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Background Banana ( Musa spp.) is the most popular marketable fruit crop grown all over the world, and a dominant staple food in many developing countries. Worldwide, banana production is affected by numerous diseases and pests. Novel and rapid methods for the timely detection of pests and diseases will allow to surveil and develop control measures with greater efficiency. As deep convolutional neural networks (DCNN) and transfer learning has been successfully applied in various fields, it has freshly moved in the domain of just-in-time crop disease detection. The aim of this research is to develop an AI-based banana disease and pest detection system using a DCNN to support banana farmers. Results Large datasets of expert pre-screened banana disease and pest symptom/damage images were collected from various hotspots in Africa and Southern India. To build a detection model, we retrained three different convolutional neural network (CNN) architectures using a transfer learning approach. A total of six different models were developed from 18 different classes (disease by plant parts) using images collected from different parts of the banana plant. Our studies revealed ResNet50 and InceptionV2 based models performed better compared to MobileNetV1. These architectures represent the state-of-the-art results of banana diseases and pest detection with an accuracy of more than 90% in most of the models tested. These experimental results were comparable with other state-of-the-art models found in the literature. With a future view to run these detection capabilities on a mobile device, we evaluated the performance of SSD (single shot detector) MobileNetV1. Performance and validation metrics were also computed to measure the accuracy of different models in automated disease detection methods. Conclusion Our results showed that the DCNN was a robust and easily deployable strategy for digital banana disease and pest detection. Using a pre-trained disease recognition model, we were able to perform deep transfer learning (DTL) to produce a network that can make accurate predictions. This significant high success rate makes the model a useful early disease and pest detection tool, and this research could be further extended to develop a fully automated mobile app to help millions of banana farmers in developing countries.

Bananas ( Musa spp.) are a critical global food crop, providing a primary source of nutrition for millions of people. Traditional methods for disease monitoring and detection are often time-consuming, labor-intensive, and prone to inaccuracies. This study introduces an AI-powered multiplatform georeferenced surveillance system designed to enhance the detection and management of banana wilt diseases. We developed and evaluated several deep learning foundation models, including YOLO-NAS, YOLOv8, YOLOv9, and Faster-RCNN to perform accurate disease detection on both platforms. Our results demonstrate the superior performance of YOLOv9 in detecting healthy, Fusarium Wilt and Xanthomonas Wilt diseased plants in aerial images, achieving high mAP@50, precision and recall metrics ranging from 55 to 86%. In terms of ground level images, we organized the dataset based on disease occurrence in Africa, Latin America, India, Asia and Australia. For this platform, YOLOv8 outperforms the rest and achieves mAP@50, precision and recall between 65 and 99% depending on the plant part and region. Additionally, we incorporated Explainable AI techniques, such as Gradient-weighted Class Activation Mapping, to enhance model transparency and trustworthiness. Human in the Loop Artificial Intelligence was also utilized to enhance the ground level model’s predictions.

Small-holder banana fields are often intercropped with various annual crops to optimize land-use in East and Central Africa, a practice severely constrained by light availability under the banana canopy. Light availability is not a major constraint in newly established banana fields, giving a window of opportunity to target light-demanding annual crops before shifting to more shade-tolerant crops. This study investigated the performance of climbing and bush beans and the vegetable amaranth in banana fields with varying shade levels across three sites in the South Kivu province, DR Congo. These crops were selected for their highly nutritious and good market value and the added benefit of nitrogen fixation for the legumes. We show that both grain legumes and vegetable amaranth can achieve reasonable yields during a first annual cropping season in newly established banana fields, irrespective of the plant density. Declines in yield occurred during a second cropping season in more densely spaced banana fields (2 × 2 m and 2 × 3 m). A greater decline occurred in amaranth and its cultivation should be limited to the first annual cropping season or to less dense banana fields. The legumes could be extended to a second cropping season with reasonable yield. Significant variability in amaranth and legumes performance was observed across sites, with rapid yield declines occurring under more fertile soil conditions due to fast banana growth/canopy formation and under more vigorous cultivars. The choice of banana spacing will need to be tailored to the banana cultivar, soil conditions and the farmers’ objectives.

Bananas and plantains are vital for food security and smallholder livelihoods in Africa, but diseases pose a significant threat. Traditional disease surveillance methods, like field visits, lack accuracy, especially for specific diseases like Xanthomonas wilt of banana (BXW). To address this, the present study develops a Deep-Learning system to detect BXW-affected stems in mixed-complex landscapes within the Eastern Democratic Republic of Congo. RGB (Red, Green, Blue) and multispectral (MS) images from unmanned aerial vehicles UAVs were utilized using pansharpening algorithms for improved data fusion. Using transfer learning, two deep-learning model architectures were used and compared in our study to determine which offers better detection capabilities. A single-stage model, Yolo-V8, and the second, a two-stage model, Faster R-CNN, were both employed. The developed system achieves remarkable precision, recall, and F1 scores ranging between 75 and 99% for detecting healthy and BXW-infected stems. Notably, the RGB and PAN UAV images perform exceptionally well, while MS images suffer due to the lower spatial resolution. Nevertheless, specific vegetation indexes showed promising performance detecting healthy banana stems across larger areas. This research underscores the potential of UAV images and Deep Learning models for crop health assessment, specifically for BXW in complex African systems. This cutting-edge deep-learning approach can revolutionize agricultural practices, bolster African food security, and help farmers with early disease management. The study’s novelty lies in its Deep-Learning algorithm development, approach with recent architectures (Yolo-V8, 2023), and assessment using real-world data, further advancing crop-health assessment through UAV imagery and deep-learning techniques.Article HighlightsDeep-learning models are developed using drone-captured images to detect BXW-affected banana stems in complex landscapes.Combinations of vegetation indexes and pansharpening techniques enhance low-resolution multispectral images.The system achieves a 75% to 99% precision, recall, and F1 score for identifying healthy and BXW-infected stems in RGB, MS, and pansharpening images.

Background Inflammatory bowel diseases (IBDs) are chronic gastrointestinal diseases that negatively affect the enjoyment of food and engagement in social and cultural gatherings. Such experiences may promote psychosocial challenges, an aspect of IBD often overlooked and under‐supported in clinical settings and research. Objectives This study explored the psychosocial experiences that young adults with IBD have with food via a qualitative patient‐led research process. Methods Trained patient researchers conducted this study by engaging peers via semi‐structured interviews and focus groups in a three‐step co‐design process. Participants (n = 9) identified the research topic (SET), explored the topic and identified emerging themes (COLLECT), refined themes and made recommendations for healthcare system change (REFLECT). Results Themes that emerged included: ‘Experimenting with Food’, ‘Evolution Over Time’, ‘Diet Changes are Emotional’ and ‘Role of Stigma’. Participants identified the significance and frustrations of repeated testing and experimenting with food compatibility, and noted nuances in food relationships as they gain knowledge and experience over time. They emphasized the importance of maintaining a sense of hope throughout and wished to impart this to newly diagnosed patients. Conclusion Participants experience numerous psychosocial challenges as they strive to manage their diet, noting gaps in support available from IBD practitioners. Participants made practical recommendations for healthcare system change to improve patient outcomes, highlighting the importance of sharing stories and collaboratively including patients in the development of new services and protocols. Authors recommend further research in this area to build a body of knowledge and support that helps IBD patients maintain hope while navigating challenges with food. Patient or Public Contribution The first four authors on this paper were the lead researchers in this study's design and analysis and identify as patients; they conducted the research with this identity at the forefront following a peer‐to‐peer research model. These authors were mentored by patient researchers who also contributed to the manuscript, and the research process itself was co‐lead and directed by other patient participants and consultants. Results and recommendations coming from this paper came directly from patient participants.

EDITOR: We at Safari West wish to express our heartfelt thanks to The Press Democrat, Staff Writer Rayne Wolfe and particularly photographer John Burgess for the engaging story on Dr. Glen Benjamin and all the babies born at Safari West this past year. Wolfe's article wonderfully evoked the extraordinary spirit and character of Dr. Benjamin and his remarkable work. We are honored and blessed to have him.

Media and service provider reports of sexual and gender based violence (SGBV) perpetrated against men in armed conflicts have increased. However, response to these reports has been limited, as existing evidence and programs have primarily focused on prevention and response to women and girl survivors of SGBV. This study aims to contribute to the evidence of SGBV experienced by males by advancing our understanding of the definition and characteristics of male SGBV and the overlap of health, social and economic consequences on the male survivor, his family and community in conflict and post-conflict settings. The qualitative study using purposive sampling was conducted from June-August 2010 in the South Kivu province of Eastern DRC, an area that has experienced over a decade of armed conflict. Semi structured individual interviews and focus group discussions were conducted with adult male survivors of SGBV, the survivors' wife and/or friend, health care and service providers, community members and leaders. This study found that SGBV against men, as for women, is multi-dimensional and has significant negative physical, mental, social and economic consequences for the male survivor and his family. SGBV perpetrated against men and boys is likely common within a conflict-affected region but often goes unreported by survivors and others due to cultural and social factors associated with sexual assaults, including survivor shame, fear of retaliation by perpetrators and stigma by community members. All key stakeholders in our study advocated for improvements and programs in several areas: (1) health care services, including capacity to identify survivors and increased access to clinical care and psychosocial support for male survivors; (2) economic development initiatives, including microfinance programs, for men and their families to assist them to regain their productive role in the family; (3) community awareness and education of SGBV against men to reduce stigma and discrimination and increase acceptance of survivors by family and larger community.

Many survivors of gender based violence (GBV) in the Democratic Republic of Congo (DRC) report barriers to access health services including, distance, cost, lack of trained providers and fear of stigma. In 2004, Foundation RamaLevina (FORAL), a Congolese health and social non-governmental organization, started a mobile health program for vulnerable women and men to address the barriers to access identified by GBV survivors and their families in rural South Kivu province, Eastern DRC. FORAL conducted a case study of the implementation of this program between July 2010-June 2011 in 6 rural villages. The case study engaged FORAL staff, partner health care providers, community leaders and survivors in developing and implementing a revised strategy with the goal of improving and sustaining health services. The case study focused on: (1) Expansion of mobile clinic services and visit schedule; (2) Clinical monitoring and evaluation system; and (3) Recognition, documentation and brief psychosocial support for symptoms suggestive of anxiety, depression and PTSD. During this period, FORAL treated 772 women of which 85% reported being survivors of sexual violence. Almost half of the women (45%) reported never receiving health services after the last sexual assault. The majority of survivors reported symptoms consistent with STI. Male partner adherence to STI treatment was low (41%). The case study demonstrated areas of strengths in FORAL’s program, including improved access to health care by survivors and their male partner, enhanced quality of health education and facilitated regular monitoring, follow-up care and referrals. In addition, three critical areas were identified by FORAL that needed further development: provision of health services to young, unmarried women in a way that reduces possibility of future stigma, engaging male partners in health education and clinical care and strengthening linkages for referral of survivors and their partners to psychosocial support and mental health services. FORAL’s model of offering health education to all community members, partnering with local providers to leverage resources and their principal of avoiding labeling the clinic as one for survivors will help women and their families in the DRC and other conflict settings to comfortably and safely access needed health care services.

The fundamental concepts set forth in the formal Post-Conflict Needs Assessment (PCNA) initiative created by the United Nations Development Group have the potential to be adapted to assist local groups in documenting the needs of and the provision of health care to survivors of sex- and gender-based violence (SGBV) in Eastern Democratic Republic of Congo (DRC). In partnership with Congolese health care providers, we took the first step in advocating for a locally-adapted and focused needs assessment through the development and administration of surveys to providers in the South Kivu Province, DRC. The content of the surveys was largely based on lists of medical supplies deemed essential for reproductive health and for the care of survivors by the Reproductive Health Response in Crises Consortium. The providers in both urban and rural settings considered many of the supplies identified on the surveys necessary for the care of survivors (84%; p < 0.05) but considered few accessible (26%; p < 0.05) in their particular clinical settings. Providers also felt that the existing list of supplies was inadequate to meet the needs of survivors, and also that providers needed ongoing training to improve supply procurement and management, more knowledge of the needs of male survivors of SGBV, and more educational opportunities to improve the quality of care to survivors. Given the deficiencies expressed by providers in the surveys, this study demonstrated a critical need for a locally-adapted and focused needs assessment to improve health services to survivors.