Simultaneously Predicting Multiple Plant Traits from Multiple Sensors via Deformable CNN Regression

Raja, P. M. Siva; Olenskyj, Alex; Kamangir, Hamid; Earles, Mason

doi:10.48550/arxiv.2112.03205

Cited by 4 publications

(7 citation statements)

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“…Similar to previous studies, the image interpretation capability of the CNN made it possible to implement the model with a good explanatory power [ 22 , 28 ]. The R 2 and NRMSE values for the leaf area were better than those of the other indices.…”

Section: Resultsmentioning

confidence: 55%

“…In a previous study [ 28 ], Raja et al proposed a CNN-based estimation model using the same dataset as that in this study [ 30 ]. The NMSE value of this study was 0.87, larger than the NMSE values of 0.068 and 0.069 of the MIMO and MISO models to which the DCNN was applied in a previous study.…”

Section: Resultsmentioning

confidence: 99%

“…The model architecture was developed using the TensorFlow 2.5.1 framework and Python 3.8. The model structure consists of a model containing two individual models, unlike the MISO and MIMO models in the previous studies [ 27 , 28 ]. Two-stage architecture might be effective for data with time-series characteristics by connecting the two models in series [ 35 , 36 , 37 , 38 ].…”

Section: Methodsmentioning

confidence: 99%

“…In addition, to numerically compare the performance with a previous study [ 28 ], the NMSE value was calculated using Equation (4):

where gt is the actual growth indices, p is the estimated value, n is the number of images, and m is the number of growth indices.…”

Section: Methodsmentioning

confidence: 99%

“…Several researchers used the public RGB-D dataset of greenhouse lettuces [ 28 , 29 ] published by Hemming et al [ 30 ], whose dataset was built for the online challenge session of the Third Autonomous Greenhouse Challenge. Raja et al developed deep learning models, including multi-input multi-output (MIMO) and MISO models, using a CNN and a deformable CNN (DCNN) to estimate growth indices [ 28 ]. Raja et al reported a normalized mean squared error (NMSE) of 0.068 for MIMO and 0.069 for MISO.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Greenhouse Lettuce Growth Indices Based on a Two-Stage CNN Using RGB-D Images

Gang

Kim

2022

Sensors

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Growth indices can quantify crop productivity and establish optimal environmental, nutritional, and irrigation control strategies. A convolutional neural network (CNN)-based model is presented for estimating various growth indices (i.e., fresh weight, dry weight, height, leaf area, and diameter) of four varieties of greenhouse lettuce using red, green, blue, and depth (RGB-D) data obtained using a stereo camera. Data from an online autonomous greenhouse challenge (Wageningen University, June 2021) were employed in this study. The data were collected using an Intel RealSense D415 camera. The developed model has a two-stage CNN architecture based on ResNet50V2 layers. The developed model provided coefficients of determination from 0.88 to 0.95, with normalized root mean square errors of 6.09%, 6.30%, 7.65%, 7.92%, and 5.62% for fresh weight, dry weight, height, diameter, and leaf area, respectively, on unknown lettuce images. Using red, green, blue (RGB) and depth data employed in the CNN improved the determination accuracy for all five lettuce growth indices due to the ability of the stereo camera to extract height information on lettuce. The average time for processing each lettuce image using the developed CNN model run on a Jetson SUB mini-PC with a Jetson Xavier NX was 0.83 s, indicating the potential for the model in fast real-time sensing of lettuce growth indices.

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Section: Resultsmentioning

confidence: 55%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…In addition, to numerically compare the performance with a previous study [ 28 ], the NMSE value was calculated using Equation (4):

where gt is the actual growth indices, p is the estimated value, n is the number of images, and m is the number of growth indices.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Greenhouse Lettuce Growth Indices Based on a Two-Stage CNN Using RGB-D Images

Gang

Kim

2022

Sensors

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Automatic non-destructive multiple lettuce traits prediction based on DeepLabV3 +

Shen

et al. 2022

Food Measure

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Application of X-ray computed tomography in soil and plant -a review

Ghosh,

Maity,

Rabbi

et al. 2023

Front. Environ. Sci.

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X-ray computed tomography (X-ray CT) is a non-destructive method of soil analysis which can provide three-dimensional (3D) view, quantitative information of the internal organization of the soil. In this paper, we discuss the potential application of X-ray CT in characterization of soil properties like porosity and pore size distribution (PSD), root architecture, soil phase classification, water and solute transport in soil, and highlight the research during last 10–15 years. Here, we review the recent development of X-ray CT in soil science, use of artificial intelligence and machine learning in image analysis, point out the major challenges associated with its use, discuss few improvements to overcome these difficulties and elaborate the possible future technological developments for non-invasive/destructive soil characterization by integrating X-ray CT with recently available complementary techniques.

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Simultaneously Predicting Multiple Plant Traits from Multiple Sensors via Deformable CNN Regression

Cited by 4 publications

References 0 publications

Estimation of Greenhouse Lettuce Growth Indices Based on a Two-Stage CNN Using RGB-D Images

Estimation of Greenhouse Lettuce Growth Indices Based on a Two-Stage CNN Using RGB-D Images

Automatic non-destructive multiple lettuce traits prediction based on DeepLabV3 +

Application of X-ray computed tomography in soil and plant -a review

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