Rice yield estimation based on K-means clustering with graph-cut segmentation using low-altitude UAV images

Reza, Nasim; Na, In Seop; Baek, Sun Wook; Lee, Kyeong–Hwan

doi:10.1016/j.biosystemseng.2018.09.014

Cited by 118 publications

(68 citation statements)

References 14 publications

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“…Yield estimation of cereals using plant height [137,138], CIs/VIs [18,65,75,81,82,132,138] derived from RGB, multispectral and/or hyperspectral sensors are available in the literature. An image processing method combined with the K-means clustering algorithm with a graph-cut (KCG) algorithm on RGB images collected using a multi-rotor drone is utilized to estimate rice yield [139]. Similarly, NDVI alone is used to estimate rice yield [18].…”

Section: Yield Estimationmentioning

confidence: 99%

A Review on Drone-Based Data Solutions for Cereal Crops

Panday

Pratihast

Aryal

et al. 2020

Drones

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Food security is a longstanding global issue over the last few centuries. Eradicating hunger and all forms of malnutrition by 2030 is still a key challenge. The COVID-19 pandemic has placed additional stress on food production, demand, and supply chain systems; majorly impacting cereal crop producer and importer countries. Short food supply chain based on the production from local farms is less susceptible to travel and export bans and works as a smooth system in the face of these stresses. Local drone-based data solutions can provide an opportunity to address these challenges. This review aims to present a deeper understanding of how the drone-based data solutions can help to combat food insecurity caused due to the pandemic, zoonotic diseases, and other food shocks by enhancing cereal crop productivity of small-scale farming systems in low-income countries. More specifically, the review covers sensing capabilities, promising algorithms, and methods, and added-value of novel machine learning algorithms for local-scale monitoring, biomass and yield estimation, and mapping of them. Finally, we present the opportunities for linking information from citizen science, internet of things (IoT) based on low-cost sensors and drone-based information to satellite data for upscaling crop yield estimation to a larger geographical extent within the Earth Observation umbrella.

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Section: Yield Estimationmentioning

confidence: 99%

A Review on Drone-Based Data Solutions for Cereal Crops

Panday

Pratihast

Aryal

et al. 2020

Drones

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“…One of the most important aspects of GSM method evaluated was its function in prediction of GY and GN. Yield prediction is considered as the main purpose of many remote sensing and digital imagery studies (Alganci et al, 2014;Wang et al, 2014;Pantazi et al, 2016;Chen and Jing, 2017;Chlingaryan et al, 2018;Donohue et al, 2018;Hassan et al, 2018;Lai et al, 2018;Schut et al, 2018;Walter et al, 2018;Reza et al, 2019), due to its priority in crop sciences and practices. Also in the present study, it was expected that GY and its important contributor, GN, would be desirable choices for examining the GSM practical application and assessing the validation of its function.…”

Section: Discussionmentioning

confidence: 99%

Green-gradient based canopy segmentation: A multipurpose image mining model with potential use in crop phenotyping and canopy studies

Haghshenas

Emam

2018

Preprint

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Light extinction is the most fundamental aspect of green canopies. The exponential form of light gradient is extensively evaluated or utilized by conventional approaches mainly in contribution to the vital concept of leaf area index (LAI), which reasonably characterizes canopies based on their theoretical capability for light attenuation i.e. having greater leaf surfaces. We analyzed the image archive of heterogeneous wheat canopies (cultivar mixtures), captured from experimental plots of a two-year field study conducted at College of Agriculture, Shiraz University, Iran, to evaluate the option of using commercial digital cameras for comparing the canopies based on their relative optics, and also providing new representation of light extinction within the canopy. Here, two novel distinct techniques including different imaging and calculation methods were employed. In the first one, or Green-based Segmentation Model (GSM), pixels of vegetation parts were categorized into "n" light-to-darkness groups based on their similar Green values (RGB color system). Then, mean red, mean green, and mean blue values of each group were calculated and their separate trends were plotted against the mentioned green light-to-dark steps. In the second method, a kind of in-situ spectroscopy of sun-exposed leaves (DLBE, Double-shot Light Balance Equations) was evaluated under the isotropic scattering assumption, with the aim of estimating the absorbed portion of light by sun-exposed leaves, using reflected and transmitted portions. Facing the sun, and then with the sun behind the camera, two images not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/241786 doi: bioRxiv preprint first posted online Jan. 1, 2018; 2 were taken from each plot; in which "Transmission" and "Reflection" ROIs (ranges of interest) were extracted, respectively. Finally, normalized color values were calculated using mean RGBs of either type of ROI. Results of GSM showed that the green-scaled gradient of reflected red and blue lights had robust exponential trends which could be readily utilized for identifying canopies in the form of one or two image-derived equation (

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“…From Fig. 2, it can be depicted that EO can provide quite a large number of indicators for the SDG framework such as data on the condition of the atmosphere [49], oceans [50], crops [51], forests [52], climate [53], natural disasters [54], natural resources [55], urbanisation [56], biodiversity [57] and human conditions [58]. The two most important indicators are population distribution (I-1), and cities/infrastructure mapping (I-2) since they contribute to all the SDGs.…”

Section: Overview On Earth Observation For Sustainable Goals Developmentmentioning

confidence: 99%

Monitoring sustainable development by means of earth observation data and machine learning: a review

2020

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This paper presents and explores the different Earth Observation approaches and their contribution to the achievement of United Nations Sustainable Development Goals. A review on the Sustainable Development concept and its goals is presented followed by Earth Observation approaches relevant to this field, giving special attention to the contribution of Machine Learning methods and algorithms as well as their potential and capabilities to support the achievement of Sustainable Development Goals. Overall, it is observed that Earth Observation plays a key role in monitoring the Sustainable Development Goals given its cost-effectiveness pertaining to data acquisition on all scales and information richness. Despite the success of Machine Learning upon Earth Observation data analysis, it is observed that performance is heavily dependent on the ability to extract and synthesise characteristics from data. Hence, a deeper and effective analysis of the available data is required to identify the strongest features and, hence, the key factors pertaining to Sustainable Development. Overall, this research provides a deeper understanding on the relation between Sustainable Development, Earth Observation and Machine Learning, and how these can support the Sustainable Development of countries and the means to find their correlations. In pursuing the Sustainable Development Goals, given the relevance and growing amount of data generated through Earth Observation, it is concluded that there is an increased need for new methods and techniques strongly suggesting the use of new Machine Learning techniques.

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Rice yield estimation based on K-means clustering with graph-cut segmentation using low-altitude UAV images

Cited by 118 publications

References 14 publications

A Review on Drone-Based Data Solutions for Cereal Crops

A Review on Drone-Based Data Solutions for Cereal Crops

Green-gradient based canopy segmentation: A multipurpose image mining model with potential use in crop phenotyping and canopy studies

Monitoring sustainable development by means of earth observation data and machine learning: a review

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