Machine vision monitoring and detection system for large farm activities

• Main Author: De Ocampo, Anton Louise P.
• Format: text
• Language: English
• Published: Animo Repository 2020
• Subjects: Agricultural innovations; Computer vision; Electrical and Computer Engineering
• Online Access: https://animorepository.dlsu.edu.ph/etd_doctoral/1416; https://animorepository.dlsu.edu.ph/context/etd_doctoral/article/2467/viewcontent/DeOcampo_AntonLouisePernez_11788879_MachineVisionMonitoringAndDetectionSystemForLargeFarmActivities_1_Redacted.pdf
• Institution: De La Salle University

In the country, it was only last March 2, 2019, that the ‘First Smart Farm in the Philippines’ has been inaugurated. The farm is owned by the government and not by any local farmer or farm owners. To hasten up the involvement of local farmers to the idea of smart farming, technologies that are easily deployable and less expensive can be introduced to them. One of the crux aspects in the implementation of smart farming is the monitoring system which observes the significant indicators that help farmers to identify what is needed, and where and when it should be applied. The machine vision monitoring and detection system developed in this research work consist primarily of three modules. First, the path planning module is designed to generate the mission-specific waypoints based on the user-defined area-of-interest (AOI) for the unmanned aerial vehicle intended for data acquisition. The second module is the farm activity monitor (FAM) which detects and counts farmers in the field and recognizes their activities. The final module is the crop health monitoring (CHM) which collects the field data related to vegetation fraction, weed estimate, nitrogen and chlorophyll contents of crops, and pest damage detection. Also, the crop schedule and the planned farm activities can be accessed.