Growth and yield monitoring of potato crop using Sentinel-1 data through cloud computing

Singha, Chiranjit; Swain, Kishore Chandra; Jayasuriya, H. P. W.

doi:10.1007/s12517-022-10844-6

Cited by 10 publications

(2 citation statements)

References 45 publications

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“…Unlike the approaches used for other crops examined in this review, most potato studies relied on a single VI, typically NDVI or NDYI. In general, VIs have been employed by researchers to differentiate potatoes from other crops [44,54], to predict yields [89,90] and to evaluate crop health and growth [90][91][92][93].…”

Section: Potato Cropmentioning

confidence: 99%

“…Aiming to explore the potential of Light-Weight Multispectral UAV Sensors and their ability to detect potato diseases, the researchers demonstrated that potato blight can be successfully detected at an early stage of infestation by using NDVI. A reference study was also conducted by Singha et al [93], who harnessed satellite data from Sentinel 1 and 2 to track the potato crop's growth cycle. Their NDVI analysis indicated the utility of satellite-based monitoring in offering valuable insights for both farmers and researchers, facilitating informed decision-making throughout the development of the potato crop.…”

Section: Potato Cropmentioning

confidence: 99%

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Using Remote Sensing Vegetation Indices for the Discrimination and Monitoring of Agricultural Crops: A Critical Review

Vidican,

Mălinaș,

Ranta

et al. 2023

Agronomy

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The agricultural sector is currently confronting multifaceted challenges such as an increased food demand, slow adoption of sustainable farming, a need for climate-resilient food systems, resource inequity, and the protection of small-scale farmers’ practices. These issues are integral to food security and environmental health. Remote sensing technologies can assist precision agriculture in effectively addressing these complex problems by providing farmers with high-resolution lenses. The use of vegetation indices (VIs) is an essential component of remote sensing, which combines the variability of spectral reflectance value (derived from remote sensing data) with the growth stage of crops. A wide array of VIs can be used to classify the crops and evaluate their state and health. However, precisely this high number leads to difficulty in selecting the best VI and their combination for specific objectives. Without thorough documentation and analysis of appropriate VIs, users might find it difficult to use remote sensing data or obtain results with very low accuracy. Thus, the objective of this review is to conduct a critical analysis of the existing state of the art on the effective use of VIs for the discrimination and monitoring of several important agricultural crops (wheat, corn, sunflower, soybean, rape, potatoes, and forage crops), grasslands and meadows. This data could be highly useful for all the stakeholders involved in agricultural activities. The current review has shown that VIs appear to be suitable for mapping and monitoring agricultural crops, forage crops, meadows and pastures. Sentinel-1 and Sentinel-2 data were the most utilized sources, while some of the frequently used VIs were EVI, LAI, NDVI, GNDVI, PSRI, and SAVI. In most of the studies, an array of VIs needed to be employed to achieve a good discrimination of crops or prediction of yields. The main challenges in using VIs are related to the variation of the spectral characteristics during the vegetation period and to the similarities of the spectral signatures of various crops and semi-natural meadows. Thus, further studies are needed to establish appropriate models for the use of satellite data that would prove to have greater accuracy and provide more relevant information for the efficient monitoring of agricultural crops.

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Section: Potato Cropmentioning

confidence: 99%

Section: Potato Cropmentioning

confidence: 99%

Using Remote Sensing Vegetation Indices for the Discrimination and Monitoring of Agricultural Crops: A Critical Review

Vidican,

Mălinaș,

Ranta

et al. 2023

Agronomy

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A Systematic Review of Vegetation Indices for Potato Growth Monitoring and Tuber Yield Prediction from Remote Sensing

Mukiibi,

Machakaire,

Franke

et al. 2024

Potato Res.

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Crop intelligence and yield prediction of potato (Solanum tuberosum L.) are important to farmers and the processing industry. Remote sensing can provide timely information on growth status and accurate yield predictions during the growing season. However, there is limited documentation on the most suitable vegetation indices (VIs) and optimal growth stages for acquiring remote sensing imagery of potato. To address this knowledge gap, a systematic review was conducted. Original scientific manuscripts published between 2000 and 2022 were identified using various databases. The findings indicate that satellite imagery is the most widely used source of remote sensing data for tuber yield prediction, whereas unmanned aerial vehicle systems (UAVs) and handheld sensors are more frequently applied for growth monitoring. The normalized difference vegetation index (NDVI), red-edge chlorophyll index (CIred-edge), green chlorophyll index (CIgreen), and optimized soil-adjusted vegetation index (OSAVI) are the most frequently used VIs for the growth and yield estimation of potato. The tuber initiation stage was found to be the most appropriate stage for remote sensing data acquisition. This review will assist potato farmers, agronomists and researchers in selecting the most suitable VIs for monitoring specific growth variables and selecting the optimal timing during the growing season to obtain remote sensing images.

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A review of Best Management Practices for potato crop using Precision Agricultural Technologies

Ahmad¹,

Sharma²

2023

Smart Agricultural Technology

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Growth and yield monitoring of potato crop using Sentinel-1 data through cloud computing

Cited by 10 publications

References 45 publications

Using Remote Sensing Vegetation Indices for the Discrimination and Monitoring of Agricultural Crops: A Critical Review

Using Remote Sensing Vegetation Indices for the Discrimination and Monitoring of Agricultural Crops: A Critical Review

A Systematic Review of Vegetation Indices for Potato Growth Monitoring and Tuber Yield Prediction from Remote Sensing

A review of Best Management Practices for potato crop using Precision Agricultural Technologies

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