Ground Profile Recovery from Aerial 3D LiDAR-based Maps

The paper presents the study and implementation of the ground detection methodology with filtration and removal of forest points from LiDAR-based 3D point cloud using the Cloth Simulation Filtering (CSF) algorithm. The methodology allows to recover a terrestrial relief and create a landscape map of a forestry region. As the proof-of-concept, we provided the outdoor flight experiment, launching a hexacopter under a mixed forestry region with sharp ground changes nearby Innopolis city (Russia), which demonstrated the encouraging results for both ground detection and methodology robustness.Comment: 8 pages, FRUCT-2019 conferenc

COMPARISON OF TWO TYPES OF SENSORS AND THEIR EFFECT ON SPRAY QUALITY PEAR TREES

This study was aimed to reduce the amount of the sprayed solution lost during trees spraying.  At the same time, the concentration of the sprayed solution on the target (tree or bush) must be ensured and to find the best combination of treatments. Two factors controls the spraying process: (i) spraying speed (1.2 km/h, 2.4 km/h, 3.6 km/h), and (ii) the type of sensor. The test results showed a significant loss reduction percentage. It reached (6.05%, 5.39% and 2.05%) at the speed (1.2 km/h, 2.4 km/h, 3.6 km/h), respectively. It was noticed that when the speed becomes higher the loss becomes less accordingly. The interaction between the 3.6 km/h speed and the type of Ultrasonic sensor led to a decrease in the percentage of the spray losses reached to 1.69. For the coverage percentage, the increase in the spraying speed from 1.2 km/h to 2.4 km/h, and then to 3.6 km/h led to a significant decrease in the percentage of coverage (from 17.73% to 13.14%, and then to 11.12%), respectively. The interaction between the type of sensor and the speed has significantly affected the spray density. The speed was 3.6 km/h, and the type of Ultrasonic sensor was superior in obtaining the highest spray density of 83.2 drops/cm2

Ground Profile Recovery from Aerial 3D LiDAR-based Maps

The paper presents the study and implementation of the ground detection methodology with filtration and removal of forest points from LiDAR-based 3D point cloud using the Cloth Simulation Filtering (CSF) algorithm. The methodology allows to recover a terrestrial relief and create a landscape map of a forestry region. As the proof-of-concept, we provided the outdoor ?ight experiment, launching a hexacopter under a mixed forestry region with sharp ground changes nearby Innopolis city (Russia), which demonstrated the encouraging results for both ground detection and methodology robustness

An analysis of the effects of water regime on grapevine canopy status using a UAV and a mobile robot

In this paper, we propose a novel approach for analyzing the effects of water regime on grapevine canopy status using robotics as an aid for monitoring and mapping. Data from an unmanned aerial vehicle (UAV) and a ground mobile robot are used to obtain multispectral images and multiple vegetation indexes, and the 3D reconstruction of the canopy, respectively. Unlike previous works, sixty vegetation indexes are computed precisely by using the projected area of the vineyard point cloud as a mask. Extensive experimental tests on repeated plots of Pinot gris vines show that the GDVI, PVI, and TGI vegetation indexes are positively correlated with the water potential: GDVI (R2=0.90 and 0.57 for the stem and pre-dawn water potential, respectively), PVI (R2=0.90 and 0.57), TGI (R2=0.87 and 0.77). Furthermore, the canopy volume and the canopy area projected on the ground are impacted by the water status, as well as stem and pre-dawn water potential measurements. The results obtained in this work demonstrate the feasibility of the proposed approach and the potential of robotic technologies, supporting precision viticulture

Unmanned aerial vehicle based tree canopy characteristics measurement for precision spray applications

The critical components for applying the correct amount of agrochemicals are fruit tree characteristics such as canopy height, canopy volume, and canopy coverage. An unmanned aerial vehicle (UAV)-based tree canopy characteristics measurement system was developed using image processing approaches. The UAV captured images using a high-resolution red-green-blue (RGB) camera. A digital surface model (DSM) and a digital terrain model (DTM) were generated from the captured images. A tree canopy height map was generated from the subtraction of DSM and DTM. A total of 24 apple trees were randomly targeted to measure the canopy characteristics. Region of interest (ROI) was generated across the boundary of each targeted tree. The height of all pixels within each ROI was computed separately. The pixel with maximum height was considered as the height of the respective tree. For computing canopy volume, the sum of all pixel heights from individual ROI was multiplied by the square of ground sample distance (GSD) of 5.69 mm·pixel−1. A segmentation method was employed to calculate the canopy coverage of the individual trees. The segmented canopy pixel area was divided by the total pixel area within the ROI. The results showed an average relative error of 0.2 m(6.64%) while comparing automatically measured tree height with ground measurements. For tree canopy volume, a mean absolute error of 0.25 m3 and a root mean square error of 0.33 m3 were achieved. The study estimated the possible agrochemical requirement for spraying the fruit trees, ranging from 0.1 to 0.32 l based on tree canopy volumes. The overall investigations suggest that the UAV-based tree canopy characteristics measurements could be a potential tool to calculate the pesticide requirement for precision spraying applications in tree fruit orchards

Validación de un sensor de Ultrasonido para la caracterización de la vegetación en plantaciones de manzano

The EU adapted a set of proposals to make the EU's climate, energy, transport and tax policies adequate to reduce net greenhouse gas emissions, aiming to reduce emissions by 55% by 2030 However, to achieve these reductions, the European GREEN DEAL was launched, which encompasses different areas of action. One of the areas of action of this commission is the action plan "From the Farm to the Table" that covers all phases of the food chain and formulates a more sustainable food policy, through this strategy the plan to combat climate change and protect the environment. In such a way that the European Union promoted the HORIZON 2020 program where various projects such as INNOSETA and OPTIMA were created with the aim of developing, innovating and implementing new phytosanitary application techniques, therefore, one of the most important concepts is the characterization of the vegetation, before carrying out any application of phytosanitary products that are often disproportionate, and therefore it is essential to ensure correct dosage. In this way, the technology regarding the sensors helps us to be able to characterize the vegetation. In this sense, the objective of this work has been the validation of an ultrasound sensor for the Characterization of Vegetation in apple plantations. For this, 6 ultrasound sensors with 3 types of Narrow configurations were used; narrow cone, Medium with average cone and Wide with wide cone. The sensors have been placed on a mast at different heights and mounted on a Qi 9.0 Inverter Sprayer (FEDE) with H30 Technology. The tests were carried out in both artificial and real vegetation, simulating different densities to see the response of the signal from the sensors. The results showed that it is reliable to use these sensors and the proposed configuration to estimate the width of vegetation in apple trees at three heights. Furthermore, it was found that the configuration of the sensors is less important when working with very dense vegetations, but that in sparse vegetations (very early phenological stages) it is advisable to work with beam configurations between medium and wide. A methodology has also been developed to estimate the number of holes in the vegetation from the signal of the ultrasound sensors.La UE va adaptar un conjunt de propostes per fer que les polítiques de clima, energia, transport i impostos de la UE, siguin adequades per reduir les emissions netes de gasos d'efecte hivernacle, tenint com a objectiu reduir les emissions en un 55% pel 2030, però, per aconseguir aquestes reduccions, es va posar en marxa el GREEN DEAL europeu que s'embarca diferents àmbits d'actuació. Un dels àmbits d'actuació d'aquesta comissió és el pla d'acció "De la Graja a la Taula" que abasta totes les fases de la cadena d'alimentació i formula una política alimentària més sostenible, a través d'aquesta estratègia es reforcés el pla per combatre amb el canvi climàtic i protegir el medi ambient. De tal manera que la Unió Europea impuls al programa HORITZÓ 2020 on es va crear diversos projectes com INNOSETA i OPTIMA amb l'objectiu de desenvolupar, innovar i implementar noves tècniques d'aplicació fitosanitàries, per això, una dels conceptes més importants és la caracterització de la vegetació, abans de realitzar qualsevol aplicació de productes fitosanitaris que sovint són desproporcionades, i per això és imprescindible per assegurar una correcta dosificació. D'aquesta manera, la tecnologia pel que fa als sensors ens ajuda a poder caracteritzar la vegetació. En aquest sentit l'objectiu d'aquest treball ha estat la validació d'un sensor d'ultrasons per a la Caracterizacion de la Vegetació en plantacions de pomeres, per això es van utilitzar 6 sensors d'ultrasons amb 3 tipus de configuracions Narrow; de con estret, Medium de con mitjana i Wide de con ample. Els sensors s'han col·locat en un pal a diferents altures i es van muntar en un Polvoritzador Inverter Qi 9.0 (FEDE) amb Tecnologia H30. Els assajos es van realitzar en vegetació tant artificial com a real, simulant diferents densitats per veure la resposta del senyal dels sensors. Els resultats van mostrar que és fiable utilitzar aquests sensors i la configuració proposta per estimar l'amplada de vegetació en pomera a tres altures. A més, es va veure que la configuració dels sensors és menys important quan es treballa amb vegetacions molt denses, però que en vegetacions poc denses (estadis fenològics molt inicials) és recomanable treballar amb configuracions de feix entre mig i ample. També s'ha desenvolupat una metodologia per a l'estimació de l'nombre de buits en la vegetació a partir del senyal dels sensors d'ultrasons.La UE adapto un conjunto de propuestas para hacer que las políticas de clima, energía, transporte e impuestos de la UE, sean adecuadas para reducir las emisiones netas de gases de efecto invernadero, teniendo como objetivo reducir las emisiones en un 55% para el 2030, sin embargo, para lograr estas reducciones, se puso en marcha el GREEN DEAL europeo que embarca distintos ámbitos de actuación. Uno de los ámbitos de actuación de esta comisión es el plan de acción "De la Graja a la Mesa" que abarca todas las fases de la cadena de alimentacion y formula una política alimentaria más sostenible, a través de esta estrategia se reforzara el plan para combatir con el cambio climático y proteger el medio ambiente. De tal forma que la Unión Europea impulso el programa HORIZONTE 2020 donde se creó diversos proyectos como INNOSETA y OPTIMA con el objetivo de desarrollar, innovar e implementar nuevas técnicas de aplicación fitosanitarias, por ello, una de los conceptos más importantes es la caracterización de la vegetación, antes de realizar cualquier aplicación de productos fitosanitarios que con frecuencia son desproporcionadas, y por ello es imprescindible para asegurar una correcta dosificación. De este modo, la tecnología respecto a los sensores nos ayuda a poder caracterizar la vegetación. En tal sentido el objetivo de este trabajo ha sido la validación de un sensor de ultrasonidos para la Caracterizacion de la Vegetación en plantaciones de manzanos, para ello se utilizaron 6 sensores de ultrasonidos con 3 tipos de configuraciones Narrow; de cono estrecho, Medium de cono promedio y Wide de cono ancho. Los sensores se han colocado en un mástil a distintas alturas y se montaron en un Pulverizador Inverter Qi 9.0 (FEDE) con Tecnología H30. Los ensayos se realizaron en vegetación tanto artificial como real, simulando distintas densidades para ver la respuesta de la señal de los sensores. Los resultados mostraron que es fiable utilizar estos sensores y la configuración propuesta para estimar la anchura de vegetación en manzano a tres alturas. Además, se vio que la configuración de los sensores es menos importante cuando se trabaja con vegetaciones muy densas, pero que en vegetaciones poco densas (estadios fenológicos muy iniciales) es recomendable trabajar con configuraciones de haz entre medio y ancho. También se ha desarrollado una metodología para la estimación del número de huecos en la vegetación a partir de la señal de los sensores de ultrasonidos¿Objectius de Desenvolupament Sostenible::12 - Producció i Consum Responsable

Integration of Multiscale Sensing Data for Phenomics Applications

Sensing technologies can be a powerful tool for phenotyping in breeding programs. Plant phenotypes can be assessed non-invasively and repeatedly across the whole population and throughout the plant development period utilizing advanced sensors and remote sensing platforms. In this study, multiscale sensing platforms—satellite, unmanned aerial vehicle (UAV), proximal sensing system, and Internet of Things (IoT) based sensing systems—equipped with sensors such as visible/RGB, multispectral, and hyperspectral systems were utilized for field-based phenomics applications. The applicability of a suitable sensing technology depends on the area of study, specific phenomics application, sensor specification, and data acquisition conditions. Three main phenomics applications were explored: (i) pasture crop health status evaluation, (ii) above-ground biomass quantity and quality evaluation in the field pea, and (iii) evaluating wheat yield potential in winter and spring wheat. The first study demonstrates the reliability of using a high-resolution satellite (ground sampling distance, GSD = 3 m) and UAV imagery for pasture management. The data from multiscale sensing data showed that the grazing density significantly affected pasture biomass (p < 0.05) only in 2019, and the vegetation index (VI) data from the two imagery types were highly correlated (r ≥ 0.78, p < 0.001, 2019). In the second study, the above-ground biomass (AGBM) and biomass quality (12 quality traits) were evaluated using UAV-based RGB and multispectral imaging, and hyperspectral sensing, respectively, in the winter pea breeding program (2019 and 2020 seasons). Three image processing approaches were evaluated for AGBM estimation, where the best results were acquired using the 3D point cloud model at 1.5 alpha shape technique showing high correlation with harvested fresh (r = 0.78–0.81, p < 0.001) and dry (r = 0.70–0.81, p < 0.001) AGBM. Similarly, the selected features from the normalized difference spectral indices and the ratio spectral indices extracted from hyperspectral data with the random forest model provided high predictive accuracy for all 12 biomass quality traits (0.81 < R2 < 0. 93; 0.05 < RMSE (%) < 1.80; 0.03 < MAE (%) < 1.32).In the wheat study, the vegetation indies were highly correlated between satellite (GSD = 0.31 m) and UAV data (0.42 ≤ r ≤ 0.99, p < 0.01) from winter and spring wheat breeding trials (2020 and 2021). The yield prediction using such VIs with the high-resolution satellite imagery (6.26 ≤ RMSE% ≤ 25.49; 5.11 ≤ MAE% ≤ 20.95; 0.17 ≤ r ≤0.78) and UAV imagery (5.53 ≤ RMSE% ≤ 17.20; 4.28 ≤ MAE% ≤ 14.20; 0.43 ≤ r ≤ 0.92) was also high. In addition to these two platforms, an intelligent and compact IoT-based sensor system was developed for independent and automated phenomics applications to measure and monitor plant responses in real-time. The sensor development, improvisation, and implementation encompassed three field seasons (2020, 2021, and 2022 seasons). The developed IoT-based sensor system could be successfully implemented to monitor multiple trials for timely crop management and increased resource efficiency. The system shows a high potential for supporting plant breeding programs for in-field phenotyping applications. All studies demonstrated promising results in monitoring and estimating crop performance and phenotypic traits using multiscale sensing systems