🏞️ Fine-Scale Mapping of Soil Organic Matter in Agricultural Soils Using UAVs and Machine Learning by Jannis Heil, et al. ➡️ https://brnw.ch/21wLcCB
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I had a productive conversation with Igor Tihonov from Solvi AB regarding their three-year study on using drone imagery for cabbage counts and size estimation. Their researchers used Solvi to develop automated, accurate yield projections based solely on images. Methodology Aerial surveys were conducted using the DJI X4S RGB at a 0.5 cm GSD (Ground Sample Distance, where each pixel corresponds to 0.5 cm in real life). Surveys were conducted: 3-4 weeks after planting 11-13 into the season 2-3 weeks preharvest 2000 cabbages were weighed, and their head diameters were manually measured to determine an average cabbage weight per centimeter. Solvi was then used to automate head diameter measurements from drone imagery. Researchers then combined automated diameter measurements with ground truth measurements of cabbage weight per centimeter, enabling the calculation of yield projections using drone imagery. Main Findings Scouting: imagery enabled farmers to identify areas of crop loss during the early growth stage. Counting: Image based crop counting achieved 99.2% accuracy in the early plant growth stage compared to manual counts, demonstrating a potential to reduce labor costs. Additionally, there was a minimal decrease in crop count accuracy for surveys conducted before harvest. Cabbage's visual distinctiveness, which simplifies image processing, may have contributed to this. Categorization: Small cabbages are rejected due to strict buying contracts, while oversized ones may fetch lower prices. Researchers used Solvi to categorize cabbages into three size categories, allowing growers to understand size distribution across their field and increasing the accuracy of automated yield projections. Yield: Comparing image-based yield projections to actual yield at harvest revealed 80-90% accuracy. A possible use-case of this method would allow farmers time to harvest based on head size, allowing for further yield increases. Any ideas on how these techniques could be implemented for other crops? Comment below! I’ll be covering more successful use cases of drones for #precisionagriculture so stay tuned. Feel free to send me any interesting projects you come accross. #AgTech #dronesforgood
Can drone imagery be used to estimate cabbage yield? 🤔 Over the course of 3 years, we have worked closely with growers, researchers and agronomists in a collaborative project with one goal - to assess if drones can be used for yield estimations in cabbage and if so, what the accuracy would be. The result? [Spoiler alert] It turned out pretty well. Early-stage counts provided growers with highly accurate (99.2% accuracy) estimate of early crop loss, while the pre-harvest counts and size-estimations gave growers a projected yield which was not too far from what we hand-measured in the fields. Learn more about the approach we used in our latest article 👇 https://lnkd.in/gDGPimij
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Can drone imagery be used to estimate cabbage yield? 🤔 Over the course of 3 years, we have worked closely with growers, researchers and agronomists in a collaborative project with one goal - to assess if drones can be used for yield estimations in cabbage and if so, what the accuracy would be. The result? [Spoiler alert] It turned out pretty well. Early-stage counts provided growers with highly accurate (99.2% accuracy) estimate of early crop loss, while the pre-harvest counts and size-estimations gave growers a projected yield which was not too far from what we hand-measured in the fields. Learn more about the approach we used in our latest article 👇 https://lnkd.in/gDGPimij
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UAVs are changing the game in agriculture! The University of Missouri Extension's Weed Science Department is leading the charge. They are working with producers and retailers like MFA. Key focus areas include coverage, nozzle technology, and application parameters. UAVs use specialized atomization nozzles, different from traditional ones enhancing nozzle performance and droplet size. Watch the full episode now! https://lnkd.in/gmxjYFn7 Learn more at AgNowTV.com
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Webinar alert! 🎥 Join us and Boris Calvét from MAS Seeds for a webinar next Tuesday, June 4th, to learn how to use drone imagery for more efficient assessment of field trials with focus on plant counts in corn and sunflower crops. We’ll dive into best practices of drone imagery collection for different types of field trial assessments, discuss how to process the data and extract plot-level metrics, learn how breeders at MAS Seeds use drone imagery for field trial assessments and conclude with a few practical examples from their corn and sunflower trials. In this collaborative webinar, presented by MAS Seeds and Solvi, will cover the following topics: 📷 Which drones and sensors to use for field trial assessments 🛫 How to collect drone data to facilitate accurate analytics 📊 Which plot-level metrics can be extracted from the drone imagery 🔬 How MAS Seeds is leveraging drone imagery in their research 🌱 Practical examples from MAS Seed’s corn and sunflower trials 🙋 Q&A session The webinar will be held in English. Mark next Tuesday, June 4th, in your calendar and register on the link below 👇 https://lnkd.in/d8vS_dxV
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#AgricultureMdpi Open for submissions 📢 New #specialissue: “Application of UAVs in Precision Agriculture—2nd Edition” 📝 Guest Editors: Prof. Dr. Jiyu Li, Dr. Jiating Li, and Dr. Suiyuan Shen ⏰ Deadline: 20 July 2024 Read more: https://lnkd.in/gThCACvP #agriculturalUAVs #effectsofoperation #efficiencyofoperation #routeplanning #energy_consumptionmatching #variablespray #driftsuppression #depositionofdroplets
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Can drone imagery be used to estimate cabbage yield? 🤔 Over the course of 3 years, we have worked closely with Swedish growers, researchers and agronomists in a collaborative project with one goal - to assess if drones can be used for yield estimations in cabbage and if so, what the accuracy would be. The result? [Spoiler alert] It turned out pretty well. Early-stage counts provided growers with highly accurate (99.2% accuracy) estimate of early crop loss, while the pre-harvest counts and size-estimations gave growers a projected yield which was not too far from what we hand-measured in the fields. Learn more about the approach we used in our latest article 👇 https://lnkd.in/gbGGj7ei
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Director - Technology & Solutions. Co-founder of Birdscale Technology. Expert in Drones and Precision Agriculture. Sustainability Enthusiast | Techno Consultant #DroneInnovation
Exploring Precision Agriculture: 1min read. Here is a small use-case for weed detection! 1. 🚀 Utilizing high-precision drones equipped with multispectral cameras to meticulously capture field imagery. 2. 🤖 Employing sophisticated machine learning software for precise weed detection based on specialized vegetation indices. 3. 🛰️ Leveraging RTK-based GPS for accurate geospatial data, enabling targeted weed elimination through strategic drone spraying. 4. 🌿 Enhancing farming efficiency and minimizing environmental impact, epitomizing precision agriculture principles. For detailed know how, feel free to reach out to me. The Working illustration of detection and ground truthing attached. #PrecisionAg #SmartFarming #AgTechInnovation
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Webinar alert! 🎥 Join me and Boris Calvét from MAS Seeds for a webinar next Tuesday, June 4th, to learn how to use drone imagery for more efficient assessment of field trials with focus on plant counts in corn and sunflower crops. We’ll dive into best practices of drone imagery collection for different types of field trial assessments, discuss how to process the data and extract plot-level metrics, learn how breeders at MAS Seeds use drone imagery for field trial assessments and conclude with a few practical examples from their corn and sunflower trials. We'll cover: 📷 Which drones and sensors to use for field trial assessments 🛫 How to collect drone data to facilitate accurate analytics 📊 Which plot-level metrics can be extracted from the drone imagery 🔬 How MAS Seeds is leveraging drone imagery in their research 🌱 Practical examples from MAS Seed’s corn and sunflower trials 🙋 Q&A session The webinar will be held in English. Mark next Tuesday, June 4th, in your calendar and register on the link below in the comments. 👇
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Crop Counting and Detection Using Yolo¶ with UAVs. In this example, we will use Yolo v8 for counting and detecting crops at emergence time. This is important for breeding programs, where we use AI to gain time and accuracy, instead of laborious manual traits estimation, which is expensive and error prone. This is an example running with less than 100 images using yolo8nano. (https://lnkd.in/ewMbNYki). steps are 1. Create a virtual environment 2. install labelme package for annotation (https://lnkd.in/eyY79Cds) 3. install ultralytics package for easy use of yolo (https://lnkd.in/ez-Y2dw9) 4. take video footage and generate sample images for annotation with labelme 5 . Train Yolo on your custom dataset extract the weights and run Inferecence #deeplearning #objectdetection #yolov8 #dronetechnology
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Good Job Expert Dr. Mbaye Modou
Crop Counting and Detection Using Yolo¶ with UAVs. In this example, we will use Yolo v8 for counting and detecting crops at emergence time. This is important for breeding programs, where we use AI to gain time and accuracy, instead of laborious manual traits estimation, which is expensive and error prone. This is an example running with less than 100 images using yolo8nano. (https://lnkd.in/ewMbNYki). steps are 1. Create a virtual environment 2. install labelme package for annotation (https://lnkd.in/eyY79Cds) 3. install ultralytics package for easy use of yolo (https://lnkd.in/ez-Y2dw9) 4. take video footage and generate sample images for annotation with labelme 5 . Train Yolo on your custom dataset extract the weights and run Inferecence #deeplearning #objectdetection #yolov8 #dronetechnology
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