A Semi-Automatic Workflow to Extract Irregularly Aligned Plots and Sub-Plots: A Case Study on Lentil Breeding Populations

Plant breeding experiments typically contain a large number of plots, and obtaining phenotypic data is an integral part of most studies. Image-based plot-level measurements may not always produce adequate precision and will require sub-plot measurements. To perform image analysis on individual sub-plots, they must be segmented from plots, other sub-plots, and surrounding soil or vegetation. This study aims to introduce a semi-automatic workflow to segment irregularly aligned plots and sub-plots in breeding populations. Imagery from a replicated lentil diversity panel phenotyping experiment with 324 populations was used for this study. Image-based techniques using a convolution filter on an excess green index (ExG) were used to enhance and highlight plot rows and, thus, locate the plot center. Multi-threshold and watershed segmentation were then combined to separate plants, ground, and sub-plot within plots. Algorithms of local maxima and pixel resizing with surface tension parameters were used to detect the centers of sub-plots. A total of 3489 reference data points was collected on 30 random plots for accuracy assessment. It was found that all plots and sub-plots were successfully extracted with an overall plot extraction accuracy of 92%. Our methodology addressed some common issues related to plot segmentation, such as plot alignment and overlapping canopies in the field experiments. The ability to segment and extract phenometric information at the sub-plot level provides opportunities to improve the precision of image-based phenotypic measurements at field-scale.

Construcción de prototipo de CANSAT para toma de imágenes aéreas para detección de zonas de vegetación en agricultura de precisión

En este artículo se presenta el diseño del CANSAT Heliospectrum[1] desarrollado para su aplicación en percepción remota y análisis de imágenes para competir en el concurso anual realizado por la Sociedad de Sistemas Electrónicos y Aeroespaciales (AESS), capítulo Colombia. El equipo Helios, desarrollador del CANSAT, hace parte del grupo de investigación ASTRA de la Universidad de Antioquia. El diseño se realizó siguiendo las restricciones impuestas por el concurso, en las cuales el picosatélite debía asemejarse a una radiosonda con transmisores, componentes electrónicos, sensores con medición de aceleraciones, presión atmosférica, temperatura, campos magnéticos y aptitud para volar a 1.000 metros de altura (como mínimo), caer libremente y aterrizar con la ayuda de un paracaídas. Así mismo, cumple con los requerimientos necesarios para hacer del CANSAT una plataforma multipropósito con énfasis en percepción remota para aplicación en agricultura de precisión. Se implementó una cámara RGB como carga útil que, con ayuda de un algoritmo de análisis de imágenes que implementa el Excess Green Index (ExG) y el espacio de color Hue, Saturation, Value (HSV), permitió obtener resultados cualitativos de índices de vegetación. El CANSAT está conceptualizado en cinco subsistemas: computadora de vuelo, telemetría, estructura, descenso y recuperación, y potencia. En la aviónica del picosatélite se encuentran componentes principales como el microcontrolador Teensy 3,5, GPS GY-NEO6MV2, IMU GY-89 y en el sistema de recuperación un paracaídas expulsado con un servomotor. El prototipo de CANSAT fue fabricado y probado con éxito por el equipo, resultando ganador de la categoría “Cóndores” de la competencia CANSAT Colombia de la AESS. [1] CANSAT con designación HELI0513 en el concurso CANSAT Colombia 2020.

Early Monitoring of Health Status of Plantation-Grown Eucalyptus pellita at Large Spatial Scale via Visible Spectrum Imaging of Canopy Foliage Using Unmanned Aerial Vehicles

Eucalyptus is a diverse genus from which several species are often deployed for commercial industrial tree plantation due to their desirable wood properties for utilization in both solid wood and fiber products, as well as their growth and productivity in many environments. In this study, a method for monitoring the health status of a 22.78 ha Eucalyptus pellita plantation stand was developed using the red-green-blue channels captured using an unmanned aerial vehicle. The ortho-image was generated, and visual atmospheric resistance index (VARI) indices were developed. Herein, four classification levels of pest and disease were generated using the VARI-green algorithm. The range of normalized VARI-green indices was between −2.0 and 2.0. The results identified seven dead trees (VARI-green index −2 to 0), five trees that were severely infected (VARI-green index 0 to 0.05), 967 trees that were mildly infected (VARI-green index 0.06 to 0.16), and 10,090 trees that were considered healthy (VARI-green index 0.17 to 2.00). The VARI-green indices were verified by manual ground-truthing and by comparison with normalized difference vegetation index which showed a mean correlation of 0.73. This study has shown practical application of aerial survey of a large-scale operational area of industrial tree plantation via low-cost UAV and RGB camera, to analyze VARI-green images in the detection of pest and disease.

Índices vegetais fotogramétricos no milho em sucessão a plantas de cobertura do solo em Areia, Paraíba (Brasil)

Para melhor controle e eficiência dos cultivos agrícolas nas regiões semiáridas brasileiras, o uso de avaliações com dados provenientes de sensoriamento remoto em conjunto com sistemas de informações geográficas (SIG) estão sendo amplamente utilizados. Neste trabalho objetivou-se comparar os índices vegetativos em sistema de cultivo de milho e plantas de cobertura do solo nas condições semiáridas do Brejo da Paraíba. A pesquisa foi realizada em Areia, Paraíba, Brasil, nos anos agrícolas de 2018/2019 e 2019/2020. O delineamento foi em blocos casualizados, com quatro repetições, no esquema fatorial 3 × 6, correspondente a três genótipos de milho (cultivar Robusto, milho crioulo Pontinha e híbrido AG1051) e seis sistemas de cultivo de plantas de cobertura [(Brachiaria ruziziensis, milheto (Pennisetum glaucum), feijão guandu (Cajanus cajan), Crotalária espectabilis, Crotalária juncea e como testemunha apenas o solo descoberto)], totalizando 18 tratamentos. Foram avaliados os índices vegetais de Visible Atmospherically Resistant Index, Redness Index, Normalized green-Red Difference Index, Ground Level Image Analysis, Excess Red-Green, Excess Red Vegetative Index, Excess Green Index e o Color Index of Vegetation Extraction. De maneira geral, os resultados demonstraram que para observar diferenças entre os genótipos de milho é indicado o uso do índice Excess Red Vegetative Index. Para observar diferenças do efeito das plantas de cobertura no milho, indica-se o Normalized green-Red Difference.

Physiological Indicators in Two Lettuce Cultivars Under Saline Stress

Lettuce (Lactuca sativa L.) is considered as the main leafy vegetable in Brazil. In the last decades, there had been many changes in the predominant varietal types in the country, however, issues regarding the use of saline water inhibit the growth by the osmotic effect. The aim of this study is avaliate the effect of water salinity on physiological in lettuce cultivars. The experiment was carried out at the Alagoas Federal University, Arapiraca Campus, in a completely randomized design and with a 5 × 2 factorial scheme, with six replications. Five treatments of water salinity levels were analyzed (ECw: 0.14, 1.54, 2.94, 4.34, and 5.74 dS m-1 at 25 °C) in two types of lettuce crops (Saia Véia and Vitoria Verdinha). Stomatal conductance, net photosynthesis, transpiration rate, water use efficiency, leaf temperature, and green index were assessed at 10, 20, and 30 days after the application of the treatments. The saline stress caused by the increase in saline concentrations decreased the photosynthesis and transpiration rates, which were associated with the reduction of stomatal conductance in both cultivars. Nevertheless, Saia Véia cultivar was higher tolerance in all tested saline levels compared to Vitória Verdinha. The green index for Vitoria Verdinha was seven times higher when compared to Saia Véia from the lowest to the highest saline levels. The cultivars differ in salt sensitivity, which could be useful for producers to choose the cultivar that is most adapted to the region and breeders regarding improvement prospects for adaptation of the lettuce under saline stress. In addition to osmotic stress, which is the first to happen, there are others.

Detecting Biophysical Characteristics and Nitrogen Status of Finger Millet at Hyperspectral and Multispectral Resolutions

Finger millet (Eleusine coracana Gaertn L.) is an important grain crop for small farmers in many countries. Reliable estimates of crop parameters, such as crop growth and nitrogen (N) content, through remote sensing techniques can improve in-season management of finger millet. This study investigated the relationships of hyperspectral reflectance with canopy height, green canopy cover, leaf area index (LAI), and N concentrations of finger millet using an optimal waveband selection procedure with partial least square regression (PLSR). Predictive performance of 13 vegetation indices (VIs) computed from the original hyperspectral data as well as synthesized Landsat-8 and Sentinel-2 data were evaluated and compared for estimating various crop parameters with simple linear regression (SLR) and multilinear regression (MLR) models. The optimal wavebands determined by PLSR were mostly concentrated within 1,000–1,100 nm for both LAI and dry biomass but were scattered for other canopy parameters. The SLR statistics resulted in the simple ratio pigment index (SRPI) and red/green index (RGI) performing best when predicting LAI (R2v = 0.53–0.59) and canopy cover (R2v = 0.72–0.76). The blue/green index (BGI1) was strongly related to canopy height (R2v = 0.65–0.78), dry biomass (R2v = 0.42–0.49), and N concentration (R2v = 0.70–0.83) of finger millet, regardless of spectral resolutions. The MLR approach, using four maximum VIs as input variables, improved the prediction accuracy of N concentration by 14% compared to both SLR and waveband selection methods. VIs computed from synthesized Landsat-8 and Sentinel-2 satellite data resulted in similar or greater prediction accuracy than hyperspectral data for various canopy parameters of finger millet, indicating publicly accessible multispectral data could serve as alternative to hyperspectral data for improved crop management decisions via precision agriculture.

Controlled phase synthesis of VmOn in differing oxidation states using a simplified formic acid process, quantified with a new generalized index designed for use with public domain material process information

This work reports a simplified low-cost environmentally benign synthetic process for the production of pure or nearly pure phase vanadium oxides in three different oxidation states, and quantifies the sustainability of the process with a green index.