Adaptive Thresholding of CNN Features for Maize Leaf Disease Classification and Severity Estimation

Abstract

Convolutional neural networks (CNNs) are the gold standard in the machine learning (ML) community. As a result, most of the recent studies have relied on CNNs, which have achieved higher accuracies compared with traditional machine learning approaches. From prior research, we learned that multi-class image classification models can solve leaf disease identification problems, and multi-label image classification models can solve leaf disease quantification problems (severity analysis). Historically, maize leaf disease severity analysis or quantification has always relied on domain knowledge—that is, experts evaluate the images and train the CNN models based on their knowledge. Here, we propose a unique system that achieves the same objective while excluding input from specialists. This avoids bias and does not rely on a “human in the loop model” for disease quantification. The advantages of the proposed system are many. Notably, the conventional system of maize leaf disease quantification is labor intensive, time-consuming and prone to errors since it lacks standardized diagnosis guidelines. In this work, we present an approach to quantify maize leaf disease based on adaptive thresholding. The experimental work of our study is in three parts. First, we train a wide variety of well-known deep learning models for maize leaf disease classification, then we compare the performance of the deep learning models and finally extract the class activation heatmaps from the prediction layers of the CNN models. Second, we develop an adaptive thresholding technique that automatically extracts the regions of interest from the class activation maps without any prior knowledge. Lastly, we use these regions of interest to estimate image leaf disease severity. Experimental results show that transfer learning approaches can classify maize leaf diseases with up to 99% accuracy. With a high quantification accuracy, our proposed adaptive thresholding method for CNN class activation maps can be a valuable contribution to quantifying maize leaf diseases without relying on domain knowledge.

Machine Learning and Deep Learning for Maize Leaf Disease Classification: A Review

“…This method detection displayed with a heatmap, an explanation through colors in image certain parts. Methods such as Grad-CAM, Grad-CAM++, ScoreCAM can be tested for implementation [61], [62]. None of the articles that have been reviewed, implemented this algorithm yet.…”

Adams Algorithm with Generative Adversial Network for Maize Leaf Disease Classification

“…In 2022, Mafukidzeet al [23] have executed the DL method for the identification and quantification of diseased maize. Initially, RGB images of the maize leaves were taken as input and fed into the DL model for the classification of images into various categories.…”

A field-based recommender system for crop disease detection using machine learning

“…The research aimed at integrating a smartphone-based disease diagnostic application with a real-time feedback tool that farmers can use at any time in their gardens. Computer vision and machine learning (ML) have been used to diagnose crop diseases in crops, e.g., study by Sambasivam and Opiyo ( 2021 ) that investigated cassava diseases, crop yield estimation, crop weed identification, and severity estimation among other areas (Kumar et al, 2015 ; Tripathi and Maktedar, 2020 ; Mafukidze et al, 2022 ). Transfer learning and convolutional neural network approach are used on a cassava dataset of 2,756 images comprising three cassava diseases and two types of pest damage (Ramcharan et al, 2017 ).…”