Hemispherical photography to estimate biophysical variables of cotton

Brandão, Ziany Neiva; Zonta, J. H.

doi:10.1590/1807-1929/agriambi.v20n9p789-794

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…Plant characterization using machine vision by portable imaging and analysis software is the most investigated technique [ 4 ]. RGB cameras have been widely used for phenology monitoring [ 5 ], plant geometric characterization [ 6 ], nitrogen application [ 7 ], yield monitoring [ 8 ] and weed/crop discrimination [ 9 ]. These cameras can acquire images with a high resolution and at a low cost.…”

Section: Introductionmentioning

confidence: 99%

Influence of Wind Speed on RGB-D Images in Tree Plantations

Andújar

Dorado

Bengochea-Guevara

et al. 2017

Sensors

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Weather conditions can affect sensors’ readings when sampling outdoors. Although sensors are usually set up covering a wide range of conditions, their operational range must be established. In recent years, depth cameras have been shown as a promising tool for plant phenotyping and other related uses. However, the use of these devices is still challenged by prevailing field conditions. Although the influence of lighting conditions on the performance of these cameras has already been established, the effect of wind is still unknown. This study establishes the associated errors when modeling some tree characteristics at different wind speeds. A system using a Kinect v2 sensor and a custom software was tested from null wind speed up to 10 m·s−1. Two tree species with contrasting architecture, poplars and plums, were used as model plants. The results showed different responses depending on tree species and wind speed. Estimations of Leaf Area (LA) and tree volume were generally more consistent at high wind speeds in plum trees. Poplars were particularly affected by wind speeds higher than 5 m·s−1. On the contrary, height measurements were more consistent for poplars than for plum trees. These results show that the use of depth cameras for tree characterization must take into consideration wind conditions in the field. In general, 5 m·s−1 (18 km·h−1) could be established as a conservative limit for good estimations.

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Section: Introductionmentioning

confidence: 99%

Influence of Wind Speed on RGB-D Images in Tree Plantations

Andújar

Dorado

Bengochea-Guevara

et al. 2017

Sensors

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“…will conduct experiments in more regions to verify the robustness of the models. And a new instrument of the LI-3100C table leaf area meter, (LI-COR Inc., LincoIn, Nebraska, USA) [66] will be used to avoid the impacts of the stems and owers on the output of LAI, and more realistic LAI data will be obtained to improve and validate the accuracy of potato yield estimation by ground-measurement data.…”

Section: Discussionmentioning

confidence: 99%

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Luo

et al. 2020

Preprint

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Background The accurate estimation of potato yield at regional scale is crucial for food security, precision agriculture and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative LAI (rLAI) data to improve the accuracy of potato yield estimation based on weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with single stage and weighted stage was completed. Results The results showed that among the six test rVIs (rNDVI, rCIred edge, rCIgreen, rEVI2, rNDRE and rMTCI), rCIred edge was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

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“…The LAI was measured by the plant canopy Digital Image Analyzer CI-110 (CID USA). The final result is the average value of three measurements in each test area (Brand and Zonta, 2016). The H was measured by hand using a ruler, and the final result is the average of six measurements taken in each test area.…”

Section: Field Data Collectionmentioning

confidence: 99%

Evaluation of important phenotypic parameters of tea plantations using multi-source remote sensing data

Wang²,

Fan³

et al. 2022

Front. Plant Sci.

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Tea height, leaf area index, canopy water content, leaf chlorophyll, and nitrogen concentrations are important phenotypic parameters to reflect the status of tea growth and guide the management of tea plantation. UAV multi-source remote sensing is an emerging technology, which can obtain more abundant multi-source information and enhance dynamic monitoring ability of crops. To monitor the phenotypic parameters of tea canopy more efficiently, we first deploy UAVs equipped with multispectral, thermal infrared, RGB, LiDAR, and tilt photography sensors to acquire phenotypic remote sensing data of tea canopy, and then, we utilize four machine learning algorithms to model the single-source and multi-source data, respectively. The results show that, on the one hand, using multi-source data sets to evaluate H, LAI, W, and LCC can greatly improve the accuracy and robustness of the model. LiDAR + TC data sets are suggested for assessing H, and the SVM model delivers the best estimation (Rp2 = 0.82 and RMSEP = 0.078). LiDAR + TC + MS data sets are suggested for LAI assessment, and the SVM model delivers the best estimation (Rp2 = 0.90 and RMSEP = 0.40). RGB + TM data sets are recommended for evaluating W, and the SVM model delivers the best estimation (Rp2 = 0.62 and RMSEP = 1.80). The MS +RGB data set is suggested for studying LCC, and the RF model offers the best estimation (Rp2 = 0.87 and RMSEP = 1.80). On the other hand, using single-source data sets to evaluate LNC can greatly improve the accuracy and robustness of the model. MS data set is suggested for assessing LNC, and the RF model delivers the best estimation (Rp2 = 0.65 and RMSEP = 0.85). The work revealed an effective technique for obtaining high-throughput tea crown phenotypic information and the best model for the joint analysis of diverse phenotypes, and it has significant importance as a guiding principle for the future use of artificial intelligence in the management of tea plantations.

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Hemispherical photography to estimate biophysical variables of cotton

Cited by 8 publications

References 17 publications

Influence of Wind Speed on RGB-D Images in Tree Plantations

Influence of Wind Speed on RGB-D Images in Tree Plantations

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Evaluation of important phenotypic parameters of tea plantations using multi-source remote sensing data

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