# Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation
## 2 Related Work
### Transfer Learning on Maize Leaf Disease Classification  
In its simplest terms, transfer learning is adapting or reusing the knowledge that the CNN has learned previously and applying that knowledge to a new set of problems [23]. For example, in Figure 2, we can have a CNN such as VGG16 that has learned to recognize objects from the ImageNet database and then use all or part of that knowledge to recognize  
Figure 1: Paper layout. the maize leaf diseases in our database. In most cases, we transfer the knowledge gained from a lot of data to a problem that has relatively little training data. This is helpful because most image recognition problems from popular datasets such as ImageNet (14 million images and 1000 classes), MS-COCO (1.5 million images and 1000 classes) and CIFAR10 (60,000 images and 10 classes) rely on similar low-level features, and the CNN will have learned the basic structure of image objects, thereby increasing the learning rate on the new dataset. Transfer learning has been used in several studies to address some of the limitations of deep learning models in generic image recognition problems. From the literature, it is clear that transfer learning leverages less training data, decreases training time and greatly improves the performance of deep learning models, as we shall see in this paper. Most early studies in this field focus on the use of handcrafted features for leaf disease detection and classification. Notably, the work by [PERSON] et al. proposes an early maize leaf disease detection and classification system using machine learning [24]. In their work, they extract distinctive maize leaf features such as shape, color and texture and apply machine learning techniques such as Naive Bayes, Decision Tree, K-Nearest Neighbor, Support Vector Machine and Random Forest to classify the leaf images [24]. Their results demonstrated that the Random Forest classifier performs well, giving accuracies of over 79%, while the Support Vector Machine achieves a lower accuracy of just over 77%. In a similar study, [PERSON] et al. developed maize leaf disease recognition and classification using only a Support Vector Machine [25]. They extracted similar features (texture, color and morphology), but in this instance, the authors managed to obtain good accuracies of 95% from a dataset of only 800 images. This can be attributed to a small dataset. These results and other works [26; 27; 28; 29] demonstrate the feasibility of using handcrafted features for crop leaf disease classification; however, the limitations of relying on such techniques naturally include: (1) the need for expert knowledge, (2) lack of robustness and (3) extensive time needed to identify relevant features from leaf images [30]. In order to improve performance, some authors have presented a unique approach to identifying maize leaf diseases. They consider a learning paradigm that automatically learns the features of maize leaf diseases without the need for expert knowledge. For instance, [PERSON] et al. developed an automated system to identify Norther Leaf Blight in maize plants using deep learning [31]. They trained several CNNs to identify the presence of the disease on maize leaf images. Although their task was a binary classification problem, the authors managed to achieve a classification accuracy of 96% on the test set. A common technique in the literature is to combine a deep learning feature extraction module with a supervised classifier such as SVM [32], Decision Tree [33] or K-Nearest Neighbor [33; 34]. This kind of architecture leverages the convolutional operation of the  
Figure 2: Illustration of the transfer learning process. Transfer learning involves adapting a pre-trained CNN model to suit the requirements of a new problem. In this case, consider the original model (**a**) that has been trained to recognize objects from a dataset such as ImageNet.