Modern agriculture and food production systems are facing increasing pressures from
climate change, land and water availability, and, more recently, a pandemic. These factors …

High‐throughput phenotyping (HTP) with unoccupied aerial systems (UAS), consisting of
unoccupied aerial vehicles (UAV; or drones) and sensor (s), is an increasingly promising …

Timely and accurate crop monitoring and yield forecasting before harvesting are valuable for
precision management, policy and decision making, and marketing. The aim of this study is …

In this research a machine learning framework was developed for cotton yield estimation
using multi-temporal remote sensing data collected from unmanned aircraft system (UAS) …

Assessing plant population of cotton is important to make replanting decisions in low plant
density areas, prone to yielding penalties. Since the measurement of plant population in the …

This study presents a comparative study of multispectral and RGB (red, green, and blue)
sensor-based cotton canopy cover modelling using multi-temporal unmanned aircraft …

The incorporation of autonomous computation and artificial intelligence (AI) technologies
into smart agriculture concepts is becoming an expected scientific procedure. The airborne …

Precision agriculture has emerged as a dominant force in the United States, with
widespread adoption of advanced technologies and decision support systems (DSS) since …

High spatial and spectral resolution (H 2) imagery collected by unmanned aerial vehicle
(UAV) systems is an important data source for precise crop classification. Although this data …

Citrus greening is a severe disease significantly affecting citrus production in the United
States because the disease is not curable with currently available technologies. For this …