ORCID Profile
0000-0002-4620-0409
Current Organisations
Monash University
,
Pixxel
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Springer International Publishing
Date: 2018
Publisher: Burleigh Dodds Science Publishing
Date: 28-04-2020
Publisher: Burleigh Dodds Science Publishing
Date: 28-04-2020
Publisher: MDPI AG
Date: 24-06-2023
DOI: 10.3390/AGRICULTURE13071292
Abstract: The biophysical properties of a crop are a good indicator of potential crop stress conditions. However, these visible properties cannot indicate areas exhibiting non-visible stress, e.g., early water or nutrient stress. In this research, maize crop biophysical properties including canopy height and Leaf Area Index (LAI), estimated using drone-based RGB images, were used to identify stressed areas in the farm. First, the APSIM process-based model was used to simulate temporal variation in LAI and canopy height under optimal management conditions, and thus used as a reference for estimating healthy crop parameters. The simulated LAI and canopy height were then compared with the ground-truth information to generate synthetic data for training a linear and a random forest model to identify stressed and healthy areas in the farm using drone-based data products. A Healthiness Index was developed using linear as well as random forest models for indicating the health of the crop, with a maximum correlation coefficient of 0.67 obtained between Healthiness Index during the dough stage of the crop and crop yield. Although these methods are effective in identifying stressed and non-stressed areas, they currently do not offer direct insights into the underlying causes of stress. However, this presents an opportunity for further research and improvement of the approach.
Publisher: MDPI AG
Date: 08-02-2022
Abstract: We propose data-driven approaches to water content estimation and ripeness classification of the strawberry fruit. A narrowband hyperspectral spectroradiometer was used to collect reflectance signatures from 43 strawberry fruits at different ripeness levels. Then, the ground truth water content was obtained using the oven-dry method. To estimate the water content, 674 and 698 nm bands were selected to create a normalized difference strawberry water content index. The index was used as an input to a logarithmic model for estimating fruit water content. The model for water content estimation gave a correlation coefficient of 0.82 and Root Mean Squared Error (RMSE) of 0.0092 g/g. For ripeness classification, a Support Vector Machine (SVM) model using the full spectrum as input achieved over 98% accuracy. Our analysis further show that, in the absence of the full spectrum data, using our proposed water content index as input, which uses reflectance values from only two frequency bands, achieved 71% ripeness classification accuracy, which might be adequate for certain applications with limited sensing resources.
Publisher: Elsevier BV
Date: 04-2021
Publisher: Elsevier BV
Date: 12-2021
Publisher: Springer International Publishing
Date: 19-11-2019
Publisher: Elsevier BV
Date: 08-2023
Publisher: Springer International Publishing
Date: 2020
Start Date: 2017
End Date: 2021
Funder: IITB-Monash Research Academy
View Funded ActivityStart Date: 2019
End Date: 2020
Funder: Monash University
View Funded Activity