PREDICTING CORN NUTRIENT STATUS BASED ON HYPERSPECTRAL IMAGING
Significant portions of nitrogen (40–60%), phosphorus (80–90%) and potash (30–50%) applied in agricultural fields are not taken up by plants, causing serious issues for farmers and the environment. Fertilizer losses result in greater fertilizer input costs and the cost of fertilizer is projected to increase due to limited ore resources and increasing fossil fuel prices. Moreover, excess fertilizer application can contaminate water and air, resulting in human health problems. Leaching fertilizers also induce eutrophication, acid rain and global climate change. Therefore, developing crops with high nutrient uptake efficiency is important for economic and environmental sustainability of agriculture. Crop improvement depends on efficiency and accuracy of genotyping and phenotyping. Genotyping has improved in recent years and is generally efficient and accurate. In contrast, improvements in phenotyping lag far behind. Lack of high-throughput (efficient, accurate and inexpensive) phenotyping (HTP) methods limit the speed of genetic improvement. As a result, there is an increasing interest in development of HTP for predicting crop nutrient status. My research addresses whether hyperspectral data in the visible-near-infrared range (HS-VNIR) acquired by a handheld device or an unmanned aerial vehicle (UAV) can be used for predicting maize nutrient status. Proximal and remote sensing data coupled with ground reference measurements of hybrid maize nutrient status were collected in fertilizer strip trials conducted at Purdue Agricultural Centers located throughout Indiana. Statistical models were developed to predict nutrient status based on HS-VNIR with coefficients of determination of cross-validation [R2 (CV)] used to evaluate the performance of the predictive models. Models with acceptable goodness-of-fit [R2 (CV) > 0.30] were considered satisfactory. These studies demonstrated that models developed using handheld proximal sensing data performed adequately for predicting N, K, Mg, Ca, P, S, Mn, Zn and B. Similarly, models developed using UAV-based HS-VNIR could be used to predict N, K, Mg, Ca, P, S, Mn, Zn and B. Models that combine proximal and remote sensing data also performed well with predictions of N, K, Mg, Ca, P, S, Mn, Zn and B. In conclusion, handheld or UAV-based hyperspectral imaging can provide corn breeders with HTP data on the status of all macronutrients (N, K, Mg, Ca, P, S) and some micronutrients (Mn, Zn, B). Deployment of this technology may provide a valuable tool to support development of cultivars with improved nutrient uptake efficiencies.
- Doctor of Philosophy
- West Lafayette