As we stated in previous Smart Farm Newsletter articles, in-season yield prediction is desirable information for producers. Recently, we had a very interesting chat with a farmer during AgSmart 2022. This farmer was asking about the procedure we adopted for yield forecasting in previous years. I explained to him this forecast was based on site-specific assessments of the crop yield calculated based on the number of heads and kernels per head sampled a few weeks before harvest.
The farmer's reaction to my explanation was: “I have been doing this for decades and that is how I know what will be my field average yields, so where is the innovation on this?” Although the farmer had a point, the emphasis of our yield forecast is not only to obtain the average yield for the field, but how and by how much the yield varies WITHIN the field. This information can be used while planning for variable rate application of fertilizers for next season, and obtaining this information a few weeks before harvest may result in better fertilizer price. Also, in the case of any environmental disaster (fire, hail, etc.) the within-field yield information would still be available guaranteeing continuity of data (important as we move to a data-driven agriculture), and this information could be used to accurately assess the loss. A previous Smart Farm yield forecast article presents a great example: when using a predicted yield map, it provided reliable information; however, before harvest the crop was hit by a hail storm. This caused over 50% of yield loss, which was also assessed by comparing the maps from predicted yield and yield monitor output.
In-season yield prediction has multiple applications in precision agriculture. Thus, the Digital Ag Team at Olds College of Agriculture & Technology has continued the evaluation of this tool as a component of the HyperLayer Data Project. For the past three years, Unmanned Aerial Vehicle (UAV also known as drone) and Sentinel 2 (S2) imagery were used for yield forecast evaluation, and stunning results were obtained. However, both technologies present positive and negative aspects.
While UAV imagery provides high-resolution data (centimeter-level), collecting and processing the data requires time, experience, licenses, etc. which reduces the scalability potential of this technology. On the other hand, S2 imagery is automatically collected and can be easily downloaded from HERE, thus a very scalable technology. However, it provides images with a lower resolution (10 m for red, blue, green, and near-infrared bands) and images are subject to effects of atmospheric conditions (clouds, smoke, etc.). In this scenario, we have decided to test another solution, PlanetScope (PS), also a satellite platform but with images acquired daily (versus a 5-day interval for Sentinel 2), which increases the probability of having good quality and clear images. In addition, PS imagery is provided with a higher spatial resolution (3.7 m) than S2 (10 m). Although this technology is very promising, it is important to mention that it is a paid service. However, Planet provides an Educational and Research Program which allowed us to have free but limited non-commercial access to PlanetScope imagery.
To forecast the grain yield on spring wheat, 20 locations within one of Smart Farm fields (Field 15/16) were sampled. These locations were selected based on multiple layers of data previously collected through the HyperLayer Data Project (soil samples, yield, proximal soil sensor, terrain, etc.). In each location, 1 m of the crop was evaluated by counting the number of heads and kernels per head (Fig. 1), in sequence, using thousand kernel weight (TKW) these values were converted to their equivalent in bushels per acre. The results are shown in Table 1.