Rice nitrogen nutrition estimation with RGB images and machine learning methods

Shi, Peihua; Wang, Yuan; Xu, Jiucheng; Zhao, Yanling; Yang, Baolin; Yuan, Zhengqi; Sun, Qingyun

doi:10.1016/j.compag.2020.105860

Cited by 67 publications

(38 citation statements)

References 34 publications

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“…For the rapid and accurate evaluation of crop nitrogen status, most recent studies have used chlorophyll meters and spectral remote sensing image technology for monitoring and diagnosis [ 4 – 6 ]. However, these methods generally suffer from unreliable diagnostic results when nitrogen is absorbed as a substance [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Simulation of the critical nitrogen dilution curve in Jiangxi double-cropped rice region based on leaf dry matter weight

Chun

Liu

et al. 2021

PLoS ONE

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In order to investigate the feasibility of using rice critical nitrogen concentration as a nitrogen nutrition diagnosis index, a two-year positioning field gradient experiment using four rice varieties and four nitrogen levels (0, 75, 150, 225 kg·ha–1 for early rice; 0, 90, 180, 270 kg·ha–1 for late rice) was conducted for early and late rice. The critical dilution curves (Nc%) of the double-cropped rice based on leaf dry matter (LDM) were constructed and verified using the field data. Two critical nitrogen dilution curves and nitrogen nutrition indexes (NNI) of rice LDM were constructed for early rice [Nc% = 2.66LDM−0.79, R2 = 0.88, NNI ranged between 0.29–1.74, and the average normalized root mean square error (n-RMSE = 19.35%)] and late rice [Nc% = 7.46LDM−1.42, R2 = 0.91, NNI was between 0.55–1.53, and the average (n-RMSE = 15.14%)]. The relationship between NNI and relative yield was a quadratic polynomial equation and suggested that the optimum nitrogen application rate for early rice was sightly smaller than 150 kg·ha–1, and that for late rice was about 180 kg·ha-1. The developed critical nitrogen concentration dilution curves, based on leaf dry matter, were able to diagnose nitrogen nutrition in the double-cropped rice region.

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Section: Introductionmentioning

confidence: 99%

Simulation of the critical nitrogen dilution curve in Jiangxi double-cropped rice region based on leaf dry matter weight

Chun

Liu

et al. 2021

PLoS ONE

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“…The flowering stage represents the nutrient growth peak of the wheat plant, which can well reflect the maximal photosynthetic capacity and yield potential. The color indices indicate vegetation coverage, stature, and nitrogen concentration [49,50], all of which can be beneficial for grain yield. Therefore, strong correlations were expected at this stage.…”

Section: Relationships Between Grain Yield and Color Indices At A Single Stagementioning

confidence: 99%

Wheat Yield Prediction Based on Unmanned Aerial Vehicles-Collected Red–Green–Blue Imagery

et al. 2021

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Unmanned aerial vehicles-collected (UAVs) digital red–green–blue (RGB) images provided a cost-effective method for precision agriculture applications regarding yield prediction. This study aims to fully explore the potential of UAV-collected RGB images in yield prediction of winter wheat by comparing it to multi-source observations, including thermal, structure, volumetric metrics, and ground-observed leaf area index (LAI) and chlorophyll content under the same level or across different levels of nitrogen fertilization. Color indices are vegetation indices calculated by the vegetation reflectance at visible bands (i.e., red, green, and blue) derived from RGB images. The results showed that some of the color indices collected at the jointing, flowering, and early maturity stages had high correlation (R2 = 0.76–0.93) with wheat grain yield. They gave the highest prediction power (R2 = 0.92–0.93) under four levels of nitrogen fertilization at the flowering stage. In contrast, the other measurements including canopy temperature, volumetric metrics, and ground-observed chlorophyll content showed lower correlation (R2 = 0.52–0.85) to grain yield. In addition, thermal information as well as volumetric metrics generally had little contribution to the improvement of grain yield prediction when combining them with color indices derived from digital images. Especially, LAI had inferior performance to color indices in grain yield prediction within the same level of nitrogen fertilization at the flowering stage (R2 = 0.00–0.40 and R2 = 0.55–0.68), and color indices provided slightly better prediction of yield than LAI at the flowering stage (R2 = 0.93, RMSE = 32.18 g/m2 and R2 = 0.89, RMSE = 39.82 g/m2) under all levels of nitrogen fertilization. This study highlights the capabilities of color indices in wheat yield prediction across genotypes, which also indicates the potential of precision agriculture application using many other flexible, affordable, and easy-to-handle devices such as mobile phones and near surface digital cameras in the future.

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“…Nitrogen is a key component of the chlorophyll that intensifies photosynthesis in crops [4][5][6][7], and plant yield is affected by irregularity in nitrogen content [8][9][10]. The amount of nitrogen required for each crop varies [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Computer-Vision-Based Approach for Nitrogen Content Estimation in Plant Leaves

et al. 2021

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Nitrogen is an essential nutrient element required for optimum crop growth and yield. If a specific amount of nitrogen is not applied to crops, their yield is affected. Estimation of nitrogen level in crops is momentous to decide the nitrogen fertilization in crops. The amount of nitrogen in crops is measured through different techniques, including visual inspection of leaf color and texture and by laboratory analysis of plant leaves. Laboratory analysis-based techniques are more accurate than visual inspection, but they are costly, time-consuming, and require skilled laboratorian and precise equipment. Therefore, computer-based systems are required to estimate the amount of nitrogen in field crops. In this paper, a computer vision-based solution is introduced to solve this problem as well as to help farmers by providing an easier, cheaper, and faster approach for measuring nitrogen deficiency in crops. The system takes an image of the crop leaf as input and estimates the amount of nitrogen in it. The image is captured by placing the leaf on a specially designed slate that contains the reference green and yellow colors for that crop. The proposed algorithm automatically extracts the leaf from the image and computes its color similarity with the reference colors. In particular, we define a green color value (GCV) index from this analysis, which serves as a nitrogen indicator. We also present an evaluation of different color distance models to find a model able to accurately capture the color differences. The performance of the proposed system is evaluated on a Spinacia oleracea dataset. The results of the proposed system and laboratory analysis are highly correlated, which shows the effectiveness of the proposed system.

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Rice nitrogen nutrition estimation with RGB images and machine learning methods

Cited by 67 publications

References 34 publications

Simulation of the critical nitrogen dilution curve in Jiangxi double-cropped rice region based on leaf dry matter weight

Simulation of the critical nitrogen dilution curve in Jiangxi double-cropped rice region based on leaf dry matter weight

Wheat Yield Prediction Based on Unmanned Aerial Vehicles-Collected Red–Green–Blue Imagery

A Computer-Vision-Based Approach for Nitrogen Content Estimation in Plant Leaves

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