  & Common Rust & 0.94 & 0.97 & 0.96 & \\ \cline{1-1}  & Gray Leaf Spot & 0.93 & 0.92 & 0.92 & \\ \cline{1-1}  & Healthy & 0.99 & 1.00 & 1.00 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._in this work rely on multiple deep convolutional layers to obtain high performance on our dataset.