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.

Identification of diseases and physiological disorders in potato via multispectral drone imagery using machine learning tools

Honey is a functional food used worldwide and recognized for its multiple health benefits. In the present study, the physicochemical and antioxidant properties of honey produced by two species of bees (Melipona eburnea and Apis mellifera) in two seasons were evaluated. In addition, the antimicrobial activity of honey against three bacterial strains was studied. The quality of honey analyzed by LDA (linear discriminant analysis) showed four clusters mediated by the interaction, the bee species, and the collection season resulting from a multivariate function of discrimination. The physicochemical properties of the honey produced by A. mellifera met the requirements of the Codex Alimentarius, while the M. eburnea honey had moisture values outside the established ranges of the Codex. Antioxidant activity was higher in the honey of A. mellifera, and both kinds of honey showed inhibitory activity against S. typhimurium ATCC 14028 and L. monocytogenes ATCC 9118. E. coli ATCC 25922 showed resistance to the analyzed honey.

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Physicochemical Parameters, Antioxidant Capacity, and Antimicrobial Activity of Honeys from Tropical Forests of Colombia: Apis mellifera and Melipona eburnea

The germplasm of Rubus is highly diverse and has important features for cultivated plant improvement, such as resistance to diseases and pests. Hybrids of Mora de Castilla have been observed in the wild. Rubus glaucus Benth was an allopolyploid obtained by hybridization between a black raspberry and a South American blackberry. Mora de Castilla is an important breeding material due to its large fruit size, low chilling requirements, excellent fruit quality, aroma, small drupelets, seeds, everbearing production habit, and resistance to root diseases. Particularly, Mora de Castilla has great potential as a parent to improve the size and quality of cultivated Rubus species. Mora de Castilla cultivars have been selected for breeding and improvement purposes in Colombia and Ecuador. These countries have implemented breeding programs that have identified and selected high-yield plant materials. Wild-type Mora de Castilla has been bred among commercial cultivars to improve their quality features. Germplasm repositories are important for maintaining cultivar diversity. 

Breeding Programs

Introduction Evaluations of interspecific hybrids are limited, as classical genebank accession descriptors are semi-subjective, have qualitative traits and show complications when evaluating intermediate accessions. However, descriptors can be quantified using recognized phenomic traits. This digitalization can identify phenomic traits which correspond to the percentage of parental descriptors remaining expressed/visible/measurable in the particular interspecific hybrid. In this study, a line of P. vulgaris, P. acutifolius and P. parvifolius accessions and their crosses were sown in the mesh house according to CIAT seed regeneration procedures. Methodology Three accessions and one derived breeding line originating from their interspecific crosses were characterized and classified by selected phenomic descriptors using multivariate and machine learning techniques. The phenomic proportions of the interspecific hybrid (line INB 47) with respect to its three parent accessions were determined using a random forest and a respective confusion matrix. Results The seed and pod morphometric traits, physiological behavior and yield performance were evaluated. In the classification of the accession, the phenomic descriptors with highest prediction force were Fm’, Fo’, Fs’, LTD, Chl, seed area, seed height, seed Major, seed MinFeret, seed Minor, pod AR, pod Feret, pod round, pod solidity, pod area, pod major, pod seed weight and pod weight. Physiological traits measured in the interspecific hybrid present 2.2% similarity with the P. acutifolius and 1% with the P. parvifolius accessions. In addition, in seed morphometric characteristics, the hybrid showed 4.5% similarity with the P. acutifolius accession. Conclusions Here we were able to determine the phenomic proportions of individual parents in their interspecific hybrid accession. After some careful generalization the methodology can be used to: i) verify trait-of-interest transfer from P. acutifolius and P. parvifolius accessions into their hybrids; ii) confirm selected traits as “phenomic markers” which would allow conserving desired physiological traits of exotic parental accessions, without losing key seed characteristics from elite common bean accessions; and iii) propose a quantitative tool that helps genebank curators and breeders to make better-informed decisions based on quantitative analysis.

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Using phenomics to identify and integrate traits of interest for better-performing common beans: A validation study on an interspecific hybrid and its Acutifolii parents

Abstract 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. 

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Population Dynamics and Estimation of Damage of the Spittlebug Aeneolamia varia on Sugarcane in Colombia by Using remote Sensing and Machine Learning Tools

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.

https://www.mdpi.com/2223-7747/10/2/247/pdf

Carlos Enrique Velásquez-Arroyo

Papers

Machine Learning Applications and Optimization of Clustering Methods Improve the Selection of Descriptors in Blackberry Germplasm Banks