Biology subjects

Legg, J.

Publications and source records attributed to Legg, J..

6 recordsLinked to original sources

bioRxiv2025

Electrochemical Lateral Flow Assay with Linked Analytics for the Surveillance of Cassava Brown Streak Disease in East Africa

Cassava brown streak disease (CBSD) severely threatens food security in East Africa and the livelihoods of hundreds of millions globally. Effective control requires large-scale surveillance in resource-limited settings, which is currently lacking. We present ELLA (Electrochemical Lateral Flow Assay with Linked Analytics), a portable, battery-free, low-cost digital diagnostic platform integrating lateral flow assays with near-field communication and electrochemical readouts for cloud-based data storage and analytics. Validated through extensive field trials in East Africa and smaller studies in Brazil, ELLA achieved 89% agreement with RT-qPCR and 95% with ELISA, often surpassing ELISA sensitivity at a material cost below US$1 per test. Leveraging ELLAs molecular results, we trained a deep-learning model (DeepELLA) for rapid, image-based diagnosis of CBSD, enabling scalable surveillance of this and potentially emerging plant pathogens. By combining electrochemical sensing, digital connectivity, and AI-driven analytics, ELLA offers a powerful tool to strengthen plant disease monitoring and food security. Its modular design also allows adaptation to other chemical and biological targets, creating opportunities for novel datasets and new insights into plant and environmental health.

bioengineering↗

bioRxiv2025

Pathogen and pest communities in food security crops across climate gradients: Anticipating future challenges in the Great Lakes region of Africa

CONTEXTTropical cropping systems must adapt to the current and future geographic distribution of pathogen and pest communities. An important research gap is how climate change may shift the distribution of pathogens and pests in tropical lowlands and highlands. OBJECTIVEWe evaluated the current geographic risk of 27 pathogens and pests in the production of four food security crops (banana, cassava, potato, and sweetpotato) in the Great Lakes region of Africa, and the potential future risk under climate change. Models for each pathogen and pest indicate the potential for changes in geographic distribution, with model fit indicating the potential for decision support systems to facilitate management. METHODSFirst, cropland connectivity analysis identified locations likely important in the spread of crop-specific pathogens and pests, such as locations in Rwanda and Burundi. Second, we surveyed the 27 economically important pathogens and pests in Rwanda and Burundi, mapping the distribution of each across climate gradients and quantifying patterns of association. Third, we used machine learning to develop models of each species as a function of environmental variables, including host landscape variables. We also evaluated the increase in temperature across altitudes under future climate change scenarios in this region. RESULTS AND CONCLUSIONSAmong the ten machine-learning algorithms evaluated, random forests and support vector machines generally performed best for predicting severity and infestation. Host landscape variables were useful predictors for some species. Based on climate matching, 44% of the pathogens and pests could become more common with warmer temperatures at higher altitudes, while 17% may become less common. SIGNIFICANCEThis study indicates adaptation priorities for crop health in a region with multiple challenges to agricultural sustainability. The models developed here also indicate which species may have more potential and relevance for future development of pathogen and pest forecasts.

ecology↗

bioRxiv2024

Decision support for managing an invasive pathogen through efficient clean seed systems: Cassava mosaic disease in Southeast Asia

CONTEXTEffective seed systems must both distribute high-performing varieties efficiently and slow or stop the spread of pathogens and pests. Epidemics increasingly threaten crops around the world, endangering the incomes and livelihoods of smallholder farmers. Responding to these food and economic security challenges requires stakeholders to act quickly and decisively during the early stages of invasions, typically with very limited resources. The recent introduction of cassava mosaic virus into southeast Asia threatens cassava production in the region. OBJECTIVESOur goal in this study is to provide a decision-support framework for efficient management of healthy seed systems, applied to cassava mosaic disease. The specific objectives are to (1) evaluate disease risk in disease-free parts of Cambodia, Lao PDR, Thailand, and Vietnam by integrating disease occurrence, climate, topology, and land use, using machine learning; (2) incorporate this predicted environmental risk with seed exchange survey data and whitefly spread in the landscape to model epidemic spread in a network meta-population model; and (3) use scenario analysis to identify candidate regions to be prioritized in seed system management. RESULTS AND CONCLUSIONSThe analyses allow stakeholders to evaluate strategy options for allocating their resources in the field, guiding the implementation of seed system programs and responses. Fixed rather than adaptive deployment of clean seed produced more favorable outcomes in this model, as did prioritization of a higher number of districts through the deployment of smaller volumes of clean seed. SIGNIFICANCEThe decision-support framework presented here can be applied widely to seed systems challenged by the dual goals of distributing seed efficiently and reducing disease risk. Data-driven approaches support evidence-based identification of optimized surveillance and mitigation areas in an iterative fashion, providing guidance early in an epidemic, and revising them as data accrue over time.

ecology↗

bioRxiv2020

Evaluating the accuracy of a smartphone-based artificial intelligence system, PlantVillage Nuru, in identifying the viral diseases of cassava

Premise of the studyNuru is an artificial intelligence system for diagnosis of plant diseases and pests developed as a public good by PlantVillage (Penn State University), FAO, IITA and CIMMYT. It provides a simple, inexpensive and robust means of conducting in-field diagnosis without requiring internet connection and provides real-time results and advice. The present work evaluates the effectiveness of Nuru as an in-field diagnostic tool by comparing the diagnosis capability of Nuru to that of cassava experts (researchers trained on cassava pests and diseases), agricultural extension agents and farmers. MethodsThe diagnosis capability of Nuru and that of the assessed individuals was determined by inspecting cassava plants in-field and by using the cassava symptom recognition assessment tool (CaSRAT) to score images of cassava leaves. ResultsNurus accuracy for symptom recognition when using six leaves (74 - 88%, depending on the condition) was similar to that of experts, 1.5-times higher than agricultural extension agents and two-times higher than farmers. DiscussionThese findings suggests that Nuru can be an effective tool for in-field diagnosis of cassava diseases and has a potential of being a quick and cost-effective means of disseminating knowledge from researchers to agricultural extension agents and farmers.

plant biology↗

bioRxiv2017

Global cropland connectivity: A risk factor for invasion and saturation by emerging pathogens and pests

The geographic pattern of cropland is an important risk factor for invasion and saturation by crop-specific pathogens and arthropods. Understanding cropland networks supports smart pest sampling and mitigation strategies. We evaluate global networks of cropland connectivity for key vegetatively-propagated crops (banana and plantain, cassava, potato, sweetpotato, and yam) important for food security in the tropics. For each crop, potential movement between geographic location pairs was evaluated using a gravity model, with associated uncertainty quantification. The highly-linked hub and bridge locations in cropland connectivity risk maps are likely priorities for surveillance and management, and for tracing intra-region movement of pathogens and pests. Important locations are identified beyond those locations that simply have high crop density. Cropland connectivity risk maps provide a new risk component for integration with other factors - such as climatic suitability, genetic resistance, and trade routes - to inform Pest Risk Assessment and mitigation.

ecology↗

bioRxiv2017

A risk assessment framework for seed degeneration: Informing an integrated seed health strategy for vegetatively-propagated crops

Pathogen build-up in vegetative planting material, termed seed degeneration, is a major problem in many low-income countries. When smallholder farmers use seed produced on-farm or acquired outside certified programs, it is often infected. We introduce a risk assessment framework for seed degeneration, evaluating the relative performance of individual and combined components of an integrated seed health strategy. The frequency distribution of management performance outcomes was evaluated for models incorporating biological and environmental heterogeneity, with the following results. (1) On-farm seed selection can perform as well as certified seed, if the rate of success in selecting healthy plants for seed production is high; (2) When choosing among within-season management strategies, external inoculum can determine the relative usefulness of incidence-altering management (affecting the proportion of diseased plants/seeds) and rate-altering management (affecting the rate of disease transmission in the field); (3) Under severe disease scenarios, where it is difficult to implement management components at high levels of effectiveness, combining management components can produce synergistic benefits and keep seed degeneration below a threshold; (4) Combining management components can also close the yield gap between average and worst-case scenarios. We also illustrate the potential for expert elicitation to provide parameter estimates when data are unavailable.

epidemiology↗