Abstract Unmanned Aerial Vehicles (UAVs) combined with machine learning have a great
potential for crop yield estimation. In this study, a UAV equipped with an RGB (Red, Green …

Because the manual counting of soybean (Glycine max) plants, pods, and seeds/pods is
unsuitable for soybean yield predictions, alternative methods are desired. Therefore, the …

Corn (Zea mays L.) silage is an important feed ingredient in dairy cow diets in New York
(NY). Improving corn nitrogen (N) management will help increase farm profitability while …

Harvester-mounted yield monitor sensors are expensive and require calibration and data
cleaning. Therefore, we evaluated six vegetation indices (VI) from unmanned aerial system …

The aim was to evaluate if maize (Zea mays L.) grain and silage yield can be estimated from
unmanned aerial systems (UAS) imagery. A fixed-wing UAS was used to collect imagery …

Harvester-mounted yield monitor systems are increasingly used to document corn (Zea
mays L.) yield. The three most commonly used spatial estimation methods to convert point …

On-farm experimentation (OFE) allows farmers to improve crop management over time. The
randomized complete blocks design (RCBD) with field-length strips as individual plots is …

CONTEXT Reliable stability zone delineation requires considering spatial variability from
each year and temporal variability across at least three years. Yet, in cases where farms lack …

The on‐farm research network concept enables a group of farmers to test new agricultural
management practices under local conditions with support from local researchers or …

Corn (Zea mays L.) silage yield monitor data offer crucial insights into spatial and temporal
yield variability. However, equipment's sensor malfunctioning can result in data loss, and …