Machine learning (ML) and its multiple applications have comparative advantages for improving the interpretation of knowledge on different agricultural processes. However, there are challenges that impede proper usage, as can be seen in phenotypic characterizations of germplasm banks. The objective of this research was to test and optimize different analysis methods based on ML for the prioritization and selection of morphological descriptors of Rubus spp. 55 descriptors were evaluated in 26 genotypes and the weight of each one and its ability to discriminating capacity was determined. ML methods as random forest (RF), support vector machines, in the linear and radial forms, and neural networks were optimized and compared. Subsequently, the results were validated with two discriminating methods and their variants: hierarchical agglomerative clustering and K-means. The results indicated that RF presented the highest accuracy (0.768) of the methods evaluated, selecting 11 descriptors based on the purity (Gini index), importance, number of connected trees, and significance (p value < 0.05). Additionally, K-means method with optimized descriptors based on RF had greater discriminating power on Rubus spp., accessions according to evaluated statistics. This study presents one application of ML for the optimization of specific morphological variables for plant germplasm bank characterization.

The rapid and precise detection of diseases and plant disorders is the basis for the adequate and timely design of management strategies. Currently, there are several non-destructive alternatives that allow early detection, highlighting the use of spectral cameras attached to unmanned aerial vehicles (UAVs). The objective of this research was to evaluate the use of multispectral cameras on UAVs to discriminate vascular wilt caused by Verticillium spp., (VW), waterlogging stress (WL), and an unknown alteration (UA) in commercial potato (Solanum tuberosum) variety "Diacol Capiro" crops. Plots were monitored during the crop cycle, performing the visual characterization of the diseases and disorders present. Five spectral band images were acquired using a MicaSense RedEdge spectral camera attached to a Map-T680 hexacopter drone to extract the bands and calculate the vegetation indices that were calibrated and evaluated to determine their ability to discriminate between diseased and healthy plants based on a generalized linear model (GLM) and Kappa index. Additionally, the supervised random forest classification method was implemented, optimized, and evaluated using the accuracy, area under receiver operating characteristic curve (ROC-AUC), kappa index, and inference error based on k-fold cross-validation. After algorithms optimization our results show a classifier accuracy, kappa and ROC-AUC values to VW, WL and UA between 73.5-82.5%, 0.56-0.71, 0.97-0.98, and 35 37.5-51.9%, 0.07-0.06, and 0.88-0.94 for plots 1 and 2, respectively. This study reports an approach to the use of multispectral cameras attached to UAVs as a tool with potential for the detection of diseases and physiological disorders in commercial potato crops.

The spittlebug (Aeneolamia varia) is one of the most important sugarcane pests in Colombia, where a recent increase in population and distribution specially in southwestern Colombia have led to the need for new technologies for integrated pest management. The objectives of this study were to determine the spatial distribution of this pest in commercial sugarcane fields and to validate machine learning (ML) tools for indirect injury detection and impact on yield (damage) using satellite images. This study was carried out in fields grown with the CC 01-1940 variety in El Cerrito, Valle del Cauca, Colombia, where systematic sampling of the populations (number of adults and nymphs per stem) was carried out. The spatial aggregation and distribution were determined using Moran’s index and point patterns, sequence observations, and analysis with distance indicators (Sadie). The indirect injury detection and quantification of the impact on production were carried out with a ML approach using satellite image products with 10 m spatial and five days temporal resolutions, obtained from a Sentinel-2 sensor using Google Earth Engine. The results indicated that spittlebug populations had an aggregate spatial behavior and high spatial dependence. In addition, the ML algorithms predicted spittlebug injury, and the effect on production was estimated at 26.4 tons of cane per hectare, which represented a 17% reduction in the expected yield. The use of spatial analysis and remote sensing tools are an alternative for indirect detection of injury and for understanding population dynamics of the pest in sugarcane, so they can become instrumental for decision-making on an integrated pest management program.