Integration of IoT and Artificial Intelligence in Ecuadorian Agriculture: A Systematic Literature Review (2020–2025)

The Internet of Things (IoT) and Artificial Intelligence (AI) have become key technologies for advancing precision agriculture. This systematic literature review explores their integration in Ecuadorian agriculture, addressing four main aspects: AI techniques applied for pest detection and crop monitoring, types of agricultural data utilized, the most commonly implemented IoT platforms, and sensors employed in monitoring systems. The review encompasses 40 studies published between 2020 and 2025, revealing a predominance of machine learning approaches, with notable applications of Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN), achieving accuracy levels between 0,80 and 0,95. Environmental and agricultural production data were the most frequently used, while platforms such as ThingSpeak and ThingsBoard, together with local solutions, were commonly employed for real-time management. The findings highlight current technological trends and challenges related to connectivity, costs, and data quality, emphasizing the need for future research to enhance productivity and sustainability in strategic Ecuadorian crops such as banana, cacao, mango, and rice.