The Use of VARI, GLI, and VIgreen Formulas in Detecting Vegetation In aerial Images

Eng, Lim Soon; Ismail, Rozita; Hashim, Wahidah; Baharum, Aslina

doi:10.14716/ijtech.v10i7.3275

Cited by 34 publications

(21 citation statements)

References 13 publications

(16 reference statements)

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“…By utilizing the formula and substituting the parameters with the spectral images of NIR and green respectively, a geospatial image was obtained with varying colour indices on different parts of the land [18]- [20]. Similar to NDVI, the red and yellow regions shown at Figure 10.…”

Section: 𝐺𝑁𝐷𝑉𝐼 = (𝑁𝐼𝑅 -𝐺𝑅𝐸𝐸𝑁) / (𝑁𝐼𝑅 + 𝐺𝑅𝐸𝐸𝑁)mentioning

confidence: 99%

Soil pH periodic assortment with smart irrigation using aerial triboelectric nanogenerator

Karthikeyan

Kannan

Gunasekaran

et al. 2023

IJEECS

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The paper presents an idea on pre-emptively ascertaining the soil pH value on an agriculture field amalgaming drone for aerial photo and subsequent smart irrigation model with the help of internet of things (IoT). The drone used for the aerial footage (multispectral imaging) consists of specialized cameras with filters that would help in ascertaining vegetation and health of the crops in the agriculture land. The IoT device used in smart irrigation model consists of sensors which accumulate data and execute the commands given in a recurring fashion of delay. Moreover, the use of triboelectric nanogenerator (TENG) would help in feasible energy harvesting for agricultural land use.

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Section: 𝐺𝑁𝐷𝑉𝐼 = (𝑁𝐼𝑅 -𝐺𝑅𝐸𝐸𝑁) / (𝑁𝐼𝑅 + 𝐺𝑅𝐸𝐸𝑁)mentioning

confidence: 99%

Soil pH periodic assortment with smart irrigation using aerial triboelectric nanogenerator

Karthikeyan

Kannan

Gunasekaran

et al. 2023

IJEECS

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“…Indeks VARI (Visible Atmospherically Resistant Index) pada awalnya digunakan untuk memperkirakan fraksi vegetasi dengan sensitivitas minimal terhadap efek atmosfer (Lussem et al, 2018;McKinnon, 2017). Penambahan data blue-band dalam persamaan indeks VARI adalah untuk meminimalisir efek atmosfer (Eng et al, 2019). Metode tersebut dapat mendeteksi perubahan akibat akumulasi biomassa dan sensitif terhadap jumlah klorofil pada daun.…”

Section: Analisis Citra Padiunclassified

Analisis Usia Tanaman Padi Berdasarkan Berbagai Indeks Vegetasi Menggunakan Citra Kamera

Ramadhani¹,

Jalil²

2023

trilogi. j. penelit. ilmu. sos. dan. n.a.

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Sistem pertanian yang ada saat ini hanya mengandalkan pengetahuan para petani dalam mengetahui usia tanaman padi dan masih belum ada teknologi tepat guna yang dapat menganalisis usia tanaman padi. Oleh karena itu, indeks vegetasi yang didapatkan dari citra kamera digital yang tergolong dalam ground-based remote sensing merupakan salah satu metode alternatif yang dapat digunakan dalam menganalisis usia tanaman padi. Penelitian ini bertujuan untuk mengetahui model indeks vegetasi yang paling efektif dalam menganalisis usia tanaman padi. Penelitian ini dilakukan di lahan milik petani di Desa Kebonsari Kecamatan Sumbersari Kabupaten Jember. Sampel citra tanaman padi yang digunakan secara keseluruhan berjumlah sebanyak 400 citra. Analisis uji statistik menggunakan analisis determinasi untuk mencari koefisien determinasi antara variabel bebas dan variabel terikat. Varibel bebas pada penelitian ini yaitu data usia tanaman padi terukur sedangkan varibel terikat adalah indeks vegetasi. Hasil penelitian menunjukkan bahwa indeks vegetasi yang efektif untuk menganalisis usia tanaman padi menggunakan kamera digital yaitu indeks VARI. Selain itu, diperoleh juga bahwa usia tanaman padi varietas Cibogo proporsional terhadap nilai VARI hingga usia tanaman padi mencapai 78 HST. Pada usia tanaman padi 1-78 HST nilai indeks VARI mengalami peningkatan dan kemudian nilai indeks VARI mengalami penurunan hingga usia tanaman padi siap panen. Pola grafik yang didapatkan berlandaskan data hasil penelitian yakni parabolik dengan pendekatan fungsi parabolik y = -0.0002x2 + 0.0292x – 0.8001 dengan koefisien determinasi (R2) sebesar 96.8%.

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“…To reduce the impact of atmospheric conditions, the Visible Atmospherically Resistant Index (VARI) has been developed. The advantage of this solution is analysis in the range of visible light, which enables the use of RGB cameras to monitor the area [18]:…”

Section: Plant Growth Monitoringmentioning

confidence: 99%

Assessment of benefits resulting from the use of computer control systems in precision agriculture

Rząsa

2023

Bulletin of the Polish Academy of Sciences Technical Sciences

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Rising food prices mean that solutions are sought that will reduce the costs of agricultural production. One area of research is precision agriculture. This solution requires a computerized plant growth monitoring system. The paper presents a computer system for monitoring plant growth, developed for the needs of precision agriculture for small agricultural areas. The work contains a description of the monitoring system divided into the key elements of the process. An exemplary method of preparing orthophotomaps of the area was presented. The method of making maps that can be implemented on a personal computer has been described. The paper describes the most frequently used Geologic Indicators and Vegetation Index. A test of determining the coefficients was carried out on exemplary acreage with an area of 5.28 ha. Typical positioning systems for agricultural machines are discussed. The DGPS (Differential Global Positioning System) navigation method was used in the tests, which confirmed that it can be used in precision agriculture for small agricultural areas while maintaining sufficient positioning accuracy. The presented system was tested during one cycle of vegetation of winter barley sown with the no-plowing method. On this basis, an analysis of savings resulting from the reduction of the amount of fertilizers and plant care products used was carried out. The proposed system does not require complicated computer systems. It was designed in such a way that it can be implemented on standard PC equipment cooperating with a short-range drone equipped with a standard RGB (Red Green Blue) camera.

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The Use of VARI, GLI, and VIgreen Formulas in Detecting Vegetation In aerial Images

Cited by 34 publications

References 13 publications

Soil pH periodic assortment with smart irrigation using aerial triboelectric nanogenerator

Soil pH periodic assortment with smart irrigation using aerial triboelectric nanogenerator

Analisis Usia Tanaman Padi Berdasarkan Berbagai Indeks Vegetasi Menggunakan Citra Kamera

Assessment of benefits resulting from the use of computer control systems in precision agriculture

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