# Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation  
[PERSON]  
1 Department of Applied Physics and Telecommunications, Midlands State University, Senga Road,  
Govern P Bag 9055, Zimbabwe  
2 Faculty of Engineering, Busitema University, Tororo P.O. Box 236, Uganda  
3  
[PERSON]  
2 Faculty of Engineering, Busitema University, Tororo P.O. Box 236, Uganda  
3  
[PERSON]  
2 Faculty of Engineering, Busitema University, Tororo P.O. Box 236, Uganda  
3  
[PERSON]  
1 Department of Applied Physics and Telecommunications, Midlands State University, Senga Road,  
Govern P Bag 9055, Zimbabwe  
3  
[PERSON]  
1 Department of Applied Physics and Telecommunications, Midlands State University, Senga Road,  
Govern P Bag 9055, Zimbabwe  
3  
Footnote 10: [[https://www.mdpi.com/](https://www.mdpi.com/)]([https://www.mdpi.com/](https://www.mdpi.com/))models are selected based on meeting the requirements of the project [6; 7], such as training time and computational resources. Maize leaf disease quantification, or maize leaf severity analysis, is a very interesting research problem that seeks to determine the extent to which a leaf has been affected by disease [8; 9; 10]. This process relies on trained experts to visually estimate the affected area and assign a severity label. In the literature, multiple works have successfully applied multi-label automated visual classification frameworks to identify diseases and evaluate damage based on a human-defined scale [11]. The intuition is that an image can be associated with multiple labels. For example, a diseased leaf image can be classified as blight and then categorized as \"low severity\", \"medium severity\" or \"high severity\". Such methods rely on nominal scales for quantification, and have been used by [PERSON] et al. [11], among others, to indicate various levels of disease severity. This method of quantifying leaf diseases has been widely accepted to solve the problem of leaf disease quantification; however, with thousands of leaves to annotate, it becomes tedious and prone to errors. Although this solution is very popular in the literature, it cannot be applied to solve all disease quantification problems since leaf diseases have patterns that are statistical in nature, and hence more challenging for a human eye to assign a severity label. Machine learning methods that have been applied to solve maize leaf disease classification problems can be grouped into two sets: hand-crafted feature-based models [12] and automatic feature extraction models [13]. The parameters of hand-crafted feature-based models are estimated mainly based on the domain knowledge of the problem and the statistical characteristics of the leaf image. However, one of the biggest challenges, in general, of relying on statistical characteristics such as mean, standard deviation and variance, among others, is that: (i) variations may be indistinguishable for leaf diseases with low intraclass and high interclass variability [14]; (ii) different diseases may have similar effects on leaves; (iii) a single leaf may have multiple diseases, and (iv) disease symptoms may vary. On the other hand, automatic feature extraction models, particularly CNNs, require large amounts of data to learn the discriminative features of the image classes [14], which is a major limiting factor considering the difficulties of gathering the data. This work is motivated by the ability to quantify maize leaf diseases based on the information contained in the class activation maps (CAM) of the CNN. Unlike qualitative leaf disease severity estimation methods that rely mainly on domain knowledge, this approach uses the same deep learning model for leaf disease classification and leaf disease quantification, as described in Section 3. The critical component of our system is based on identifying which regions of the diseased leaf image have contributed more to the final decision of the CNN, and we express those regions as a percentage of the total leaf area to estimate disease severity.