Bartolome Laarnie O.¹, Gurat Christopher A.², Patricio Fidel Jr G.², Tobias Sherilyn P.³, Wais Armilyn H.^2,

This study presents an assessment of the current coffee berry disease detection practices among coffee farmers in Ambaguio, Nueva Vizcaya, Philippines. Coffee production remains a vital source of livelihood in the area; however, diseases such as Coffee Leaf Rust and Coffee Berry Disease continue to significantly affect crop productivity and quality. At present, most farmers rely on traditional manual inspection methods to identify plant diseases. These methods are often based on visual observation and personal experience, making them time-consuming, labor-intensive, and may be inaccurate, especially in the absence of agricultural experts and technical support in rural communities. To gain deeper insight into these challenges, the study examines the existing practices, limitations, and conditions faced by coffee farmers in detecting and managing coffee berry diseases. It also explores the need for improved and technology-supported approaches such as Artificial Intelligence (AI) and mobile-based solutions for future adoption. The findings of this assessment are expected to serve as a baseline for the possible development of a smartphone-based Coffee Berry Disease Detection system tailored to the local context. Ultimately, this preliminary study aims to provide insights that can support more efficient, accurate, and sustainable disease management practices, contributing to improved coffee production, farmer livelihood, and agricultural development in Ambaguio, Nueva Vizcaya.

Agricultural Technology, Artificial Intelligence, Coffee Berry Disease, Coffee Farmers, Coffee Leaf Rust, Disease Detection Practices, Manual Inspection, Smartphone-Based Detection, Sustainable Agriculture