# Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
We compare the performance of the proposed method with state-of-the art image segmentation techniques for leaf disease estimation. The proposed system can assign a severity estimate as a ratio without prior information from experts, and identify the following maize leaf conditions with high accuracy: Northern Corn Leaf Blight, Gray Leaf Spot, common corn rust and healthy leaves. The main goals of the suggested method are: (1) Use transfer learning to reuse CNN models to assign the correct maize leaf disease class to the given image. (2) Use CNN class activation heatmaps to derive a percentage severity estimate based on the suggested adaptive thresholding technique. Our hypothesis is that the extracted ROI from the class activation heatmap correlates with the area covered by the disease in the original leaf image. Therefore, the area of the ROI as a percentage of the total leaf area gives us an estimate of the disease severity. We summarize the main contributions of this work as follows:  
1. We propose a novel method for simultaneously classifying and quantifying maize leaf diseases without any prior knowledge of the lesions. 2. To improve the classification accuracy of maize leaf disease identification, this work investigates several models: custom CNN, Inception V3 [15], DenseNet 121 [16], DenseNet 201 [16], MobileNetV2 [17], VGG16 [18], EffcientNetB0 [19], ResNet50 [20] and LeNet [21]. The performance of the custom CNN is further examined by comparing the effect of regularization techniques such as batch normalization and dropout operations. Finally, we compare the experimental results of the custom CNN with those of the transfer learning techniques to determine the effect of CNN depth and number parameters on the performance of the model. 3. We propose a unique and computationally efficient adaptive thresholding method for quantifying the diseased regions of leaf images in an unsupervised manner. The advantage of this method is that it uses spatial relationships between the pixels to predict regions that are affected by the disease. 4. Experimental results on the dataset demonstrate two things. First, the transfer learning techniques achieve higher classification accuracies of 99%. Second, our proposed adaptive thresholding approach performs well, giving good results, and therefore is a good approach for maize leaf disease quantification. 5. Comparison evaluation with the most common maize leaf disease severity analyses such as [PERSON]'s, [PERSON]'s and [PERSON]'s thresholding reveal that our approach is robust and gives good results. 6. We created an augmented dataset consisting of 46,898 samples from the original PlantVillage [22] dataset, available at: [[https://github.com/harryD1/maize_leaf_diseases_classification_and_quantification](https://github.com/harryD1/maize_leaf_diseases_classification_and_quantification)]([https://github.com/harryD1/maize_leaf_diseases_classification_and_quantification](https://github.com/harryD1/maize_leaf_diseases_classification_and_quantification)) (accessed on 1 June 2022). We structure this paper as shown in Figure 1. We begin by giving an overview of related studies on leaf disease classification and severity estimation in Section 2. Section 3 presents the materials and methods that are used in the experiments. In Section 4, we present the results and performance of the methods based on their ability to predict the correct class among four maize disease classes. In Section 5, we present the discussion, give an outline of the work's limitations, and give future directions of our research. Section 5 concludes the work.