Iron deficiency chlorosis (IDC) is the most common reason for chlorosis in soybean (Glycine max (L.) Merrill) and causes an average yield loss of 120 million dollars per year across 1.8 million ha in the North Central US alone. As the most effective way to avoid IDC is the use of tolerant cultivars, they are visually rated for IDC by experts; however, this method is subjective and not feasible for a larger scale. An alternate more objective image processing method can be implemented in various platforms and fields. This approach relies on a color vegetation index (CVI) that can quantify chlorophyll, as chlorophyll content is a good IDC indicator. Therefore, this research is aimed at developing image processing methods at leaf, plot, and field scales with machine learning methods for chlorophyll and IDC measurement. This study also reviewed and synthesized the IDC measurement and management methods. Smartphone digital images with machine learning models successfully estimated the chlorophyll content of soybean leaves infield. Dark green color index (DGCI) was the best-correlated CVI with chlorophyll. The pixel count of four different ranges of DGCI (RPC) was used as input features for different models, and the support vector machine produced the highest performance. Handheld camera images of soybean plots extracted DGCI, which mimicked visual rating, and canopy size that were used as inputs to decision-tree based models for IDC classification. The AdaBoost model was the best model in classifying IDC severity. Unmanned aerial vehicle soybean IDC cultivar trial fields images extracted DGCI, canopy size, and their product (CDP) for IDC severity monitoring and yield prediction. The area under the curve (AUC) was employed to aggregate the data into a single value through time, and the correlation between all the features and yield was good. Although CDP at latest growth stage had the highest correlation with yield, AUC of CDP was the most consistent index for soybean yield prediction. This research demonstrated that digital image processing along with the machine learning methods can be successfully applied to the soybean IDC measurement and the various soybean related stakeholders can benefit from this research.

Iron deficiency chlorosis (IDC) is a frequent problem in soybean [Glycine max (L.) Merr.] production on calcareous soils. Greenhouse and soil incubation experiments were established to evaluate pyritic lignite efficacy to supply Fe as compared to Fe-EDDHA and Fe-EDTA sources across contrasting IDC tolerance varieties grown on a slightly acid and a calcareous soil. Soybean IDC incidence was influenced by the soil properties and variety tolerance. All iron sources increased plant dry matter accumulation on the Leeper soil, whereas on the Marietta soil only lignite at 0.672 kg ha-1 produced an increase. Lignite at 0.336 kg ha-1 successfully improved Fe availability to the plant as reflected by an increase in Fe content. Furthermore, no differences were found between the low rate of lignite and the commercial fertilizers on any of the evaluated parameters. Results from this study indicate that pyritic lignite may serve as an acceptable source of Fe on problem soils to prevent IDC.

Iron deficiency chlorosis (IDC) is a frequent problem throughout many areas of the United States where soils are high in calcium carbonate (CaCO3), including the Blackland Prairie regions of Mississippi. The main objectives of this study were to 1) determine the effects of seven different cropping systems on IDC visual symptomology and grain yield in rainfed soybeans grown in calcareous soils, and 2) evaluate the effects of soil water tension (SWT) on IDC. Rotating soybeans with corn produced significantly higher yields for both tolerant and susceptible soybean varieties. IDC symptomology was worse, and yields were lower in cropping systems with lower average SWT, or wetter soils, throughout the growing season. Last, this study evaluated the use of multispectral imagery and apparent soil electrical conductivity (ECa) to identify IDC-prone areas of a field for the site-specific implementation of management strategies that produced higher yields in the plot-scale study.