Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.

Digital twins, succinctly described as the digital representation of a physical object, is a concept that has emerged relatively recently with increasing application in the manufacturing industry. This article proposes the application of this concept to the healthcare domain to provide enhanced clinical decision support and enable more patient‐centric, and simultaneously more precise and individualized care to ensue. Digital twins combined with advances in Artificial Intelligence (AI) have the potential to facilitate the integration and processing of vast amounts of heterogeneous data stemming from diversified sources. Hence, in healthcare this can provide enhanced diagnosis and treatment decision support. In applying digital twins in combination with AI to complex healthcare contexts to assist clinical decision making, it is also likely that a key current challenge in healthcare; namely, providing better quality care which is of high value and can lead to better clinical outcomes and a higher level of patient satisfaction, can ensue. In this focus article, we address this proposition by focusing on the case study of cancer care and present our conceptualization of a digital twin model combined with AI to address key, current limitations in endometrial cancer treatment. We highlight the role of AI techniques in developing digital twins for cancer care and simultaneously identify key barriers and facilitators of this process from both a healthcare and technology perspective.This article is categorized under: Application Areas > Health Care