scholarly journals Precision Agriculture Workflow, from Data Collection to Data Management Using FOSS Tools: An Application in Northern Italy Vineyard

2021 ◽  
Vol 10 (4) ◽  
pp. 236
Author(s):  
Elena Belcore ◽  
Stefano Angeli ◽  
Elisabetta Colucci ◽  
Maria Angela Musci ◽  
Irene Aicardi
Keyword(s):  
Data Structure ◽  
Optical Sensors ◽  
Spatial Data ◽  
Precision Agriculture ◽  
New Technologies ◽  
Software Tools ◽  
Northern Italy ◽  
Data Generation ◽  
Individual Tree ◽  
Data Infrastructure

In the past decades, technology-based agriculture, also known as Precision Agriculture (PA) or smart farming, has grown, developing new technologies and innovative tools to manage data for the whole agricultural processes. In this framework, geographic information, and spatial data and tools such as UAVs (Unmanned Aerial Vehicles) and multispectral optical sensors play a crucial role in the geomatics as support techniques. PA needs software to store and process spatial data and the Free and Open Software System (FOSS) community kept pace with PA’s needs: several FOSS software tools have been developed for data gathering, analysis, and restitution. The adoption of FOSS solutions, WebGIS platforms, open databases, and spatial data infrastructure to process and store spatial and nonspatial acquired data helps to share information among different actors with user-friendly solutions. Nevertheless, a comprehensive open-source platform that, besides processing UAV data, allows directly storing, visualising, sharing, and querying the final results and the related information does not exist. Indeed, today, the PA’s data elaboration and management with a FOSS approach still require several different software tools. Moreover, although some commercial solutions presented platforms to support management in PA activities, none of these present a complete workflow including data from acquisition phase to processed and stored information. In this scenario, the paper aims to provide UAV and PA users with a FOSS-replicable methodology that can fit farming activities’ operational and management needs. Therefore, this work focuses on developing a totally FOSS workflow to visualise, process, analyse, and manage PA data. In detail, a multidisciplinary approach is adopted for creating an operative web-sharing tool able to manage Very High Resolution (VHR) agricultural multispectral-derived information gathered by UAV systems. A vineyard in Northern Italy is used as an example to show the workflow of data generation and the data structure of the web tool. A UAV survey was carried out using a six-band multispectral camera and the data were elaborated through the Structure from Motion (SfM) technique, resulting in 3 cm resolution orthophoto. A supervised classifier identified the phenological stage of under-row weeds and the rows with a 95% overall accuracy. Then, a set of GIS-developed algorithms allowed Individual Tree Detection (ITD) and spectral indices for monitoring the plant-based phytosanitary conditions. A spatial data structure was implemented to gather the data at canopy scale. The last step of the workflow concerned publishing data in an interactive 3D webGIS, allowing users to update the spatial database. The webGIS can be operated from web browsers and desktop GIS. The final result is a shared open platform obtained with nonproprietary software that can store data of different sources and scales.

scholarly journals INTEGRATION BETWEEN SURFACE AND SUBSURFACE SPATIAL OBJECTS FOR DEVELOPING OMAN 3D SDI BASED ON THE CITYGML STANDARD

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 79-84
Author(s):  
K. Al Kalbani ◽  
A. Abdul Rahman
Keyword(s):  
Data Structure ◽  
Spatial Data ◽  
3D Model ◽  
Infrastructure Management ◽  
Data Infrastructure ◽  
3D City Models ◽  
Spatial Objects ◽  
The World ◽  
Geospatial Tools ◽  
City Models

Abstract. The paper investigates the capability to integrate the surface and subsurface 3D spatial objects data structure within the 3D spatial data infrastructure (3D SDI) based on the CityGML standards. In fact, a number of countries around the world have started applying the 3D city models for their planning and infrastructure management. While others are still working toward 3D SDI by using CityGML standards. Moreover, most of these initiatives focus on the surface spatial objects with less interest to model subsurface spatial objects. However, dealing with 3D SDI requires both surface and subsurface spatial objects with clear consideration on the issues and challenges in terms of the data structure. On the other hand, the study has used geospatial tools and databases such as FME, PostgreSQL-PostGIS, and 3D City Database to generate the 3D model and to test the capability for integrating the surface and subsurface 3D spatial objects data structure within the 3D SDI. This paper concludes by describing a framework that aims to integrate surface and subsurface 3D geospatial objects data structure in Oman SDI. The authors believe that there are possible solutions based on CityGML standards for surface and subsurface 3D spatial objects. Moreover, solving the issues in data structure can establish a better vision and open new avenues for the 3D SDI.

scholarly journals Different Sensor Systems for the Application of Variable Rate Technology in Permanent Crops

2018 ◽  
Vol 12 (3) ◽  
pp. 188-195 ◽  
Author(s):  
Davor Petrović ◽  
Željko Barač
Keyword(s):  
Optical Sensors ◽  
Precision Agriculture ◽  
New Technologies ◽  
Negative Impact ◽  
Low Cost ◽  
Application Rate ◽  
Sensor Systems ◽  
Infrared Sensors ◽  
Ecological Principles

The paper presents a review of different sensory systems for trees’ characterization and detection in permanent crops and the detection of plant health status in crop conditions for the purpose of applying the variable application rate. The use of new technologies enables the use of variable inputs in production with the aim of increasing the economic profit and reducing the negative impact on the environment. World trends increasingly emphasize the use of various sensor systems to achieve precision agriculture and apply the following: ultrasonic sensors for the detection of permanent crops; LIDAR (optical) sensors for treetop detection and characterization; infrared sensors with similar characteristics of optical sensors, but with very low cost prices and N - sensors for variable nitric fertilization. The daily development of sensor systems applied in agricultural production improves the performance and quality of the machines they are installed on. With a more intensive use of sensors in agricultural mechanization, their price becomes more acceptable for widespread use by achieving high quality work with respect to the ecological principles of sustainable production.

Data assimilation of remote sensing data for farm scale maize fertilization in northern Italy

2020 ◽  
Author(s):  
Calogero Schillaci ◽  
Edoardo Tomasoni ◽  
Marco Acutis ◽  
Alessia Perego
Keyword(s):  
Spatial Resolution ◽  
Spatial Data ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Northern Italy ◽  
Yield Data ◽  
Batch Mode ◽  
Area Index ◽  
Sentinel 2

<p>To improve nitrogen fertilization is well known that vegetation indices can offer a picture of the nutritional status of the crop. In this study, field management information (maize sowing and harvesting dates, tillage, fertilization) and estimated vegetation indices VI (Sentinel 2 derived Leaf Area Index LAI, Normalized Difference Vegetation Index NDVI, Fraction of Photosynthetic radiation fPAR) were analysed to develop a batch-mode VIs routine to manage high dimensional temporal and spatial data for Decision Support Systems DSS in precision agriculture, and to optimize the maize N fertilization in the field. The study was carried out in maize (2017-2018) on a farm located in Mantua (northern Italy); the soil is a Vertic Calciustepts with a fine silty texture with moderate content of carbonates. A collection of Sentinel 2 images (with <25% cloud cover) were processed using Graph Processing Tool (GPT). This tool is used through the console to execute Sentinel Application Platform (SNAP) raster data operators in batch-mode. The workflow applied on the Sentinel images consisted in: resampling each band to 10m pixel size, splitting data into subsets according to the farm boundaries using Region of Interest (ROI). Biophysical Operator based on Biophysical Toolbox was used to derive LAI, fPAR for the estimation of maize vegetation indices from emergence until senescence. Yield data were acquired with a volumetric yield sensing in a combine harvester. Fertilization plans were then calculated for each field prior to the side-dressing fertilization. The routine is meant as a user-friendly tool to obtain time series of assimilated VIs of middle and high spatial resolution for field crop fertilization. It also overcomes the failures of the open source graphic user interface of SNAP. For the year 2018, yield data were related to the 34 LAI derived from Sentinel 2a products at 10 m spatial resolution (R<sup>2</sup>=0.42). This result underlined a trend that can be further studied to define a cluster strategy based on soil properties. As a further step, we will test whether spatial differences in assimilated VIs, integrated with yield data, can guide the nitrogen top-dress fertilization in quantitative way more accurately than a single image or a collection of single images.</p>

Environmental Benefits of Precision Agriculture Adoption

2020 ◽  
pp. 637-656 ◽  
Author(s):  
Marco Medici ◽  
Søren Marcus Pedersen ◽  
Giacomo Carli ◽  
Maria Rita Tagliaventi
Keyword(s):  
Environmental Impacts ◽  
Precision Agriculture ◽  
New Technologies ◽  
Environmental Benefits ◽  
Herbicide Application ◽  
Pesticide Application ◽  
Policy Makers ◽  
Application Rates ◽  
Biodiversity Policy ◽  
Lime Application

The purpose of this study is to analyse the environmental benefits of precision agriculture technology adoption obtained from the mitigation of negative environmental impacts of agricultural inputs in modern farming. Our literature review of the environmental benefits related to the adoption of precision agriculture solutions is aimed at raising farmers' and other stakeholders' awareness of the actual environmental impacts from this set of new technologies. Existing studies were categorised according to the environmental impacts of different agricultural activities: nitrogen application, lime application, pesticide application, manure application and herbicide application. Our findings highlighted the effects of the reduction of input application rates and the consequent impacts on climate, soil, water and biodiversity. Policy makers can benefit from the outcomes of this study developing an understanding of the environmental impact of precision agriculture in order to promote and support initiatives aimed at fostering sustainable agriculture.

On the use of environmental maps in creating environmental companty spatial data infrastructure

2016 ◽  
Vol 910 (4) ◽  
pp. 18-25
Author(s):  
S.S. Dyshlyuk ◽  
◽  
O.N. Nikolaeva ◽  
L.A. Romashova ◽  
◽  
...  
Keyword(s):  
Spatial Data ◽  
Spatial Data Infrastructure ◽  
Data Infrastructure

scholarly journals Advances in automatic identification of flying insects using optical sensors and machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Carsten Kirkeby ◽  
Klas Rydhmer ◽  
Samantha M. Cook ◽  
Alfred Strand ◽  
Martin T. Torrance ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Sensors ◽  
Precision Agriculture ◽  
Insect Pests ◽  
Crop Damage ◽  
Automatic Identification ◽  
Flying Insects ◽  
Remote Sensor ◽  
Promising Solution ◽  
Environmentally Sensitive

AbstractWorldwide, farmers use insecticides to prevent crop damage caused by insect pests, while they also rely on insect pollinators to enhance crop yield and other insect as natural enemies of pests. In order to target pesticides to pests only, farmers must know exactly where and when pests and beneficial insects are present in the field. A promising solution to this problem could be optical sensors combined with machine learning. We obtained around 10,000 records of flying insects found in oilseed rape (Brassica napus) crops, using an optical remote sensor and evaluated three different classification methods for the obtained signals, reaching over 80% accuracy. We demonstrate that it is possible to classify insects in flight, making it possible to optimize the application of insecticides in space and time. This will enable a technological leap in precision agriculture, where focus on prudent and environmentally-sensitive use of pesticides is a top priority.

scholarly journals Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards

Agriculture ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 208
Author(s):  
Daniel Queirós da Silva ◽  
André Silva Aguiar ◽  
Filipe Neves dos Santos ◽  
Armando Jorge Sousa ◽  
Danilo Rabino ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Optical Sensors ◽  
Precision Agriculture ◽  
Real Data ◽  
Relevant Information ◽  
Normal Operation ◽  
Area Index ◽  
Sensory Data ◽  
The Right

Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.

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