TiHAN-IIT Hyderabad’s Post

Saw a very cool post from Mitch Hinrichs, involving crop circles (not the Aliens!).. ..so based on that, and the curiosity which was sparked on which hardware is used, I created this infographic. It are circles which were part of the #AI #data #labeling campaign with the #Cerberus #crowdsourcing platform*. To build a dataset we asked the crowd to tag harvested #wheat and #maize growing there in the Egypt Behera region, which is in the Nile delta. Images are in false color, telling us something about crop health. Now, I can't really say what the hardware is, there are adjacent #irigation channels and using the #NIR channel, we can spot quite some field deficiencies, which would be fixable involving smart farming for example. Lastly, from a quick 'n rough #determination: each circle has a #radius of 500 meters. More about our work at BlackShore: https://lnkd.in/eTTszwRD *Crowds & Machines with BlackShore, HCSS - The Hague Centre for Strategic Studies and 52impact for #philab (European Space Agency - ESA)

🌱🛰️ Excited to share how Satellite Remote Sensing and GIS Technology revolutionize crop monitoring! 🌾💻 Leveraging satellite imagery and GIS tools, we're able to analyze cropping patterns 🌍, monitor crop health 🌿, and boost production 📈. 🔍 Using Vegetation Indices like NDVI, GNDVI, CVI, and DVI, we gain insights into crop health over time. 🔄 Merging these indices and reclassifying them helps us categorize crop health conditions accurately. 💡 This approach allows us to represent crop health conditions effectively for better decision-making in agriculture. 💪 Join us in exploring the intersection of technology and agriculture! Let's optimize crop yields while preserving resources 🌱 and ensuring food security 🍽️. #Geosastra #gismapping #giscommunity #Aketi #gisjobs #GIS #Valiveti #SatelliteRemoteSensing #CropMonitoring #Agriculture #DataAnalysis #RemoteSensing #Agriculture 🚀

📢 While we await the results of OC2, we're thrilled to showcase one of the standout subprojects from OC1: SAFRA (Sustainable Aerial Forestry Resilience Analytics) by FORCERA 🌳 SAFRA is an AI-driven solution that leverages advanced RGB drone imaging and sensor fusion to assess the health of forests with precision. With SAFRA, you can: ✅ Monitor forest health with real-time geolocation and meteorological integration ✅ Detect early signs of poor vegetation health, drought, and pest impact ✅ Utilize AI-powered analytics and Digital Twin technology for detailed insights All this powered by BC4 technology to bring early detection and resilience to our forests! 🌍🛰️ #AI #ForestryTech #SAFRA #DroneImaging #Innovation #Sustainability #Portugal #FORCERA 🌐 More info and the other subprojects: https://lnkd.in/dcwp8tuc

We are proud of the development we've done towards unlocking the potential of remote sensing for forest management. "Data is food for AI" (-Andrew Ng), and our below-canopy mobile LiDAR technology unlocks 1,000x more data than what has been done for the last hundred years to collect forestry data - hugging trees with tape measures. With Gaia's platform, every time you come back from collecting more data, a specialized satellite imagery model automatically retrains and improves based on the additional data collected below-canopy. Every time it retrains, it transparently communicates the accuracy of the model based on simple but rigorous statistics and the latest data collected on the area. If you see a lot of potential in what above-canopy forestry models can achieve when they are not starved for data, we would love to talk with you! With our data, not even the sky is the limit. https://lnkd.in/ednkwXZh

I'm happy to say that the new 'time-series' module is now live in plimanshiny. This open-source tool empowers breeders with intuitive interfaces and robust functionalities for analyzing time-series data derived from drone imagery. With plimanshiny, researchers can explore temporal dynamics, identify key trends, and make informed decisions to accelerate the development of superior crop varieties. Here you can see some of the module's functionality. As a motivating example, I analyzed the evolution of NDVI in a time series of 22 drone flights (by Cleber Azevedo). From a simple overview to an in-depth slider-comparison plot, the package will help the scientific community extract meaningful insights in time-series analysis at trial, plot, and individual levels. To get access to the app, install the development version of pliman and plimanshiny with install.packages("pak") pak::pkg_install("TiagoOlivoto/pliman") pak::pkg_install("TiagoOlivoto/plimanshiny") Load one of the packages and run library(plimanshiny) run_app() https://lnkd.in/dumN3KyP #plimanshiny #RemoteSensing #RDevelopment #Shiny #DataAnalysis #GIS #DataScience #ExcitingTimes #StayTuned #pliman #digitalfarming #digitalagronomy #remotesensing #uavs #interactive #visualization #rprogramming #spatialanalysis #rstats #phenotyping #precisionagriculture #imageanalysis #mapview #mapedit #labimages #canopy #geospatial #RemoteSensing #GIS #rspatial #buffer #spatialdata #innovation #orthomosaicimages #digitalagriculture #spatialvariability #plantbreeding #dataanalysis #precisionagriculture 🌟 Stay Tuned for More: Exciting updates, tutorials, and the official launch are on the horizon! Follow #plimanshiny for the latest news. If you have a suggestion or criticism to improve the App, don't hesitate to get in touch with me, and let's together improve the pipeline!

Lately, I have been testing different packages for individual trees extraction from LiDAR data. The results shown in the image were obtained using lidR, R package for airborne LiDAR data manipulation and visualization for Forestry applications. Beside the good segmentation results, I really appreciated the performance of the package, as it is possible to run segmentation algorithms on massive data in few seconds. The results can be used to extract various parameters including the crown radius, the tree height and shape, for further analysis. Package repo: https://lnkd.in/dXTHXUMC #Trees #Forestry #LiDAR #lidR

Sentera has come a long way indeed. I have been hearing of successful proofs of concept in detecting weeds for years. Academic conferences, marketing material, all showing stylized pictures with well placed boxes on top of weeds. I even have been guilty of arrogantly assuming that this is a solved problem in terms of algorithmic development. Only when you face the challenge of scaling and making this concept financially viable do you understand that there can be no solution without a robotic platform that supports rapid data gathering at the right parameters and a low entry cost. #PrecisionAg #AgTech #WeedDetection #WeedScout #Sentera

plimanshiny has now the ability to create profiles for vegetation indices. This tool was designed to enhance data analysis and visualization of vegetation data, making it easier than ever to gain insights from your spatial data. 1) Select your raster data and the indices you wish to analyze. 2) Draw your profile path on the map. 3) Instantly visualize the index profile and download the results for further analysis. In this example, I created the profile of three vegetation indexes using a 3-m spatial resolution orthomosaic from ©2024 Planet Labs PBC (Planet) #plimanshiny #RemoteSensing #RDevelopment #Shiny #DataAnalysis #GIS #DataScience #ExcitingTimes #StayTuned #pliman #digitalfarming #digitalagronomy #remotesensing #uavs #interactive #visualization #rprogramming #spatialanalysis #rstats #phenotyping #precisionagriculture #imageanalysis #mapview #mapedit #labimages #canopy #geospatial #RemoteSensing #GIS #rspatial #buffer #spatialdata #innovation #orthomosaicimages #digitalagriculture #spatialvariability #plantbreeding #dataanalysis #precisionagriculture 🌟 Stay Tuned for More: Exciting updates, tutorials, and the official launch are on the horizon! Follow #plimanshiny for the latest news. If you have a suggestion or criticism to improve the App, don't hesitate to get in touch with me, and let's together improve the pipeline!

Did you know drones can collect information about crops using wavelengths that aren’t visible to the human eye? Farmers can use this spectral information to calculate vegetative indices, which use two or more bands of light to calculate values for each pixel in an image. Various indices can be used to assess relevant plant characteristics, such as chlorophyll or water content. The most commonly used index is the Normalized Difference Vegetation Index (NDVI). NDVI uses red and near-infrared bands to highlight the green of the chlorophyll in healthy plants and can be used to spot problem crops up to two weeks before physical signs of distress appear. A similar measure is the Soil Adjusted Vegetation Index (SAVI). In places where soil quality may vary within the site of interest, this index minimizes soil brightness and emphasizes data from vegetation. Another useful measure is the Normalized Difference Water Index (NDWI), which uses near-infrared and short-wave infrared wavelengths to monitor changes in the water content of leaves. There are so many other indices, including Enhanced Vegetation Index (EVI), Visual Atmospheric Resistance Index (VARI), Normalized Difference Red Edge (NDRE), Leaf Area Index (LAI), and more. Read descriptions of these indices here: https://lnkd.in/eY_A4_dD #dronesforgood #agriculture #farming #precisionagriculture #ndvi #multispectral #vegetationindex #remotesensing Follow me (Kat James) to learn more about #dronesforgood. This month I'm talking about drones for precision agriculture.

FJDynamics GNSS automatic navigation system helps the crops grow.