Digital image authentication techniques have recently gained a lot of attention due to their importance to a large number of military and medical applications, banks, and institutions, which require a high level of security. Generally, digital images are transmitted over insecure media, such as the Internet and computer networks of various kinds. The Internet has become one of the basic pillars of life and a solution to many of the problems left by the coronavirus. As a result, images must be protected from attempts to alter their content that might affect important decision-making. An image authentication (IA) system is a solution to this difficult problem. In the previous literature, several methods have been proposed to protect the authenticity of an image. Digital image watermark is a strategy to ensure the reliability, resilience, intellectual property, and validity of multimedia documents. Digital media, such as images, audio, and video, can hide content. Watermarking of a digital image is a mechanism by which the watermark is embedded in multimedia and the image of the watermark is retrieved or identified in a multimedia entity. This Review Article Hasan et al.; AJRCOS, 9(3): 34-51, 2021; Article no.AJRCOS.69033 35 paper reviews IA techniques, watermark embedding techniques, tamper detection methods and discusses the performance of the techniques, the pros and cons of each technique, and the proposed methods for improving the performance of watermark techniques.

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Image Authentication Based on Watermarking Approach: Review

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.

https://www.mdpi.com/2076-3417/12/17/8412/pdf?version=1661343772

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

Identification, classification, and quantification of crop defects are of paramount interest to the farmers for preventive measures and decrease the yield loss through necessary remedial actions Due to the vast agricultural field, manual inspection of crops is tedious and time-consuming UAV based data collection, observation, identification, and quantification of defected leaves area are considered to be an effective solution The present work attempts to estimate the percentage of affected groundnut leaves area across five regions of Andhra Pradesh using image processing techniques The proposed method involves color space transformation combined with a thresholding technique to perform the segmentation The calibration measures are performed during acquisition with respect to UAV capturing distance, angle, and other relevant camera parameters Finally, our method can estimate the consolidated leaves and defected area The image analysis results across these five regions reveal that around 14–28% of leaves area is affected across the groundnut field, and thereby yield will be diminished correspondingly Hence, it is recommended to spray the pesticides on the areas affected alone across the field to improve the plant growth and thereby yield will be increased

Ashraf Mahmud

Papers

Quantification of Groundnut Leaf Defects Using Image Processing Algorithms