Project Summary:

As technology improves and scale, low-cost alternatives to traditional environmental sensors continue to emerge in the agricultural sector. In sugar beet production, two main operations have been identified by Colorado State University (CSU) Agriculture Water Quality Program (AWQP), in collaboration with Wester Sugar (WS) as an opportunity to capitalize on integrating low-cost, Internet of Things (IoT) sensing into the sugar production process: 1) detecting sugar beet susceptibility to cercospora leaf spot (CLS), a common and detrimental leaf pest, and 2) detecting sugar loss in post-harvest sugar beet piles with temperature sensors. In 2022, CSU AWQP prototyped and deployed four temperature (T) and relative humidity (RH) sensors to determine daily infection values (DIVs) for CLS with the help of University of Nebraska-Lincoln collaborators. Results from previous years indicated that the sensors were performing adequately such that they could be a reasonable substitute for current methods of monitoring CLS and storage pile temperature (PT). As such, WS has expressed interest in finding scalable ways to increase the number of sensors in use, as well as determine long-term solutions for data storage. This proposal details how CSU AWQP intends to increase functionality of existing IoT sensors used by WS and create educational workshops to train WS in the production of said sensors for future use.

Methods:

This project will utilize previously developed, low-cost, IoT CLS and PT sensors with real-time remote data access via CSU-hosted data portal created specifically for WS with data security, and data portability in mind (https://csuwaterqualitygroup.iot.ubidots.com/). This platform offers the ability to have WS employees have unique profiles with varying editing/viewing permissions as necessary for efficient operation.  Additionally, transferring data to other services is easily allowed through Application Programming Interface (API) keys.  This is what allowed CSU to quickly stream data to the UNL-hosted Panhandle Sugar beet Cercospora Monitoring website (https://phrec-irrigation.com/#/cls_monitoring).

This year, CSU AWQP intends to scale up available CLS sensors from 4 to 20-25 by adding functionality to the PT sensors.  With additional coding, 3D-printing, and soldering, and CSU AWQP staff can create more parts that could attach to existing PT sensors in place of existing sensor strings for monitoring pile temp. This would allow PT sensors to then become CLS sensors instead for the growing season, and then switch back to PT sensors when harvest is over.  The same functionality would be added to existing CLS sensors and 7-10 new CLS sensors to be developed.

Additionally, GPS functionality will be integrated in each PT/CLS sensor so that each device can report its location automatically, as opposed to being reported by the agronomist as is currently done.

Data management will be handled as previously done (as shown in Figure 1) via the CSU data portal. However, as WS continues to develop their own data management platforms, Microsoft Azure integrations may become possible if WS chooses. This may be done similarly to how data get transferred to UNL currently.

Finally, CSU AWQP staff intend on identifying employees at WS who have skills and interest in developing CLS/PT sensors. Once identified, educational meetings/workshops will be hosted to teach WS staff on the creation of CLS/PT sensors for future production.

Objectives & Timeline:

The objectives of this project are:

1. Improve and scale the previously developed, CLS/PT sensors to include GPS and sensor-switching capabilities. (Jan 1 – Jun 15)

2. In cooperation with WSC staff, deploy 20-25 CLS sensors across multiple states (ideally 1 sensor per WS agronomist) and measure performance (Jun 15 – Oct 31).

3. Stream data from CSU AWQP servers to UNL CLS monitoring platforms

4. Train WS staff to switch sensor cables on sensor units so that they can become PT sensors after 2023 harvest. PT sensors will measure temperature at 1-ft, 3-ft, and 5-ft depths to detect hot spots in real time.  A temperature threshold will be set to alert the users if the pile temperature goes above the threshold temperature. (Nov 1 – Dec 31) 

5. Analyze performance of the sensors (Nov 1 – Dec 31) 

6. Train WS staff on sensor development for future scalability (Nov 1 – Dec 31)