Recipient Organization
CERES IMAGING, INC.
1155 INDIANA ST
SAN FRANCISCO,CA 94107
Performing Department
(N/A)
Non Technical Summary
Precision irrigation and fertilization management are critical to maximizing productivity while minimizing resource use. Insufficient irrigation and fertilization reduces yields and quality, while excess irrigation wastes increasingly scarce water, and excess fertilization can lead to contamination of groundwater, soil and air. Optimizing these management decisions is difficult given the tools typically used to manage large farms. Most decisions about irrigation and fertilizer application on farms are made on the basis of field management experience and a small number of soil and leaf samples. While soil/leaf samples can be accurate, they usually have high operational cost and limited spatial coverage, leading to sub-optimal irrigation and fertilizer application for different areas within the field. Existing commercial aerial photography efforts can provide much better spatial resolution, but are difficult to translate into recommendations for field management.We aim to develop a method for using aerial imagery at select wavelengths to provide quantitative recommendations on amounts of resources to apply, and to estimate how crop yields will be affected. To develop this method, we will start by focusing on almonds, and collect both ground and aerial data during the course of the California almond growing season over a field with a variety of irrigation and fertilization treatment blocks. We will study how our aerial measurements relate to both water/fertilizer input, as well as year end yields. This method provides an avenue for inexpensively measuring water and nutrient needs over a large area, and offers potential for maintaining or increasing yields while decreasing resource use, and reducing associated environmental impacts.
Animal Health Component
80%
Research Effort Categories
Basic
(N/A)
Applied
80%
Developmental
20%
Goals / Objectives
Our goal is to quantify water and nutrient status of plants via aerial imagery, which we will then use to provide clear, quantitative and high spatial resolution recommendations on water and fertilizer application to farmers. Throughout next year's growing season, we will simultaneously collect tissue samples, soil moisture measurements, and on-the-ground plant water status measurements, in conjunction with flyovers using our imaging system. With these data, we will develop an approach to quantify in-season water and nutrient needs from our aerial imagery, and integrate our crop chlorophyll and water content measurements to develop accurate models for real-time yield monitoring.Objectives:For a minimum of 10 dates in the 2016 California growing season, collect aerial data simultaneously with the following ground data for test almond fields in Kern County:soil moisture measurements using neutron probesstem water potential, using a pressure bombtree growth monitoring, using dendrometersFor a subset of 4 of the 10 dates in which we are collecting aerial data, we will also collect the following ground data:leaf nutrient content, including nitrogen, phosphorus, and potassium, using leaf tissue analysisleaf chlorophyll content, measured using a SPAD chlorophyll meterFully process all aerial data, including sensor calibration, atmospheric correction, mosaicking, georegistration, masking, and segmentation of canopy and soil.Extract tree level estimates of plant reflectances in selected visible/NIR bands, and temperature from thermal band, for all trees for which ground data was collected.Produce model relating visible/NIR reflectances, temperature, and water and nutrient needs, as determined from ground data.Use end of season yield data, and yield from past years, to build model relating aerial data and yields.
Project Methods
Data for the experiment will be collected simultaneously in the air and on the ground throughout the 2016 California almond growing season over an experimental field operated by the UC Cooperative Extension (UCCE). Ground measurements, which include soil moisture, stem water potential, tree diameter, leaf chlorophyll content, and leaf nutrient content (NPK), will be conducted in the standard way for tens of trees within the study orchard. Aerial measurements will be collected using our imaging system. Our system includes narrow spectrum imaging in the visible/NIR (10 nanometer bandpasses centered at 480, 550, 670, 700, and 800 nanometers), designed to capture the chlorophyll signature of trees; and a thermal camera that captures over a 7.5-13 micron band that is sensitive to plant temperature. Our aerial data collection is novel. The wavelength coverage of the system provides sensitivity to both water and nutrient stress, at a resolution smaller than a tree canopy. This allows comprehensive aerial assessment of plant status at a plant level, which can then be compared with measurements obtained on the ground. At the end of the growing season, we will collect tree level yields (and will have access to tree level yields for each year going back to 2008), as well as treatment block level yields for the field. Additionally, we will collect aerial data through the season for a different experimental almond field. For this second field, we will obtain end of year yields within individual blocks within the field, but will not have ground measurements during the season.From this season long data collection, we will have a combined ground/aerial dataset with high spatial and temporal resolution, well beyond any that has previously been analyzed in the academic literature. For the primary field, we will construct a table in which each date/tree combination will be a row (e.g., 50 trees sampled at 10 different dates will produce 500 rows), and in which columns will be soil moisture, stem water potential, tree diameter, leaf chlorophyll content, leaf nutrient content, canopy level reflectance for each of the 5 visible/NIR bands, and canopy temperature from the aerial thermal measurement. We will have an additional table of yields, with a row for each tree, and a column for each year.We will use these tables as training data for multiple multivariate analyses. The goal of these analyses will be prediction of ground stress measures using the aerial data alone, as well as year-end yield. In these, we will use only aerial data as independent variables, and will use various ground measurements as dependent variables to be predicted by the model. We plan to explore various approaches for modeling the data and performing regression, which we will evaluate based on their success at fitting these training data. We will do an additional analysis using the ground measurements of water status and nutrient status as independent variables to fit the observed year-end yield data.Based on conversations with customers, relative predictions that are good to +/- 300 pounds/acre and that would highlight causes of variation in yield would be an attractive product. To evaluate the success of our modeling efforts, we will compare the explanatory power of the aerial data as compared to the ground data in predicting yields at a tree level. As we collect tree level data for the entire orchard with our imagery, we can also use generate yield predictions for each treatment block within the field (of which there are 160), and compare those predictions to actual yields. Yields depend on a number of factors beyond inputs to the field, including winter rain, nighttime temperatures, pollination success, and pests. These factors are likely to not be apparent at a small scale level, however. Thus we will focus this year on modeling yield anomalies (difference from mean field yields) within the field. For this, we will aim to do better than +/- 300 pounds/acre in predicting differences in yield from the mean at the block level. We will also use observed yields from the second almond field (without in-season ground measurements of water or nutrient status) as a test dataset for our model relating aerially measured visible/NIR/thermal data to yield, and aim to achieve the same level of accuracy in this second field.Through our partnership with the UCCE, we will share the results of the trial at monthly workshops that the UCCE hosts, explaining the methodology, as well as applications of our research.