Agroview: Cloud-based application to process, analyze and visualize UAV-collected data for precision agriculture applications utilizing artificial intelligence

TLDR

Traditional sensing technologies in specialty crop production rely on manual sampling, which is time‑consuming and labor‑intensive. The study presents Agroview, a cloud‑based AI application designed to simplify UAV‑based surveying, reduce data collection time, and lower costs. Agroview processes UAV and other platform data to detect, count, and geo‑locate plants, measure plant height and canopy size, and generate plant health maps. In a commercial citrus orchard, Agroview achieved a 2.3 % MAPE in tree detection, 4.5–12.93 % MAPE in height estimation, and 12.9–34.6 % MAPE in canopy size, demonstrating a consistent, cost‑effective, rapid phenotyping method.

Abstract

Traditional sensing technologies in specialty crops production, for pest and disease detection and field phenotyping, rely on manual sampling and are time consuming and labor intensive. Since availability of personnel trained for field scouting is a major problem, small Unmanned Aerial Vehicles (UAVs) equipped with various sensors can simplify the surveying procedure, decrease data collection time, and reduce cost. To accurate and rapidly process, analyze and visualize data collected from UAVs and other platforms (e.g. small airplanes, satellites, ground platforms), a cloud and artificial intelligence (AI) based application (named Agroview) was developed. This interactive and user-friendly application can: (i) detect, count and geo-locate plants and plant gaps (locations with dead or no plants); (ii) measure plant height and canopy size (plant inventory); (iii) develop plant health (or stress) maps. In this study, the use of this Agroview application to evaluate phenotypic characteristics of citrus trees (as a case study) is presented. It was found, that this emerging technology detected citrus trees with mean absolute percentage error (MAPE) of 2.3% in a commercial citrus orchard with 175,977 trees (1,871 acres; 39 normal and high-density spacing blocks). Furthermore, it accurately estimated tree height with 4.5% and 12.93% MAPE for normal and high-density spacing respectively, and canopy size with MAPE of 12.9% and 34.6% for normal and high-density spacing respectively. It provides a consistent, more direct, cost-effective and rapid method for field survey and plant phenotyping.