This study reviews the techniques and tools used for automatic disease identification, state-of-the-art DL models, and recent trends in DL-based image analysis, and evaluates various DL architectures, providing guidance on the suitability of these models for production environments.
It is suggested that dietary patterns rich in plant-based foods, with moderate inclusion of healthy animal-based foods, may enhance overall healthy aging, guiding future dietary guidelines.
This review provides a comprehensive examination of the bioactive roles of tannins, their nutritional implications, and their sensory effects, highlighting their importance in both dietary applications and overall well-being.
The green synthesis of nanoparticles from biomass and waste with a focus on synthetic mechanisms and applications in energy production and storage, medicine, environmental remediation, and agriculture and food is reviewed.
Arrangements have been made with the Cambridge University Press for the production of a quarterly journal, the British Journal of Nutrition, which will be devoted to the publication of original work in all branches of nutrition.
The role of AI, machine learning (ML) and other emerging technologies to overcome current and future crop management challenges are reviewed, emphasizing the transformative potential of these technologies in improving agricultural productivity and tackling global food security issues.
The number of illnesses, hospitalizations, and deaths in the United States caused by 7 major foodborne pathogens by using surveillance data and other sources, adjusted for underreporting and underdiagnosis is estimated by using surveillance data and other sources.
The association of the novel DI-GM with markers of gut microbiota diversity demonstrates the potential utility of this index for gut health-related studies.
This work synthesize and discuss the logical and implemented approaches used to design and assemble SynComs, highlighting important principles, challenges, and trends in utilizing SynComs as alternatives to agrochemicals.
The study compared a wide range of study subjects to investigate scientific approaches for smart irrigation, and focuses on the key components of smart irrigation, such as real-time irrigation scheduling, IoT, the importance of an internet connection, smart sensing, and energy harvesting.
The review highlights that state-of-the-art deep learning models have achieved impressive accuracies, with classification tasks often exceeding 95% and detection and segmentation networks demonstrating precision rates above 90% in identifying plant diseases and pest infestations.
This review aims to highlight the main groups of microbial enzymes found in soil, their role in the global nutrient cycles of carbon, nitrogen, phosphorus, sulfur, carbon sequestration, and the influence of intensive agriculture on microbial enzymatic activity, and the variations in enzyme activity across different climate zones and soil ecosystems.
It is hypothesized that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods, and support from other omics approaches, the boundaries of the current limitations could overcome the boundaries of the current limitations to achieve the highest possible prediction accuracy, making GS an effective tool in plant breeding.
A comprehensive overview of recent advances in deep learning techniques for HSI reconstruction is provided and highlights the transformative impact of deep learning-based hyperspectral image reconstruction on agricultural and food products and anticipates a future where these innovations will lead to more advanced and widespread applications in the agri-food industry.
Effective detection techniques and promising solutions to the global contamination of food with Aspergillus mycotoxins are provided, which is of great significance to ensuring food security and protecting people's lives and health.
An advanced method for plant disease detection utilizing a modified depthwise convolutional neural network integrated with squeeze-and-excitation blocks and improved residual skip connections is proposed, highlighting its effectiveness and potential as a practical tool for disease identification in agricultural applications.
Phages and phage-derived lytic proteins can be considered potential antimicrobials in the traditional farm-to-fork context, which include phage-based mixtures and commercially available phage products.
The MobileENet deep learning architecture for accurate plant pest identification with less computational effort is developed and the accuracy performance is improved to 98.83% with less information loss.
A pipeline for fine-tuning and RAG is proposed, and the tradeoffs of both are presented for multiple popular LLMs, including Llama2-13B, GPT-3.5, and GPT-4.
The integration of omics technologies, genome editing and protein design with artificial intelligence (AI) promises rapid advances in the field of crop improvement that will improve global food security.
It is found that minimally processed diets led to greater weight loss and cardiometabolic improvements than ultraprocessed diets following UK healthy eating guidelines at 8 weeks.
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