Early detection of drought stress in Arabidopsis thaliana utilsing a portable hyperspectral imaging setup

Close-range hyperspectral imaging (HSI) of plants is now a potential tool for non-destructive extraction of plant functional traits. A major motivation is the plant phenotyping related applications where different plant genotypes are explored for different environmental conditions. HSI of Arabidopsis thaliana is of particular importance as it is a model organism in plant biology. In the present work, a portable HSI setup has been used for the monitoring of a set of 6 Arabidopsis thaliana plants. The plants were monitored under controlled watering conditions where 3 plants were watered as normal and the other 3 plants were given 50% of the normal volume of water. The images were pre-processed utilising the standard normal variate (SNV) and changes over time were evaluated using unsupervised clustering over the time series. The results showed an early detection of stress from day 4 onwards compared to the commonly used normalised difference vegetation index (NDVI), which provided detection from day 9

Introduction and development of a practical lesson for improving the competence of Master students in Plant Breeding: The usefulness of specific software in phenotyping tasks

[EN] An essential step in many plant breeding programmes is the morphoagronomic phenotyping of the materials that are being developed, using standardized descriptors or, when not available, as it is the case of new crops, consensus guidelines to characterize them. Although many of the traits must be evaluated in the field, those related to the shape and size of leaves and fruits can be easily measured with specific software using digital pictures. This tool provides several advantages, including: 1) more accurate and objective measurements; 2) the possibility of measuring other traits of interest such as area or perimeter, that in other case would not be easily evaluated; or even 3) the possibility of delaying the analysis of pictures in order to focus on field traits. Therefore, the knowledge and management of this tool can become of high usefulness. The Plant Breeding Master offered by our institute is aimed at training professionals able to improve crops and develop new ones according to market trends, in addition to improve adaptation and resistance to biotic and abiotic stresses. Due to the importance of phenotyping considering any of the above breeding goals, in this paper we propose the introduction of a practical session in the mandatory subject "Instrumental Techniques": the management and comparison of adequacy of two specific software tools considering the material to be analyzed. As material, we propose a collection of pepper varieties for analysis of fruits, and a collection of rocket (Eruca and Diplotaxis spp.) germplasm for analysis of leaves, due to the broad variability observed in those materials. The MorphoLeaf v1.0 software will be used for the analysis of leaves, and the Tomato Analyzer 3.0 software will be used for the analysis of fruits and also for leaves. According to the design of the practical lesson, only one session of three hours is required, in which students will learn to properly digitalize materials considering the restrictions of each software and material used, and to obtain all the information needed in each case. In addition, there will be one hour of autonomous work in which both tools and the results obtained will be compared by the students, and a report will be prepared. This practical lesson allows students to acquire the competence for the correct use of different computer tools in the phenotyping task. The comparison of analyses using different scan adjustments and software will allow students to understand the disadvantages of each option and problems that could occur, in order to minimize them. In summary, this practical lesson gives a tool for improving the future phenotyping works of students during their careers, teaching them to consider the best software prior to analysis in order to improve the digitalizing step according to software restrictions, and to obtain more accurate information with a reduction of working time, thus increasing efficiency.Carla Guijarro-Real is grateful to the Ministerio de Educación, Cultura y Deporte of Spain for a predoctoral FPU grant (FPU14-06798). Pietro Gramazio is grateful to Universitat Politècnica de València for a post-doctoral contract (PAID-10-18) within the Programa de Ayudas de Investigación y Desarrollo initiative. Mariola Plazas is grateful to Generalitat Valenciana and Fondo Social Europeo for a post-doctoral grant (APOSTD/2018/014). Ana M Adalid-Martínez is grateful to the Ministerio de Ciencia, Innovación y Universidad of Spain for its support with a post-doctoral contract (PTA2015- 11502-I) within the Subprograma Personal Técnico de Apoyo initiative.Guijarro-Real, C.; Gramazio, P.; Plazas Ávila, MDLO.; Adalid-Martinez, AM.; Rodríguez Burruezo, A.; Prohens Tomás, J.; Fita, A. (2019). Introduction and development of a practical lesson for improving the competence of Master students in Plant Breeding: The usefulness of specific software in phenotyping tasks. IATED. 5728-5733. https://doi.org/10.21125/inted.2019.1408S5728573

Hyperspectral monitoring of green roof vegetation health state in sub-mediterranean climate: preliminary results

In urban and industrial environments, the constant increase of impermeable surfaces has produced drastic changes in the natural hydrological cycle. Decreasing green areas not only produce negative effects from a hydrological-hydraulic perspective, but also from an energy point of view, modifying the urban microclimate and generating, as shown in the literature, heat islands in our cities. In this context, green infrastructures may represent an environmental compensation action that can be used to re-equilibrate the hydrological and energy balance and reduce the impact of pollutant load on receiving water bodies. To ensure that a green infrastructure will work properly, vegetated areas have to be continuously monitored to verify their health state. This paper presents a ground spectroscopy monitoring survey of a green roof installed at the University of Calabria fulfilled via the acquisition and analysis of hyperspectral data. This study is part of a larger research project financed by European Structural funds aimed at understanding the influence of green roofs on rainwater management and energy consumption for air conditioning in the Mediterranean area. Reflectance values were acquired with a field-portable spectroradiometer that operates in the range of wavelengths 350–2500 nm. The survey was carried out during the time period November 2014–June 2015 and data were acquired weekly. Climatic, thermo-physical, hydrological and hydraulic quantities were acquired as well and related to spectral data. Broadband and narrowband spectral indices, related to chlorophyll content and to chlorophyll–carotenoid ratio, were computed. The two narrowband indices NDVI705 and SIPI turned out to be the most representative indices to detect the plant health status

Detección de características de cultivo a través de sensores ópticos en un sistema orgánico de tipo strip-cropping

Hay un mercado creciente para la agricultura orgánica. Sin embargo, la falta de atención a la biodiversidad y la fertilidad del suelo de las prácticas actuales es un tema urgente. En este contexto, el proyecto SUREVEG analiza el cultivo en franjas en la producción orgánica y su implementación en cultivos intensivos para mejorar la fertilidad del suelo y la biodiversidad en toda Europa. Para contrarrestar la mano de obra adicional de un sistema de cultivos múltiples, se propone una herramienta robótica. Dentro del marco del proyecto, se producirá una versión modular de prueba de concepto (POC) que combinará tecnologías de detección con la actuación en forma de un brazo robótico. Este sistema POC se centrará en las necesidades de fertilización, que se identificarán en tiempo real y se aplicarán en una sola planta. Este artículo se centra en la captación de las características del cultivo mediante nubes de puntos obtenidas con dos lidares y en su procesamiento

Developing New Tools to Determine Plant Spacing for Precise Agrochemical Application

CIGR - AgEng 2016 Aarhus, Denmark 26 - 29 JuneAdvances in the usage of computer imaging, communication technologies and the successful development of new techniques for precision agriculture have facilitated a smart-digital revolution in row crop agriculture in recent years. The use of a yield monitor, variable rate application (VRA) for fertilizer and herbicides, soil property maps and Global Navigation Satellite System (GNSS) technology has enabled the development of computer generated prescription maps for farm management. All these technologies are changing agricultural practices from simple mechanical operations to automated operations implemented by robotic-based systems. The automation of individual crop plant care in vegetable crop fields has increased its practical feasibility and improved efficiency and economic benefit. A systems-based approach is a key feature in the mechanization engineering design via the incorporation of precision sensing techniques. The objective of this study was to design sensing capabilities for implementation to measure plant spacing under different test conditions (California, USA and Andalucía, Spain). Three different optical sensors were used: an optical light curtain transmitter and receiver (880nm), a LiDAR sensor (905 nm), and an RGB camera. An active photoelectric transmission sensor, which contained 3 pairs of optical light curtain transmitters and receivers, evaluated the interruption by the tomato stem of the light curtain between the two devices, and was recorded simultaneously in real-time by a high-speed embedded control system. The LiDAR (model LMS 211 in California and LMS 111 in Spain, from SICK AG) was installed in a vertical orientation in the middle of the mobile platform. Additionally, a RGB spatial mosaicked image was used to adjust the data from the light beam and LiDAR sensor and obtain combined information (RGBD where D is for distance). These sensors were used to properly detect, localize, and discriminate between weed and tomato plants. The use of this detection system may result in a new technique that allows for the automatic control of aggressive weeds and the automation of weeding tools.Ministerio de Economía y Competitividad AGL2013-46343-RJunta de Andalucía P12-AGR-122