Estimation of Crop Biomass and Leaf Area Index from Multitemporal and Multispectral Imagery Using Machine Learning Approaches

Reisi-Gahrouei, Omid; McNairn, Heather; Hosseini, Mehdi; Homayouni, Saeid

doi:10.1080/07038992.2020.1740584

Cited by 28 publications

(29 citation statements)

References 49 publications

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“…Considerable researches have been conducted to estimate various crop parameters using satellite Earth observations, including RADARSAT-2 [1,17,[19][20][21], RapidEye [5,19,20,22,23], Sentinel-1 [7,17,[24][25][26][27][28], Sentinel-2 [7,25,[29][30][31][32], Landsat-5 Thematic Mapper (TM) [33,34], Landsat-7 Enhanced Thematic Mapper Plus (ETM+) [34][35][36], Landsat-8 Operational Land Imager (OLI) [7,17,31,32,[35][36][37], Worldview-2/3 [17,27,28,38,39], and MODIS [40,41].…”

Section: Introductionmentioning

confidence: 99%

“…Non-parametric MLAs, without any assumption for the statistical distribution of input data, have successfully been applied to remote sensing data to retrieve crop biophysical parameters, yield estimation, and crop mapping. Reisi Gahrouei, et al [22] used an artificial neural network (ANN) and SVR to estimate LAI and dry biomass of three crops, including soybean, corn, and canola high-resolution RapidEye data. Reisi-Gahrouei, et al [44] also used MLR and ANN to estimate crop biomass using UAVSAR data.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Estimation of Crop Biophysical Parameters Using Multi-Source and Multi-Temporal Remote Sensing Observations

et al. 2021

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Remote sensing data are considered as one of the primary data sources for precise agriculture. Several studies have demonstrated the excellent capability of radar and optical imagery for crop mapping and biophysical parameter estimation. This paper aims at modeling the crop biophysical parameters, e.g., Leaf Area Index (LAI) and biomass, using a combination of radar and optical Earth observations. We extracted several radar features from polarimetric Synthetic Aperture Radar (SAR) data and Vegetation Indices (VIs) from optical images to model crops’ LAI and dry biomass. Then, the mutual correlations between these features and Random Forest feature importance were calculated. We considered two scenarios to estimate crop parameters. First, Machine Learning (ML) algorithms, e.g., Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were utilized to estimate two crop biophysical parameters. To this end, crops’ dry biomass and LAI were estimated using three input data; (1) SAR polarimetric features; (2) spectral VIs; (3) integrating both SAR and optical features. Second, a deep artificial neural network was created. These input data were fed to the mentioned algorithms and evaluated using the in-situ measurements. These observations of three cash crops, including soybean, corn, and canola, have been collected over Manitoba, Canada, during the Soil Moisture Active Validation Experimental 2012 (SMAPVEX-12) campaign. The results showed that GB and XGB have great potential in parameter estimation and remarkably improved accuracy. Our results also demonstrated a significant improvement in the dry biomass and LAI estimation compared to the previous studies. For LAI, the validation Root Mean Square Error (RMSE) was reported as 0.557 m2/m2 for canola using GB, and 0.298 m2/m2 for corn using GB, 0.233 m2/m2 for soybean using XGB. RMSE was reported for dry biomass as 26.29 g/m2 for canola utilizing SVR, 57.97 g/m2 for corn using RF, and 5.00 g/m2 for soybean using GB. The results revealed that the deep artificial neural network had a better potential to estimate crop parameters than the ML algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Estimation of Crop Biophysical Parameters Using Multi-Source and Multi-Temporal Remote Sensing Observations

et al. 2021

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“…In the past decades, RS technologies, such as satellite imagery, have been widely used to monitor crop growth. Vegetation indices (VIs) derived from the canopy spectral reflectance are commonly used to estimate crop biomass, LAI, nitrogen content, and yield [6][7][8][9]. Among the many VIs, the normalized difference VI (NDVI), which is calculated from combinations of the red and near-infrared bands, is the most frequently used index and has been shown to have a strong correlation with crop growth indicators.…”

Section: Introductionmentioning

confidence: 99%

Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery

Qiu

Xiang

et al. 2020

Remote Sensing

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The accurate estimation of the key growth indicators of rice is conducive to rice production, and the rapid monitoring of these indicators can be achieved through remote sensing using the commercial RGB cameras of unmanned aerial vehicles (UAVs). However, the method of using UAV RGB images lacks an optimized model to achieve accurate qualifications of rice growth indicators. In this study, we established a correlation between the multi-stage vegetation indices (VIs) extracted from UAV imagery and the leaf dry biomass, leaf area index, and leaf total nitrogen for each growth stage of rice. Then, we used the optimal VI (OVI) method and object-oriented segmentation (OS) method to remove the noncanopy area of the image to improve the estimation accuracy. We selected the OVI and the models with the best correlation for each growth stage to establish a simple estimation model database. The results showed that the OVI and OS methods to remove the noncanopy area can improve the correlation between the key growth indicators and VI of rice. At the tillering stage and early jointing stage, the correlations between leaf dry biomass (LDB) and the Green Leaf Index (GLI) and Red Green Ratio Index (RGRI) were 0.829 and 0.881, respectively; at the early jointing stage and late jointing stage, the coefficient of determination (R2) between the Leaf Area Index (LAI) and Modified Green Red Vegetation Index (MGRVI) was 0.803 and 0.875, respectively; at the early stage and the filling stage, the correlations between the leaf total nitrogen (LTN) and UAV vegetation index and the Excess Red Vegetation Index (ExR) were 0.861 and 0.931, respectively. By using the simple estimation model database established using the UAV-based VI and the measured indicators at different growth stages, the rice growth indicators can be estimated for each stage. The proposed estimation model database for monitoring rice at the different growth stages is helpful for improving the estimation accuracy of the key rice growth indicators and accurately managing rice production.

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“…The emergence of plant phenotyping in recent years has brought new perspectives to horticulture research. By combining advanced sensors and automation technology, more physiological and morphological traits across plant growth and development can be assessed, e.g., daily evapotranspiration 60 , crop biomass and leaf area indices 61 , seed germination 62 , and biophysical and biochemical traits 63 . In particular, it has been shown that many machine learning- and computer vision-based analytic methods improve phenotyping accuracy, reliability, and speed.…”

Section: Discussionmentioning

confidence: 99%

MFCIS: an automatic leaf-based identification pipeline for plant cultivars using deep learning and persistent homology

et al. 2021

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Recognizing plant cultivars reliably and efficiently can benefit plant breeders in terms of property rights protection and innovation of germplasm resources. Although leaf image-based methods have been widely adopted in plant species identification, they seldom have been applied in cultivar identification due to the high similarity of leaves among cultivars. Here, we propose an automatic leaf image-based cultivar identification pipeline called MFCIS (Multi-feature Combined Cultivar Identification System), which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network (CNN). Persistent homology, a multiscale and robust method, was employed to extract the topological signatures of leaf shape, texture, and venation details. A CNN-based algorithm, the Xception network, was fine-tuned for extracting high-level leaf image features. For fruit species, we benchmarked the MFCIS pipeline on a sweet cherry (Prunus avium L.) leaf dataset with >5000 leaf images from 88 varieties or unreleased selections and achieved a mean accuracy of 83.52%. For annual crop species, we applied the MFCIS pipeline to a soybean (Glycine max L. Merr.) leaf dataset with 5000 leaf images of 100 cultivars or elite breeding lines collected at five growth periods. The identification models for each growth period were trained independently, and their results were combined using a score-level fusion strategy. The classification accuracy after score-level fusion was 91.4%, which is much higher than the accuracy when utilizing each growth period independently or mixing all growth periods. To facilitate the adoption of the proposed pipelines, we constructed a user-friendly web service, which is freely available at http://www.mfcis.online.

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Estimation of Crop Biomass and Leaf Area Index from Multitemporal and Multispectral Imagery Using Machine Learning Approaches

Cited by 28 publications

References 49 publications

Deep Learning-Based Estimation of Crop Biophysical Parameters Using Multi-Source and Multi-Temporal Remote Sensing Observations

Deep Learning-Based Estimation of Crop Biophysical Parameters Using Multi-Source and Multi-Temporal Remote Sensing Observations

Qualifications of Rice Growth Indicators Optimized at Different Growth Stages Using Unmanned Aerial Vehicle Digital Imagery

MFCIS: an automatic leaf-based identification pipeline for plant cultivars using deep learning and persistent homology

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