Automated color prediction of paddy crop leaf using image processing

Singh, Amandeep; Singh, Maninder

doi:10.1109/tiar.2015.7358526

Cited by 24 publications

(12 citation statements)

References 9 publications

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“…Hocaoglu et al proposed a two‐dimensional representation model of hourly solar radiation data. The approach provided a compact and unique visualization of data that led to accurate forecasting using image processing methods . Using the 2‐D representation of data, an image model was formed in raster scan where rows and columns represented days and hours, respectively.…”

Section: Solar Forecastingmentioning

confidence: 99%

“…The approach provided a compact and unique visualization of data that led to accurate forecasting using image processing methods. 122 Using the 2-D representation of data, an image model was formed in raster scan where rows and columns represented days and hours, respectively. The between-day correlation along the same hour segment provided vertical correlation of the image.…”

Section: Hybrid Modelsmentioning

confidence: 99%

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A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Kakkar

2020

Int J Communication

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Summary For rechargeable wireless sensor nodes, effective power management is of prime importance because of the stochastic behaviour of the environmental resources. A key issue in integrating solar resources with wireless sensor networks (WSNs) is the need of precise irradiance measurements and power to resource modelling. WSNs are employed in an adhoc manner comprises of numerous sensing nodes and organised as a network for the sake of checking and balancing the environmental factors. Each node has sensing, computation, communication, and locomotion capabilities but operates with limited battery life. Energy harvesting is a way of powering these WSNs by harvesting energy from the environment. By considering harvested energy as an energy source, certain considerations are different from that of battery‐operated networks. Nondeterministic energy availability with respect to time is the reason behind these differences, which put a limit on the maximum rate at which energy can be used. Thus, reliable knowledge of solar radiation is essential for informed design, deployment planning, and optimal management of energy in rechargeable WSNs. Further, power management is essential in self‐powerssed networks to efficiently utilize the available energy. In this paper, a detailed survey on different solar forecasting techniques has been presented for precise energy estimates. A detailed study on energy efficient power management techniques is also proposed to address the feasibility of energy‐harvesting approach in WSNs.

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Section: Solar Forecastingmentioning

confidence: 99%

Section: Hybrid Modelsmentioning

confidence: 99%

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Kakkar

2020

Int J Communication

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“…(24) A method of creating digital fruit color charts with shape and color analyses was developed. (25) Singh and Singh (26) presented a method of comparing the crop leaf color with the leaf color chart (LCC), but they did not explain how the color charts were designed. In this paper, we present a framework of CCS for flue-cured tobacco leaves developed by employing an original leaf color from digital color images, which has three characteristics: standardization, objectification, and quantification.…”

Section: Related Workmentioning

confidence: 99%

Color Chart Development by Computer Vision for Flue-cured Tobacco Leaf

Zhi¹,

Gao²,

Liu³

et al. 2018

Sensors and Materials

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“…The farmers need to capture images inside the body shade with proper lighting conditions [5]. Some researchers have used white paper as a background for better segmentation and prepossessing [7], [21]. Accurate segmentation depends on the proper data acquisition process.…”

Section: Introductionmentioning

confidence: 99%

“…Accurate color level prediction of paddy leaf into 4 categories of LCC can be a fruitful way of this research. Automatic prediction of leaf color level by using digital image processing techniques has been proposed by Singh and Singh [21]. Here, test images are compared with database generated LCC value and then the color level is predicted.…”

Section: Introductionmentioning

confidence: 99%

Nitrogen Fertilizer Recommendation for Paddies through Automating the Leaf Color Chart (LCC)

Islam¹,

Rizan²,

Tusher³

et al. 2020

IJACSA

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Nitrogen fertilizer is inevitable for rice production to ensure that the crop's nitrogen need is adequately supplied, during the growing season. International Rice Research Institute (IRRI) has proposed Leaf Color Chart (LCC) to detect the exact nitrogen need of paddy. Farmers generally monitor the plant's growth (which is also an indicator of the nitrogen concentration of leaves) by comparing the leaf color with the corresponding color of the LCC. Currently, in most cases, LCC is used manually to determine the fertilizer need and thus, there is a chance of either overestimating or underestimating the amount of fertilizer. To avoid this problem, a smart fertilizer recommendation system is proposed in this paper. The proposed method is able to automate the manual acquisition and interpretation of leaf color for classification through LCC. The experimentation considers a sample of 6000 Aman paddy leaf images. The data acquisition process was performed according to IRRI's guidance of taking the paddy leaf images within the body shade by our developed application. The data/images have already been made public in Kaggle-a wellknown dataset website. The semantic segmentation of the dataset was performed by a powerful Convolutional Neural Network (CNN) backbone architecture-DeepLabV3+. Color classification into 4 categories of the LCC was performed by CNN architecture which consists of seven layers. Information gain based evaluation was performed in the Decision Tree (DT) approach to select features and with the selected features DT classified images into 4 categories. Color classification by our two proposed methods achieved 94.22% accuracy in CNN Model and 91.22% accuracy in the DT classifier.

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Automated color prediction of paddy crop leaf using image processing

Cited by 24 publications

References 9 publications

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Color Chart Development by Computer Vision for Flue-cured Tobacco Leaf

Nitrogen Fertilizer Recommendation for Paddies through Automating the Leaf Color Chart (LCC)

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