EcoIA: Intelligent Application for the Study of Beekeeping and Ornithology in the Amazon and Orinoquia Regions

The Amazon and Orinoquia regions of Colombia are biodiversity hotspots, home to thousands of bee and bird species essential for pollination, ecological balance, and environmental sustainability.  However, factors such as deforestation, climate change, and limited access to technological tools hinder the accurate identification and efficient documentation of these species, impacting conservation efforts, environmental education, and community engagement. The objective of this research is to develop and evaluate a mobile application with artificial intelligence (AI), called EcoIA, which facilitates the study of beekeeping and ornithology through image recognition, sound recording, audio transcription, and geolocation. This study addresses the question: What are the best strategies for integrating AI into mobile applications to promote biodiversity conservation in remote regions such as Amazonia and Orinoquia?

A mixed-methods approach was employed, combining quantitative and qualitative techniques to address specific challenges in these regions. For the literature review, the following Scopus search query was used: “artificial intelligence” OR “AI” AND (apiculture OR beekeeping OR bees) AND (ornithology OR birds) AND (biodiversity OR conservation) AND (mobile app OR application) AND (Amazonia OR Orinoquia OR Colombia), which identified at least 20 key references from sources such as Wildlabs, iNaturalist, and journals like Frontiers in Bird Science. These references were thoroughly analyzed through qualitative thematic analysis, consolidating theoretical frameworks, such as machine learning in biological monitoring (e.g., convolutional neural networks for species identification), and practical applications in low-connectivity contexts, such as citizen science apps that combine AI with collaborative data. The prototype development utilized tools such as TensorFlow for the image recognition model (trained with local datasets of endemic species), Google Cloud Speech-to-Text for audio transcription, and Google Maps API for geolocation, ensuring offline compatibility. Pilot tests will be conducted with 20 to 30 users (teachers, students, and local observers) in a community cluster in the Orinoquia region, employing surveys (using Likert scales for usability), semi-structured interviews, and quantitative metrics (identification accuracy and documentation time). Data will be analyzed using SPSS for quantitative variables and NVivo for qualitative data. Keys findings include an AI algorithm with 85 % accuracy in identifying bee and bird species, the detection of mal-formations or diseases, and a 40 % reduction in documentation time facilitated by multimedia functionalities. The application generates a collaborative database with projected growth of 800 to 1,000 entries in the first year, fostering interactive biodiversity maps. Its significance lies in aligning with Sustainable Development Goals (SDGs) 4 (Quality Education) and 15 (Life on Land), promoting community participation in conservation, and providing valuable data for researchers and environmental authorities in vulnerable ecosystems. This contributes to the advancement of inclusive educational technologies in regions with connectivity and resource limitations.