Joint Ops

Overview

Starting in 2021, IoT4Ag established a “Joint Ops” framework to provide a structure for integrating and evaluating systems comprising IoT4Ag technologies in the testbeds. We have currently defined two Joint Ops, which are (1) Integrated systems to optimize nitrogen (N) application in row crops, and (2) Integrated systems to optimize water use in tree crops. These Joint Ops bring together several ongoing IoT4Ag projects to achieve systems integration towards a common objective. IoT4Ag projects are continuously assessed for their readiness level and for opportunities to contribute to the Joint Ops.

Joint Op 1:

Integrated systems to optimize
nitrogen (N) application in row crops

Joint Op 2:

Integrated systems to
optimize water use in tree crops

Joint Op 3:

Integrated systems to mitigate crop loss due to disease and pests

Nitrogen Application in Row Crops

Joint Op 1, Integrated systems to optimize nitrogen (N) application in row crops, brings together IoT4Ag breakthrough technologies in corn and cotton testbeds at the Research and Education Centers at Purdue and UF and at fields of IPAB partner Corteva Agriscience and other Ag companies. The technologies include leaf moisture sensors and aerial and ground based robotic imaging and sampling, biodegradable batteries and IsoBlue and LoRa edge communications solutions, and data from the field with multi scale, multi mode sensor fusion and biophysical modeling.

Water Use in Tree Crops

Joint Op 2, Integrated systems to optimize water use in tree crops, brings together IoT4Ag breakthrough technologies in almond and pistachio testbeds at the UC Merced and regional orchards. The technologies include moisture, sap flow, and imaging sensors and aerial and ground based robotic coordination and path planning, wireless communications solutions, and orchard data with federative learning.

Integrated Systems to Mitigate Disease and Pests

Joint Op 3, Integrated suite of projects to facilitate early detection and mitigation of diseases and aflatoxin contamination of peanuts testbeds at the University of Florida. The projects focus on monitoring and detection of important environmental variables and other features associated with disease coupled with communication technologies and downstream analytics to inform decision-making and action. The technologies include automated crop monitoring using aerial and ground based robotic imaging, ML methods for detecting aflatoxin from hyperspectral images of peanut pods and kernels, and predictive models to processes data from the integrated sensor flows to inform crop management.