Identifying and characterizing yield limiting factors in paddy rice using remote sensing yield maps

Chen, Shou-Hung; Chang, Kuo‐Wei; Shen, Yuan

doi:10.1007/s11119-012-9266-5

Cited by 11 publications

(4 citation statements)

References 38 publications

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“…Yi-Ping Wang et al [20] introduced the system to identify and characterize the limiting the production aspects in paddy areas since the required crop/year season-yield-maps could be obtained as of the satellite images with historical data. Spatio-temporal production-trend maps with inconsistent low, high, and average yields, and also improper yield regions was elucidated centered on variation with respect to time or yield which is normalized on a pixel-by-pixel basis.…”

Section: Review On Different Methodologies For Crop Identification Anmentioning

confidence: 99%

Crop Detection and Classification using Remote Sensing Images

2019

IJITEE

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crop type identification timely and accurately is one of the applications of remote sensing (RS). It assists the people to regulate the variations in the costs of the food grains. RS images are utmost beneficial for agricultural productions. Recent research methodologies focuses mainly on the crops classification using satellite RS image. This paper proffers the survey on crop detection and classification utilizing RS images. This paper also highlights the latest studies regarding the implementation of crop detection and classification techniques like, review on disparate methodologies for crop recognition and classification (different classifiers are used to detect the crop), review on crop conditions monitoring system, and review on identification of yield estimation , crop region, and also crop growth. At last, the performances of the state-of-art methods are contrasted centered on the Kappa coefficient metrics and overall accuracy. Here, accuracy is the notable metric in the crop identification system.

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Section: Review On Different Methodologies For Crop Identification Anmentioning

confidence: 99%

Crop Detection and Classification using Remote Sensing Images

2019

IJITEE

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“…CNN, SVR and other algorithms usually need to set some model parameter values in advance [28]. Because the predefined parameter values may not contain the globally optimal parameter values, the aforementioned model cannot achieve the best effect [29]. In order to overcome the problems of machine learning models in finding the best model parameters, classical optimization algorithms such as the genetic algorithm and the particle swarm optimization algorithm are used to optimize the internal parameters of machine learning models [30].…”

Section: B Problem Statementmentioning

confidence: 99%

Application of Hyperspectral Technology Combined With Bat Algorithm-AdaBoost Model in Field Soil Nutrient Prediction

et al. 2022

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This paper proposes a hyperspectral soil nutrient estimation method based on the bat algorithm (BA)-AdaBoost model. The spectral reflectance, the first derivative of the reflectance, and the reciprocal logarithm of the reflectance are analyzed based on the 800 field soil samples and their hyperspectral data collected. The first derivative of the reciprocal logarithm of the reflectance and the sensitive band was extracted using the correlation coefficient method, and the correlation of the content of soil organic matter, phosphorus, and potassium was solved. The BA is used to optimize the two core parameters of the AdaBoost model (i.e., the maximum number of iterations (n) and the weight reduction coefficient (v) of the weak learner), the classification and regression trees(CART) decision tree is selected as the weak regression learner of the model, and the coefficient of determination is used as parameter optimization. Based on the objective function value, a BA-AdaBoost model was constructed to estimate soil organic matter and phosphorus and potassium contents. The results show that the BA-AdaBoost combined model can better search for globally optimal parameters. The AdaBoost model optimized by BA significantly improved accuracy and reliability. Among the three elements, soil organic matter estimation accuracy is the highest, and the coefficient of determination and the root mean square error are 0.867 and 0.151g • kg -1 , respectively. Compared with the model before optimization, the model accuracy and reliability improved by 29.0% and 24.1%, respectively. The results indicate that hyperspectral technology combined with the BA-AdaBoost model has certain application prospects in field soil nutrient estimation.

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“…A challenge to using the integrated ROSE-YSTTS approach proposed in this study is that many farmers will not have multi-year yield maps, especially in developing countries. In this case, multiple remote sensing images taken during the growing season over the past several years may be used to estimate the spatial and temporal patterns in yield, which can then be used for MZ delineation as demonstrated for cotton [37] and rice [38] .The approach combining high resolution satellite remote sensing images and crop growth model simulations for creating yield maps without the need for any ground measured yield data are especially promising for fields without yield monitoring data [39][40][41] .…”

Section: Integrated Approach To Site-specific Management Zone Delineamentioning

confidence: 99%

An integrated approach to site-specific management zone delineation

Miao

Mulla

Robert

2018

Front. Agr. Sci. Eng.

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Dividing fields into a few relatively homogeneous management zones (MZs) is a practical and costeffective approach to precision agriculture. There are three basic approaches to MZ delineation using soil and/or landscape properties, yield information, and both sources of information. The objective of this study is to propose an integrated approach to delineating site-specific MZ using relative elevation, organic matter, slope, electrical conductivity, yield spatial trend map, and yield temporal stability map (ROSE-YSTTS) and evaluate it against two other approaches using only soil and landscape information (ROSE) or clustering multiple year yield maps (CMYYM). The study was carried out on two no-till corn-soybean rotation fields in eastern Illinois, USA. Two years of nitrogen (N) rate experiments were conducted in Field B to evaluate the delineated MZs for site-specific N management. It was found that in general the ROSE approach was least effective in accounting for crop yield variability (8.0%-9.8%), while the CMYYM approach was least effective in accounting for soil and landscape (8.9%-38.1%), and soil nutrient and pH variability (9.4%-14.5%). The integrated ROSE-YSTTS approach was reasonably effective in accounting for the three sources of variability (38.6%-48.9%, 16.1%-17.3% and 13.2%-18.7% for soil and landscape, nutrient and pH, and yield variability, respectively), being either the best or second best approach. It was also found that the ROSE-YSTTS approach was effective in defining zones with high, medium and low economically optimum N rates. It is concluded that the integrated ROSE-YSTTS approach combining soil, landscape and yield spatial-temporal variability information can overcome the weaknesses of approaches using only soil, landscape or yield information, and is more robust for MZ delineation. It also has the potential for site-specific N management for improved economic returns. More studies are needed to further evaluate their appropriateness for precision N and crop management.

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Identifying and characterizing yield limiting factors in paddy rice using remote sensing yield maps

Cited by 11 publications

References 38 publications

Crop Detection and Classification using Remote Sensing Images

Crop Detection and Classification using Remote Sensing Images

Application of Hyperspectral Technology Combined With Bat Algorithm-AdaBoost Model in Field Soil Nutrient Prediction

An integrated approach to site-specific management zone delineation

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