Wildfires are increasing in frequency and severity due to climate change, land-use changes and droughts, threatening global forests. Traditional detection methods lack speed and scalability. Smart forests, driven by machine learning (ML), utilize sensor networks and data analytics to facilitate early warning systems, risk evaluation and mitigation strategies. This article reviews recent ML advancements in wildfire detection, prediction and response, emphasizing current technologies, real-world applications and key challenges, while outlining future opportunities for enhancing forest resilience through intelligent, data-driven solutions.

This article investigates the potential of coastal fisheries to contribute to climate change mitigation through blue carbon ecosystems. It focuses on habitats like mangroves, seagrasses, and salt marshes, which not only capture atmospheric carbon but also support marine biodiversity and fishery productivity. The objective is to assess how integrating blue carbon science with fisheries resource management can enhance ecological sustainability and climate resilience. This article reviews blue carbon practices and policies, highlighting challenges like data gaps, weak coordination, and limited community involvement. It calls for integrated strategies that link climate goals, livelihoods, and carbon finance, emphasizing inclusive, science-based approaches to enhance both environmental sustainability and socioeconomic resilience in coastal regions.

Livestock are an invaluable asset to agro-based countries like India, providing power, transport, fertilizer, fuel, and nutrition. They serve as a vital source of food and economic security for many families. However, protozoan parasites pose a significant threat to livestock health, adversely affecting their well-being, productivity, and overall contribution to rural livelihoods. Common protozoan genera affecting livestock include Trypanosoma, Babesia, Theileria, Sarcocystis, Toxoplasma, Neospora, Giardia, Cryptosporidium, and various species of Coccidia. These pathogens are associated with significant morbidity, mortality, and reproductive disturbances. Therefore, timely and accurate diagnosis, followed by appropriate treatment, is crucial to prevent disease progression, reducing transmission, and minimizing the impact on animal health and agricultural output. The objective of this article is to explore effective diagnostic methods for key protozoan diseases in livestock, facilitating early detection and intervention to curb their spread and mitigate their pathological consequences.