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Crop disease detection is important due to its significant impact on agricultural productivity and global food security. Traditional disease detection methods often rely on labour‐intensive field surveys and manual inspection, which are time‐consuming and prone to human error. In recent years, the advent of imaging technologies coupled with machine learning (ML) algorithms has offered a promising solution to this problem, enabling rapid and accurate identification of crop diseases. Previous studies have demonstrated the potential of image‐based techniques in detecting various crop diseases, showcasing their ability to capture subtle visual cues indicative of pathogen infection or physiological stress. However, the field is rapidly evolving, with advancements in sensor technology, data analytics and artificial intelligence (AI) algorithms continually expanding the capabilities of these systems. This review paper consolidates the existing literature on image‐based crop disease detection using ML, providing a comprehensive overview of cutting‐edge techniques and methodologies. Synthesizing findings from diverse studies offers insights into the effectiveness of different imaging platforms, contextual data integration and the applicability of ML algorithms across various crop types and environmental conditions. The importance of this review lies in its ability to bridge the gap between research and practice, offering valuable guidance to researchers and agricultural practitioners. [ABSTRACT FROM AUTHOR]

Issue Addressed: Farmers experience skin cancer and die from melanoma at significantly higher rates than the general Australian population. This study examined Australian farmers' engagement with self‐skin examinations (SSE), participation in clinical skin examinations (CSE) by a health professional, and self‐reported barriers to engagement with these important skin cancer detection practices. Methods: A cross‐sectional, mixed‐methods design was used. Australian farmers were recruited through an industry‐based organisation representing livestock farmers. Farmers (N = 498; 22–89 years; 83.1% male) responded to a paper‐based survey that included closed‐ and open‐ended questions. Results: Farmers reported engagement with self‐conducted SSE and routine CSE that was comparable to findings in the general population, but 29.4% of farmers reported that they had not sought a CSE as soon as possible after noticing changes to their skin. Farmers reported a range of barriers to SSE, including physical difficulties examining their skin, difficulties identifying changes in their skin, forgetfulness, and lack of motivation. Barriers to CSE included accessibility, cost, difficulties finding the right doctor, and avoidance and complacency. Conclusions: There is a need to make clinical skin cancer detection more accessible to farmers, in addition to promoting self‐skin examination and help‐seeking behaviours within this at risk population. So What?: Novel approaches are needed to address systemic barriers faced by Australian farmers. These may include the use of teledermatology or artificial intelligence to assist with CSE. Remote training delivery methods may be also utilised to teach SSE skills to farmers who may be otherwise unable to access such opportunities. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel paradigm for disease detection. We build an artificial intelligence based system where various biomedical data are retrieved from distributed and homogeneous sensors. We use different deep learning architectures (VGG16, RESNET, and DenseNet) with ensemble learning and attention mechanisms to study the interactions between different biomedical data to detect and diagnose diseases. We conduct extensive testing on biomedical data. The results show the benefits of using deep learning technologies in the field of artificial intelligence of medical things to diagnose diseases in the healthcare decision‐making process. For example, the disease detection rate using the proposed methodology achieves 92%, which is greatly improved compared to the higher‐level disease detection models. [ABSTRACT FROM AUTHOR]