Biology subjects

Mwaipopo, B.

Publications and source records attributed to Mwaipopo, B..

2 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↗

bioRxiv2025

Occurrence and Distribution of Common Bacterial Blight and Bacterial Leaf Blight in Tanzanian Lowland and Highland Agro-ecologies using Digital Image Analysis

Common Bacterial Blight (CBB) and Bacterial Leaf Blight (BLB) are important constraints affecting paddy and bean yields, respectively, causing significant losses if unmanaged. For effective planning and management of such diseases, accurate and reliable quantification, i.e., prevalence, incidence, and severity, becomes crucial. Despite this, the quantification of these diseases in Tanzania remains limited. Here, digital image analysis (ImageJ and Plantix) was used in the identification and quantification of CBB and BLB in low and highland agroecologies represented by Kilosa and Mbarali districts, respectively, by surveying 24 paddy and common bean fields across 10 villages. The study revealed that CBB and BLB were highly prevalent (100%) across study sites, with varying incidence and severity. Incidence and severity of both CBB and BLB were significantly higher in Kilosa than in Mbarali, with a large proportion (>19%) of these variations accounted for by district-level differences. Interestingly, significant variations in incidence and severity were observed even at the village level. In Kilosa, CBB severity differed significantly among villages (p = 0.018), while in Mbarali, BLB incidence and severity varied significantly (p < 0.001; p = 0.0436). Village-level differences accounted for over 41% of the total variation. Conclusively, the study indicates that CBB and BLB are highly prevalent across both Kilosa and Mbarali districts and their respective villages, with incidence and severity varying due to both district- and village-level differences. To manage these diseases effectively, site-specific management and geographically targeted interventions are required that account for local variability while prioritizing areas of higher infection risk, such as Kilosa. This approach will not only improve plant disease management programs but also ensure efficient resource use, thereby promoting sustainable disease control under changing climatic conditions.

plant biology↗