Non-Invasive Sensing of Nitrogen in Plant Using Digital Images and Machine Learning for Brassica Campestris ssp. Chinensis L.

Xiong, Xin; Jing-jin, Zhang; Guo, Doudou; Chang, Liu; Huang, Danfeng

doi:10.3390/s19112448

Cited by 28 publications

(30 citation statements)

References 58 publications

(63 reference statements)

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“…This comparison indicates how appropriate the regression analysis with RF is to predict CNC. This finding was also observed in other related studies [19,[32][33][34].…”

Section: Discussionsupporting

confidence: 90%

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Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

et al. 2019

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The traditional method of measuring nitrogen content in plants is a time-consuming and labor-intensive task. Spectral vegetation indices extracted from unmanned aerial vehicle (UAV) images and machine learning algorithms have been proved effective in assisting nutritional analysis in plants. Still, this analysis has not considered the combination of spectral indices and machine learning algorithms to predict nitrogen in tree-canopy structures. This paper proposes a new framework to infer the nitrogen content in citrus-tree at a canopy-level using spectral vegetation indices processed with the random forest algorithm. A total of 33 spectral indices were estimated from multispectral images acquired with a UAV-based sensor. Leaf samples were gathered from different planting-fields and the leaf nitrogen content (LNC) was measured in the laboratory, and later converted into the canopy nitrogen content (CNC). To evaluate the robustness of the proposed framework, we compared it with other machine learning algorithms. We used 33,600 citrus trees to evaluate the performance of the machine learning models. The random forest algorithm had higher performance in predicting CNC than all models tested, reaching an R2 of 0.90, MAE of 0.341 g·kg−1 and MSE of 0.307 g·kg−1. We demonstrated that our approach is able to reduce the need for chemical analysis of the leaf tissue and optimizes citrus orchard CNC monitoring.

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“…This comparison indicates how appropriate the regression analysis with RF is to predict CNC. This finding was also observed in other related studies [19,[32][33][34].…”

Section: Discussionsupporting

confidence: 90%

“…Regarding the LNC assessment, a study [32] calculated the nitrogen nutrition index (NNI) and evaluated it with machine learning models using RGB images. The authors used a potted pakchoi experiment in a greenhouse and compared the performance of different algorithms in two different stages.…”

Section: Related Workmentioning

confidence: 99%

Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

et al. 2019

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“…As for the image itself, the major limitation of a UAV image data collection is the low capacity to compensate and analyze larger areas. However, this type of aerial remote sensing is important when considering the spatial resolution and highly detailed information obtained on the vegetation cover, permitting an analysis at a plant or crop-plot level [68][69][70][71]. Additionally, by evaluating crop at an aerial view, it is easier to ascertain the relationship between spectral data and biophysical variables, since the end-user can reduce the amount of noise introduced in the system by extracting only pixels corresponding with the canopy itself.…”

Section: Discussionmentioning

confidence: 99%

Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques

et al. 2020

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Under ideal conditions of nitrogen (N), maize (Zea mays L.) can grow to its full potential, reaching maximum plant height (PH). As a rapid and nondestructive approach, the analysis of unmanned aerial vehicles (UAV)-based imagery may be of assistance to estimate N and height. The main objective of this study is to present an approach to predict leaf nitrogen concentration (LNC, g kg−1) and PH (m) with machine learning techniques and UAV-based multispectral imagery in maize plants. An experiment with 11 maize cultivars under two rates of N fertilization was carried during the 2017/2018 and 2018/2019 crop seasons. The spectral vegetation indices (VI) normalized difference vegetation index (NDVI), normalized difference red-edge index (NDRE), green normalized difference vegetation (GNDVI), and the soil adjusted vegetation index (SAVI) were extracted from the images and, in a computational system, used alongside the spectral bands as input parameters for different machine learning models. A randomized 10-fold cross-validation strategy, with a total of 100 replicates, was used to evaluate the performance of 9 supervised machine learning (ML) models using the Pearson’s correlation coefficient (r), mean absolute error (MAE), coefficient of regression (R²), and root mean square error (RMSE) metrics. The results indicated that the random forest (RF) algorithm performed better, with r and RMSE, respectively, of 0.91 and 1.9 g.kg−¹ for LNC, and 0.86 and 0.17 m for PH. It was also demonstrated that VIs contributed more to the algorithm’s performances than individual spectral bands. This study concludes that the RF model is appropriate to predict both agronomic variables in maize and may help farmers to monitor their plants based upon their LNC and PH diagnosis and use this knowledge to improve their production rates in the subsequent seasons.

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“…The images analysis referred to the method of Xiong et al [29] and was conducted by the same company as that used in Xiong et al's study, Nanjing AgriBrain Big Data Technology Co., Ltd. (Nanjing, China). The image processing and phenotypic feature extraction are documented in detail in the report by Xiong et al [29]. The extraction of the color features involved the transformation of RGB (red, green, blue), LAB (L is luminosity, A is the range from magenta to green, and B is the range from yellow to blue), and HSV (hue, saturation, value) color spaces.…”

Section: Image Acquisition and Analysismentioning

confidence: 99%

Evaluating the Waterlogging Tolerance of Wheat Cultivars during the Early Growth Stage Using the Comprehensive Evaluation Value and Digital Image Analysis

et al. 2022

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The accurate and efficient screening of waterlogging-tolerant cultivars is an effective way to mitigate waterlogging damages. An experiment was conducted to evaluate the performance of 28 wheat varieties mainly planted in the middle and lower reaches of the Yangtze River, China, under control and waterlogging conditions. When the 15-day waterlogging that was initiated at the third-leaf stage was completed, the aboveground dry weight, plant height, leaf number on main stem, culm number, leaf area, and SPAD readings of wheat seedlings were significantly decreased by 14%, 11%, 6%, 13%, 14%, and 15% compared with the control treatment (maintaining approximately 80% of field capacity), respectively. The results showed that the percentage reductions in the dry weight and leaf area under stress accurately represented the influence of the majority of the measured agronomic traits and were significantly negatively correlated with the respective dry weight and leaf area of different cultivars under waterlogging. This suggests that dry weight and leaf area can be used as agronomic traits for screening waterlogging-tolerant cultivars. The comprehensive evaluation value of waterlogging tolerance (CEVW) was closely related to the percentage reduction in dry weight, plant height, culm number, leaf area, and SPAD reading. The range of CEVW was 0.187–0.819, indicating a wide variation in the waterlogging tolerance of the wheat cultivars. Comparing the top-view images, the phenotypic texture parameters (dissimilarity, homogeneity, and angular second moment (ASM)) extracted from the side-view images better reflected the dry weight, plant height, and leaf area under different water treatments. The percentage reduction in ASM had the strongest correlation with CEVW (root mean square error = 0.109); thus, the ASM is recommended as a suitable phenotypic parameter to evaluate waterlogging tolerance. The present results provide references for the rapid and intelligent screening of waterlogging-tolerant wheat cultivars, but future studies need to consider the stress evaluation of the adult plants.

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Non-Invasive Sensing of Nitrogen in Plant Using Digital Images and Machine Learning for Brassica Campestris ssp. Chinensis L.

Cited by 28 publications

References 58 publications

Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques

Evaluating the Waterlogging Tolerance of Wheat Cultivars during the Early Growth Stage Using the Comprehensive Evaluation Value and Digital Image Analysis

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