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A Simple Trilingual APP for Determining Near-Surface Soil Moisture
https://orcid.org/http://orcid.org/0000-0001-5377-8519
The locust has been devastating pest, which destructs the crops and pastures. Scientists discover the breeding habitats of locust based on soil surface water content (SWC) to devise preventive measures. Hence, accurate interpretation of SWC is vital to safeguard economic livelihood and ensure food security. Researchers usually adapt satellite data and manual/automatic colour-based image processing techniques to interpret SWC. However, satellites could not capture high-resolution images and ground information in densely vegetated areas. In addition, many of the ground surveying teams/farmers could not conduct colour analysis due to the lack of knowledge. Therefore, this manuscript introduces a newly developed web app to overcome the limitations of previously developed techniques. The steps involved in developing the new app were demonstrated to avoid the manual image analysis. Four series of experiments were conducted to quantify the moisture content using the newly developed app. The moisture content was also quantified using conventional manual image analysis technique to validate the newly developed app. The difference between the moisture contents obtained from the above-mentioned methods was found to be 1%–3%. This shows that the newly developed app has potential to identify the locust breeding habitats and guide the ground surveying teams to prevent locust swarm formation.
A Simple Trilingual APP for Determining Near-Surface Soil Moisture
https://orcid.org/http://orcid.org/0000-0001-5377-8519
The locust has been devastating pest, which destructs the crops and pastures. Scientists discover the breeding habitats of locust based on soil surface water content (SWC) to devise preventive measures. Hence, accurate interpretation of SWC is vital to safeguard economic livelihood and ensure food security. Researchers usually adapt satellite data and manual/automatic colour-based image processing techniques to interpret SWC. However, satellites could not capture high-resolution images and ground information in densely vegetated areas. In addition, many of the ground surveying teams/farmers could not conduct colour analysis due to the lack of knowledge. Therefore, this manuscript introduces a newly developed web app to overcome the limitations of previously developed techniques. The steps involved in developing the new app were demonstrated to avoid the manual image analysis. Four series of experiments were conducted to quantify the moisture content using the newly developed app. The moisture content was also quantified using conventional manual image analysis technique to validate the newly developed app. The difference between the moisture contents obtained from the above-mentioned methods was found to be 1%–3%. This shows that the newly developed app has potential to identify the locust breeding habitats and guide the ground surveying teams to prevent locust swarm formation.
A Simple Trilingual APP for Determining Near-Surface Soil Moisture
https://orcid.org/http://orcid.org/0000-0001-5377-8519
The locust has been devastating pest, which destructs the crops and pastures. Scientists discover the breeding habitats of locust based on soil surface water content (SWC) to devise preventive measures. Hence, accurate interpretation of SWC is vital to safeguard economic livelihood and ensure food security. Researchers usually adapt satellite data and manual/automatic colour-based image processing techniques to interpret SWC. However, satellites could not capture high-resolution images and ground information in densely vegetated areas. In addition, many of the ground surveying teams/farmers could not conduct colour analysis due to the lack of knowledge. Therefore, this manuscript introduces a newly developed web app to overcome the limitations of previously developed techniques. The steps involved in developing the new app were demonstrated to avoid the manual image analysis. Four series of experiments were conducted to quantify the moisture content using the newly developed app. The moisture content was also quantified using conventional manual image analysis technique to validate the newly developed app. The difference between the moisture contents obtained from the above-mentioned methods was found to be 1%–3%. This shows that the newly developed app has potential to identify the locust breeding habitats and guide the ground surveying teams to prevent locust swarm formation.
A Simple Trilingual APP for Determining Near-Surface Soil Moisture
Indian Geotech J
Kalra, Kanishk (author) / Gadi, Vinay Kumar (author) / Alybaev, Dastan (author) / Garg, Ankit (author) / Sreedeep, S. (author) / Sahoo, Lingaraj (author)
Indian Geotechnical Journal ; 51 ; 870-875
2021-08-01
6 pages
Article (Journal)
Electronic Resource
English
Trilingual Dictionary for materials and structures
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Soil moisture-vegetation interaction from near-global in-situ soil moisture measurements
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Bibliography on methods for determining soil moisture
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