Technological advances have played a critical role in the production of flower crops,
enabling farmers to maximize yields and reduce losses while also improving the quality of …

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

Accurate and timely crop yield prediction is difficult to achieve due to the nonlinear and
dynamic spatial–temporal correlations included during the crop growth process. The latest …

The development of cost-effective, digitally based decision support systems is a key
challenge in the optimization of farm management. Yet, the majority of sensor-based …

Organic agriculture is often regarded as less damaging to the environment than
conventional agriculture, though at the expense of lower yields. Field-specific precision …

On-farm experimentation (OFE) embeds the conduct of agronomic research within normal
farm business operations such that experiments are driven by farmers' needs for business …

During the past few decades, a range of digital strategies for Nitrogen (N) management
using various types of input data and recommendation frameworks have been developed …

Crop growth is often uneven within an agricultural parcel, even if it has been managed
evenly. Aerial images are often used to determine the presence of vegetation and its spatial …

Few mechanisms turn field-specific ecological data into management recommendations for
crop production with appropriate uncertainty. Precision agriculture is mainly deployed for …

Accurate modeling of site-specific crop yield response is key to providing farmers with
accurate site-specific economically optimal input rates (EOIRs) recommendations. Many …