Malaria and Dengue are the major vector borne diseases (VBDs) with high prevalence in the tropics, where their existence is significantly linked to the changing climatic conditions which favour the continuous breeding of vectors. In Tanzania, the burden for malaria is more pronounced in rural and peri-urban settings than in urban, with prevalence of up to 24% for under 5 children, varying across geographical zones. This paper presents a high-resolution UAV dataset collected from Mtwara and Tabora regions in southern and western zones, respectively, for development of surveillance models for potential mosquito breeding sites. The presented dataset contains 19 classes of mosquito breeding sites that were identified from 471 aerial images as a 80.5% of retained images with useful information from the total of 585 collected in Igunga and Masasi Districts in Tabora and Mtwara regions, respectively. With an experimental approach, the paper presents the use of the imagery dataset on the state of art Gemma 3N E4B, an instruction-tuned multimodal architecture capable of processing both visual and textual data. By using the 80.5% proportion remained after collaborative image labelling, we trained and validated the instructional model with exceptional convergence characteristics and a stabilization phase yielding a loss of 0.0319. The reduction in loss was 96.7%, from 9.620 to 0.319, within 117 steps.
Nyambo et al. (Sun,) studied this question.