Predicting Barley Harvest Time in Dryland Conditions Using Satellite Images

Adiban, Reza; Türkoğlu, Aras; Parchami-Araghi, Farzin

doi:10.29133/yyutbd.909711

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…(Kumhálová & Matějková, 2017) in their study relied on satellite images with medium spatial resolution (Landsat satellite data) and very high resolution images (QuickBird and WorldView-2 satellites), with artificial crop sensors (GreenSeeker). Each of (Skakun et al, 2019), (Zhao et al, 2020b), (Řezník et al, 2020) and(ADİBAN et al, 2021) used (Landsat 8) and (Sentinel-2) data to map and estimate wheat and barley yields in conjunction with field-based data. (Mkhabela et al, 2011) and (Qader et al, 2018b) also used low spatial accuracy multi-temporal MODIS satellite data to predict wheat and barley yields through NDVI and EVI.…”

Section: Figure-8mentioning

confidence: 99%

Sentinel‐2 accurately estimated wheat yield in a semi-arid region compared with Landsat 8

Ibrahim

Rasul

Abdullah³

2023

International Journal of Remote Sensing

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Wheat and barley are among the primary food resources of the world population; therefore, their growth and observation are essential in farms to enhance food security worldwide. On top of that, careful observation of the product is essential to find solutions for the issues faced during their production and to reduce the impacts of weather changes. With the advancement of Remote Sensing chnology te , the observation and estimation process has increased. In this study, numbers of spectral vegetation indices was used along with canopy biophysical properties ( LAI ) and biochemical properties (chlorophyll), there calculated from (Landsat 8 and Sentinel-2) satellite data. The wheat and barley samples were collected before were be ready for harvest, and a relation with the vegetarian indices was established using the Multi-Linear Regression module, in which the equations used in predicting the harvest were developed and used to create a graph for expected harvest. The result indicated that there is a strong relationship between the vegetation indices of Sentinel-2 and Landsat images and the actual grain yield with R 2 of 0.77 and 0.71, respectively. The results show that the strongest correlation is observed between the LAI data obtained from Sentinel data and cereal yield data, with an R 2 0.68, and the highest correlation for the indices of Landsat images is observed in the NDWI with R 2 0.59 and the lowest degree of error was in the root mean square error (RMSE) for the Sentinel-2 and Landsat 8 with 0.57 and 1.54. In addition, this study also showed that the least relationship for grain yield prediction was observed between the NDRI for Sentinel-2 (R 2 0.1) and SAVI for Landsat image (R 2 0.47).

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Section: Figure-8mentioning

confidence: 99%

Sentinel‐2 accurately estimated wheat yield in a semi-arid region compared with Landsat 8

Ibrahim

Rasul

Abdullah³

2023

International Journal of Remote Sensing

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“…Hydroponics is a soilless method of cultivating plants, enabling the production of fresh green forage from various crops like maize, barley, oats, and cowpeas (Bakshi et al, 2017). Among these crops, barley grains (Hordeum vulgare L.) have long been utilized in animal feed (Adiban et al, 2021) and the malting industry (Madakemohekar et al, 2018). Barely sprouts provide animals with essential nutrients, including proteins, carbohydrates, minerals, vitamins, and water.…”

Section: Introductionmentioning

confidence: 99%

Developing Sterilization and Lighting Systems for Sprouting Rooms Using Ozone and Optical Fibers

ALİ IBRAHİM AL-RAJHİ,

SHAWKY EL-SAYED

2023

Yüzüncü Yıl Üniversitesi Tarım Bilimleri Dergisi

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The increasing population has led to the widespread adoption of hydroponics. Hydroponic production of fresh green forage requires minimal space, does not use soil, and allows for rapid harvesting. A fully controlled sprouting room can yield a substantial amount of green fodder from a small area with less water consumption. This study aims to investigate the effectiveness of ozone on seed germination, seedling growth, and microbial sterilization during germinated barley processing. Additionally, the sterilization of the barley sprouting room was conducted using ultraviolet and infrared light, which provides optimal sprouting conditions.The study comprises three experimental variables: three levels of ozonized water (13, 26, and 39 mg L-1) combined with three light sources (fluorescent, infrared, and ultraviolet) and three light duration times (8, 16, and 24 h). The measurements include shoot length, fresh yield weight, dry yield weight, conversion factor, chlorophyll content, N, P, K, crude protein, ash, and log reduction.The results indicated that the maximum values were observed when using ozonized water at 39 mg L-1, Ultraviolet LED as a light source, and a sterilizing medium with a light duration time of 24 h. Conversely, the minimum values were observed when using ozonized water at 13 mg L-1, fluorescent LEDs as a light source, and a light duration time of 8 h. Based on the findings, it is highly recommended to utilize the developed sprouting room throughout the year for the production of fresh forage.

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Sentinel‐2 Accurately Estimated Wheat Yield in a Semi-arid Region Compared With Landsat‐8

Ibrahim¹,

Rasul²,

Abdullah³

2022

Preprint

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Wheat and barley are among the primary food resources of the world population; therefore, their growth and observation are essential in farms to enhance food security worldwide. On top of that, careful observation of the product is essential to find solutions for the issues faced during their production and to reduce the impacts of weather changes. With the advancement of Remote Sensing technology, the observation and estimation process has increased. In this study, numbers of spectral vegetation indices was used along with canopy biophysical properties ( LAI ) and biochemical properties (chlorophyll), there calculated from (Landsat 8 and Sentinel-2) satellite data. The wheat and barley samples were collected before were be ready for harvest, and a relation with the vegetarian indices was established using the Multi-Linear Regression module, in which the equations used in predicting the harvest were developed and used to create a graph for expected harvest. The result indicated that there is a strong relationship between the vegetation indices of Sentinel-2 and Landsat images and the actual grain yield with R2 of 0.77 and 0.71, respectively. The results show that the strongest correlation is observed between the LAI data obtained from Sentinel data and cereal yield data, with an R2 0.68, and the highest correlation for the indices of Landsat images is observed in the NDWI with R2 0.59 and the lowest degree of error was in the root mean square error (RMSE) for the Sentinel-2 and Landsat 8 with 0.57 and 1.54. In addition, this study also showed that the least relationship for grain yield prediction was observed between the NDRI for Sentinel-2 (R2 0.1) and SAVI for Landsat image (R2 0.47).

show abstract

Predicting Barley Harvest Time in Dryland Conditions Using Satellite Images

Cited by 3 publications

References 17 publications

Sentinel‐2 accurately estimated wheat yield in a semi-arid region compared with Landsat 8

Sentinel‐2 accurately estimated wheat yield in a semi-arid region compared with Landsat 8

Developing Sterilization and Lighting Systems for Sprouting Rooms Using Ozone and Optical Fibers

Sentinel‐2 Accurately Estimated Wheat Yield in a Semi-arid Region Compared With Landsat‐8

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