Identifying nematode damage in large soybean areas is not always achievable in a practical way. Multispectral reflectance sensors have not been thoroughly evaluated to detect nematode damage in soybeans (Glycine max L.). The main research aims of this study were to: (i) determine the bivariate relationship between individual spectral bands and vegetation indices (VIs) relative to soybean conditions (symptomatic versus asymptomatic), and (ii) to select the best model for identifying plant conditions using three algorithms (logistic regression—LR, random forest—RF, conditional inference tree—CIT) and three options for data input using bands, vegetation indices (VIs), and bands plus VIs. The trial was conducted in Brazil on three on-farm soybean fields presenting different species of nematode infestation. Multispectral imagery was obtained using a drone-mounted MicaSense RedEdge^® sensor. At each sampling, georeferenced point nematode infestation and spectral measurements of soybean plants were retrieved for the classification of symptomatic and asymptomatic areas, according to the threshold level adopted. Bivariate analysis of variance (ANOVA), LR, RF, and CIT were used to select the multispectral bands/VIs that discriminated among symptomatic and asymptomatic plants, assessing the best model via their respective parameters for accuracy, sensitivity, and specificity. The greatest classification accuracy (>0.70) was achieved when using the CIT algorithm with the spectral bands only, with green (560 ± 20 nm) and near-infrared (840 ± 40 nm) included as the main spectral input variables in the model. These results demonstrate the potential of combining remotely sensed data and machine learning to distinguish nematode-symptomatic and asymptomatic soybean plants.

Short-cycle horticulture crops often rely on fertigation to provide immediately available nutrients. This practice poses an environmental threat when nutrients, particularly phosphorus, leach from containers and enter waterways. One method that could be used to reduce the loss of phosphorus and other key anions from horticultural crops is incorporating activated aluminum into container substrates. This study investigates the incorporation of three rates of activated aluminum into a pine bark substrate, and the effects this amendment may have on the container leachate nutrient content and the growth of a popular short-cycle crop (Tagetes patula, French marigold). The addition of activated aluminum reduced the cumulative mass of phosphorus in container leachate by 69–96% compared to a standard pine bark substrate. The growth index and subjective plant quality scores of Tagetes were equivalent between the activated aluminum-amended substrates and pine bark only substrates, in all cases, producing a salable, quality crop. This study demonstrates that incorporating activated aluminum into container substrates is an effective way to reduce phosphorus loss to the environment without sacrificing crop quality.

Buckwheat (Fagopyrum esculentum Moench) belongs to the Polygonaceae family and has been widely cultivated due to its high nutritional, nutraceutical, and medicinal properties. Brazil ranks seventh-...