Biology subjects

McCloskey, P.

Publications and source records attributed to McCloskey, P..

4 recordsLinked to original sources

bioRxiv2026

Integrated Molecular and AI-Based Diagnostics for Banana Diseases: Development, Optimization, and Field Deployment of LAMP and Computer Vision Technologies

Banana and plantain (Musa spp.) production in Sub-Saharan Africa is severely constrained by multiple diseases, with Banana bunchy top virus (BBTV) representing the most devastating viral threat. Inadequate diagnostic infrastructure limits effective management, particularly for asymptomatic infections disseminated through informal planting material exchange. This study presents an integrated diagnostic framework combining Loop-Mediated Isothermal Amplification (LAMP) molecular diagnostics with deep learning-based computer vision for rapid, scalable disease detection under field conditions. A LAMP assay targeting the BBTV DNA-S coat protein gene was developed using conserved sequences from diverse African isolates and validated with a simplified alkaline extraction protocol eliminating conventional DNA purification. The assay achieved 100% specificity and concordant detection with PCR and qPCR, reducing diagnostic time from 4 to 6 hours to 60 minutes. In-house recombinant Bst LF polymerase production demonstrated comparable enzymatic performance to commercial alternatives, with projected per-reaction cost reductions of 70 to 80%. Concurrently, an SSDLite MobileNetV2 object detection model was developed through 19 iterative training cycles on 19,914 field-collected images spanning 22 disease and physiological stress classes. The final model achieved recall rates of 92.5% for BBTV, 91.0% for Banana Xanthomonas Wilt, and 98.1% for healthy leaf classification, deployed via the PlantVillage mobile application for real-time offline diagnostics. A QR code-based metadata system integrates phenotypic AI assessments with molecular confirmation for comprehensive surveillance. This complementary framework addresses broad-scale phenotypic screening and molecular confirmation of pre-symptomatic infections, providing accessible tools to safeguard food security across Sub-Saharan Africa.

plant biology↗

bioRxiv2023

A low-cost, AI-powered Measurement Verification and Reporting System for growing trees with smallholder farmers

Limited access to low-cost tools to measure, report, and verify (MRV) tree growth with smallholder farmers limits the scaling of tree planting efforts in developing countries. Artificial Intelligence (AI) offers the potential for low-cost, reliable, and accessible measurement and verification tools to be developed for an MRV platform to scale tree planting efforts in developing countries. Here, we present an AI-powered non-contact tree diameter measurement and verification tool. We have developed an AI-powered algorithm that accurately estimates the diameter of a tree from an image of the tree with a reference object. This non-contact measurement method utilizes semantic segmentation and image processing techniques to analyze an image of the tree with the reference object. The performance of the proposed method was evaluated on 142 trees with tape-measured diameters at breast height ranging from 5 to 60 cm. A regression analysis between predicted and measured diameter values had an R2 and an RMSE of 0.97 and 2.23 cm, respectively. Thus, using a smartphone application, the non-contact method developed here can empower anyone to accurately measure and report tree growth by just taking pictures of the trees with the reference object. The images submitted with on-farm measurements serve as data for future verification operations using the AI-powered algorithm. With the reference object serving as a unique tree identifier, a trees survival and diameter measurements can be tracked over time. The MRV system described here, with the developed AI-powered non-contact tree diameter measurement and verification tool, can empower organizations to plant, grow, and monitor trees with anyone, including smallholder farmers.

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↗

bioRxiv2019

From village to globe: A dynamic real-time map of African fields through PlantVillage

A major bottleneck to the application of machine learning tools to satellite data of African farms is the lack of high-quality ground truth data. Here we describe a high throughput method using youth in Kenya that results in a cost-effective method for high-quality data in near real-time. This data is presented to the global community, as a public good, on the day it is collected and is linked to other data sources that will inform our understanding of crop stress, particularly in the context of climate change.

ecology↗