Laboratory for Bioproduction and Machinery 


Comparative Analysis of Remote Sensing via Drone and On-the-go Soil Sensing via Veris U3: A Dynamic Approach

The primary objective of this study is to investigate the strengths and limitations of drone-based remote sensing and on-the-go Veris U3 sensor in agricultural contexts. Through a series of field trials, data encompassing various parameters including vegeta-tion index and soil properties were collected in parallel using both methodologies. As the same fertilizers have been consistently applied year after year, there arose a critical need to evaluate their ongoing necessity and effectiveness. To address this, the Veris soil sensor was deployed as a valuable tool in this study. The focus on utilizing Veris sensor and in-tegrating its data with other sources like NDVI from drones will contribute to under-standing of soil-plant interactions and enable data-driven decisions that can enhance crop productivity and sustainability.


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NDVI analysis distributed by grid 1x1m, July 2023, Muramatsu, Japan


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Soil EC data analysis interpolation by the Kriging method, surveyed before fertilizing, April 2023, Muramatsu, Japan
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Soil EC data analysis interpolation by the Kriging method, surveyed after fertilizing, May 2023, Muramatsu, Japan


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Soil pH data analysis interpolation by the Kriging method, surveyed before fertilizing, April 2023, Muramatsu, Japan
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Soil pH data analysis interpolation by the Kriging method, surveyed after fertilizing, May 2023, Muramatsu, Japan


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Soil IR data analysis interpolation by the Kriging method, surveyed before fertilizing, April 2023, Muramatsu, Japan
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Soil IR data analysis interpolation by the Kriging method, surveyed after fertilizing, May 2023, Muramatsu, Japan
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The relationship between drone-based remote sensing NDVI data and on-the-go ground-based soil sensing EC data before and after fertilization.

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The relationship between drone-based remote sensing NDVI data and on-the-go ground-based soil sensing pH data before and after fertilization.

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The relationship between drone-based remote sensing NDVI data and on-the-go ground-based soil sensing IR data before and after fertilization.


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