Citrus canker (CC), caused by one of the most destructive subfamilies of the bacterial phytopathogen Xanthomonas citri subsp. Citri (Xcc), poses a serious threat to the significantly important citrus fruit crop grown worldwide. This has been the subject of ongoing epidemiological and disease management research. Currently, five different forms have been identified of CC, in which Canker A (Xanthomonas citri subsp. citri) being the most harmful and infecting the majority of citrus cultivars. Severe infection symptoms include leaf loss, premature fruit drop, dieback, severe fruit blemishing or discoloration, and a decrease in fruit quality. The infection spreads rapidly through wind, rain splash, and warm and humid climates. The study of the chromosomal and plasmid DNA of bacterium has revealed the evolutionary pattern among the pathovars, and research on the Xcc genome has advanced our understanding of how the bacteria specifically recognize and infect plants, spread within the host, and propagates itself. Quarantine or exclusion programs, which prohibit the introduction of infected citrus plant material into existing stock, are still in use. Other measures include eliminating sources of inoculum, using resistant hosts, applying copper spray for protection, and implementing windbreak systems. The main focus of this study is to highlight the most recent developments in the fields of Xcc pathogenesis, epidemiology, symptoms, detection and identification, host range, spread, susceptibility, and management. Additionally, it presents an analysis of the economic impact of this disease on the citrus industry and suggests strategies to reduce its spread, including the need for international collaboration and research to reduce the impact of this disease on the global citrus industry.

Ensuring global food security amid mounting challenges, such as population growth, disease infestations, resource limitations, and climate change, is a pressing concern. Anticipated increases in food demand add further complexity to this critical issue. Plant pathogens, responsible for substantial crop losses (up to 41%) in major crops like wheat, rice, maize, soybean, and potato, exacerbate the situation. Timely disease detection is crucial, yet current practices often identify diseases at advanced stages, leading to severe infestations. To address this, remote sensing and Hyperspectral imaging (HSI) have emerged as robust and nondestructive techniques, exhibiting promising results in early disease identification. Integrating machine learning algorithms with image data sets enables precise spatial–temporal disease identification, facilitating timely detection, predictive modeling, and effective disease management without compromising fitness or climate adaptability. By harnessing these cutting-edge technologies and data-driven decision-making, growers can optimize input costs while achieving enhanced yields, making significant strides toward global food security in the face of climate change risks. This review will discuss some of the foundational concepts of remote sensing, several platforms used for remote sensing data collection, successful application of the approach, and its future perspective.

Integrating artificial intelligence (AI), with edge computing, and precision agriculture is revolutionizing farming making it more resilient and sustainable. This article focuses on combine positive impact that the merger of these transformative technologies, can have in providing solutions for key challenges such as plant disease detection, resource optimization, and real-time decision-making. AI algorithms enables rapid and precise analysis of massive agricultural data allowing early disease detection and preventive measures to ensure plant health. Concurrently, edge computing gives the power of reduced latency with on spot data processing and solutions provision to the farmers, even in areas with limited coverage. The fusion of these technologies aligns with key UN sustainable development goals (SDGs), by optimizing the use of water, fertilizers, and pesticides, reducing environmental impacts, and mitigating climate change effects. However, the widespread adoption of AI and edge computing in agriculture is constrained by challenges such as hardware limitations, data collection, quality issues, and the need for technical expertise in particular cases. This review explores how these technologies are currently being used in agriculture, their pros, cons, and potential areas for further research and development. Encouraging interdisciplinary collaboration and continuous innovation will be crucial to overcome these challenges, ensuring that AI and edge computing play a central role in securing global food security and promoting climate-resilient farming.