2024
1.
Srikanth Gorthi; Dattatray G. Bhalekar; Lav R. Khot; Markus Keller
Modeling Grape Berry Temperature for Effective Heat Stress Management in Vineyards Proceedings Article
In: 2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 237–241, 2024.
Abstract | Links | BibTeX | Tags: Atmospheric modeling, Berry Temperature, Lasso Regression, Pipelines, Random Forest, Random forests, Real-time systems, Ridge Regression, Soil, Soil measurements, Solar radiation, Stress, Temperature measurement, Water heating
@inproceedings{gorthi_modeling_2024,
title = {Modeling Grape Berry Temperature for Effective Heat Stress Management in Vineyards},
author = {Srikanth Gorthi and Dattatray G. Bhalekar and Lav R. Khot and Markus Keller},
url = {https://ieeexplore.ieee.org/document/10948825},
doi = {10.1109/MetroAgriFor63043.2024.10948825},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
booktitle = {2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},
pages = {237\textendash241},
abstract = {This study developed a machine learning model to predict berry temperature using localized weather and soil attributes measured during the summer of 2023. Berry temperature was observed to be higher compared to canopy and air temperature during extreme heat events. A tree-based pipeline optimization tool was used to find an optimum machine learning algorithm. Amongst the tested models, Lasso Regression exhibited reasonable accuracy (R2 = 0.98) and root mean squared error of 0.93 °C on the test dataset.},
keywords = {Atmospheric modeling, Berry Temperature, Lasso Regression, Pipelines, Random Forest, Random forests, Real-time systems, Ridge Regression, Soil, Soil measurements, Solar radiation, Stress, Temperature measurement, Water heating},
pubstate = {published},
tppubtype = {inproceedings}
}
This study developed a machine learning model to predict berry temperature using localized weather and soil attributes measured during the summer of 2023. Berry temperature was observed to be higher compared to canopy and air temperature during extreme heat events. A tree-based pipeline optimization tool was used to find an optimum machine learning algorithm. Amongst the tested models, Lasso Regression exhibited reasonable accuracy (R2 = 0.98) and root mean squared error of 0.93 °C on the test dataset.
2.
Basavaraj R. Amogi; Lav R. Khot; Bernardita V. Sallato
Localized Crop Physiology Sensing System Driven Apple Fruit Color Progression Monitoring Proceedings Article
In: 2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), pp. 232–236, 2024.
Abstract | Links | BibTeX | Tags: Biomedical monitoring, Crops, cyber physical system, fruit color, hue, Image color analysis, Monitoring, Prevention and mitigation, Real-time systems, Sensors, Solar heating, Solar radiation, Stress
@inproceedings{amogi_localized_2024,
title = {Localized Crop Physiology Sensing System Driven Apple Fruit Color Progression Monitoring},
author = {Basavaraj R. Amogi and Lav R. Khot and Bernardita V. Sallato},
url = {https://ieeexplore.ieee.org/document/10948846},
doi = {10.1109/MetroAgriFor63043.2024.10948846},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
booktitle = {2024 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)},
pages = {232\textendash236},
abstract = {Fruit color is a critical quality attribute that significantly affects the commercial value of apples. Elevated air temperatures and solar radiation during heat waves can substantially impact fruit coloration, while mitigation techniques such as netting can further compromise fruit color development by trapping heat. Continuous monitoring of fruit color throughout the growing season, particularly under heat stress conditions, is thus essential for informed grower decision-making. This study leverages a previously developed localized crop physiology sensing system (CPSS) to enable real time monitoring of apple fruit color progression. Using visible imagery captured by the CPSS, apple fruit color was quantified by extracting the hue angle (ˆtextbackslashcirctextbackslashmathbfh). The results showed that, the influence of sunlight on measured color accuracy (ΔE) is lower and more stable during mid-day hours (1000 h − 1300 h), whereas notable variations were observed during early morning and late afternoon periods. The ˆtextbackslashcirctextbackslashmathbfh hence calculated for RGB images captured around 1200 h was utilized to track fruit color progression. Its monitoring over the summer 2022 growing season showed variations as an effect of environmental stressors, especially in response to heat wave. The developed approach offers a tool for growers to adjust different heat mitigation techniques during heat wave/events to mitigate any negative implication on fruit coloration.},
keywords = {Biomedical monitoring, Crops, cyber physical system, fruit color, hue, Image color analysis, Monitoring, Prevention and mitigation, Real-time systems, Sensors, Solar heating, Solar radiation, Stress},
pubstate = {published},
tppubtype = {inproceedings}
}
Fruit color is a critical quality attribute that significantly affects the commercial value of apples. Elevated air temperatures and solar radiation during heat waves can substantially impact fruit coloration, while mitigation techniques such as netting can further compromise fruit color development by trapping heat. Continuous monitoring of fruit color throughout the growing season, particularly under heat stress conditions, is thus essential for informed grower decision-making. This study leverages a previously developed localized crop physiology sensing system (CPSS) to enable real time monitoring of apple fruit color progression. Using visible imagery captured by the CPSS, apple fruit color was quantified by extracting the hue angle (ˆtextbackslashcirctextbackslashmathbfh). The results showed that, the influence of sunlight on measured color accuracy (ΔE) is lower and more stable during mid-day hours (1000 h − 1300 h), whereas notable variations were observed during early morning and late afternoon periods. The ˆtextbackslashcirctextbackslashmathbfh hence calculated for RGB images captured around 1200 h was utilized to track fruit color progression. Its monitoring over the summer 2022 growing season showed variations as an effect of environmental stressors, especially in response to heat wave. The developed approach offers a tool for growers to adjust different heat mitigation techniques during heat wave/events to mitigate any negative implication on fruit coloration.
