2024
11.
Ozmen Erkin Kokten; Raviv Raich; James Holmes; Alan Fern
Learning Extended Forecasts of Soil Water Content via Physically-Inspired Autoregressive Models Proceedings Article
In: 2024 International Conference on Machine Learning and Applications (ICMLA), pp. 400–407, 2024, (ISSN: 1946-0759).
Abstract | Links | BibTeX | Tags: autoregressive training, non-linear state-space models, Pipelines, Predictive models, Soil measurements, State-space methods, Stress, teacher-forcing, time-series, Training, Training data, Weather forecasting
@inproceedings{kokten_learning_2024,
title = {Learning Extended Forecasts of Soil Water Content via Physically-Inspired Autoregressive Models},
author = {Ozmen Erkin Kokten and Raviv Raich and James Holmes and Alan Fern},
url = {https://ieeexplore.ieee.org/abstract/document/10903312},
doi = {10.1109/ICMLA61862.2024.00060},
year = {2024},
date = {2024-12-01},
urldate = {2024-12-01},
booktitle = {2024 International Conference on Machine Learning and Applications (ICMLA)},
pages = {400\textendash407},
abstract = {Vine stress resulting from soil water content (SWC) restrictions allows growers to improve grape and subsequent wine quality. In this work, we consider learning models that can forecast SWC to assist growers' irrigation decisions. In particular, we investigate training auto-regressive recurrent neural networks to make multi-day hourly forecasts of SWC based on historical data from soil-moisture sensors, irrigation sched-ules, and evapotranspiration estimates. Our work addresses two practical challenges in training such models. First, trained auto-regressive models are prone to error propagation, which quickly degrades longer-term forecasts. Second, it is difficult to learn the underlying causal relationship between irrigation and soil moisture due to the training data having limited coverage of the primary control input, irrigation. We propose a training strategy that combines one-step teacher forcing loss with a loss over multi-step autoregressive predictions and novel regularization terms to ensure SWC forecasts align with scientific models, effectively addressing the key challenges. We present results from five irrigation blocks with two cultivars, using datasets ranging from 2947 to 4784 hourly measurements of SWC, irrigation, and weather. Our methodology achieves precise SWC predictions and generates realistic forecasts for untrained irrigation scenarios.},
note = {ISSN: 1946-0759},
keywords = {autoregressive training, non-linear state-space models, Pipelines, Predictive models, Soil measurements, State-space methods, Stress, teacher-forcing, time-series, Training, Training data, Weather forecasting},
pubstate = {published},
tppubtype = {inproceedings}
}
Vine stress resulting from soil water content (SWC) restrictions allows growers to improve grape and subsequent wine quality. In this work, we consider learning models that can forecast SWC to assist growers' irrigation decisions. In particular, we investigate training auto-regressive recurrent neural networks to make multi-day hourly forecasts of SWC based on historical data from soil-moisture sensors, irrigation sched-ules, and evapotranspiration estimates. Our work addresses two practical challenges in training such models. First, trained auto-regressive models are prone to error propagation, which quickly degrades longer-term forecasts. Second, it is difficult to learn the underlying causal relationship between irrigation and soil moisture due to the training data having limited coverage of the primary control input, irrigation. We propose a training strategy that combines one-step teacher forcing loss with a loss over multi-step autoregressive predictions and novel regularization terms to ensure SWC forecasts align with scientific models, effectively addressing the key challenges. We present results from five irrigation blocks with two cultivars, using datasets ranging from 2947 to 4784 hourly measurements of SWC, irrigation, and weather. Our methodology achieves precise SWC predictions and generates realistic forecasts for untrained irrigation scenarios.
12.
Mohammad Rafid Ul Islam; Prasad Tadepalli; Alan Fern
Self-attention-based Diffusion Model for Time-series Imputation Journal Article
In: Proceedings of the AAAI Symposium Series, vol. 4, no. 1, pp. 424–431, 2024, ISSN: 2994-4317, (Number: 1).
Abstract | Links | BibTeX | Tags:
@article{islam_self-attention-based_2024,
title = {Self-attention-based Diffusion Model for Time-series Imputation},
author = {Mohammad Rafid Ul Islam and Prasad Tadepalli and Alan Fern},
url = {https://ojs.aaai.org/index.php/AAAI-SS/article/view/31827},
doi = {10.1609/aaaiss.v4i1.31827},
issn = {2994-4317},
year = {2024},
date = {2024-11-01},
urldate = {2024-11-01},
journal = {Proceedings of the AAAI Symposium Series},
volume = {4},
number = {1},
pages = {424\textendash431},
abstract = {Time-series modeling is essential for applications in agriculture, weather forecasting, food production, and more. However, missing data due to sensor malfunctions, power outages, and human errors is a common issue, complicating the training of machine learning models. We propose a diffusion-based generative model to
address this problem and fill the gaps in the data. Our approach captures feature and time correlations through a two-stage imputation process. Our model outperforms state-of-the-art imputation methods and is more scalable in GPU resources.},
note = {Number: 1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Time-series modeling is essential for applications in agriculture, weather forecasting, food production, and more. However, missing data due to sensor malfunctions, power outages, and human errors is a common issue, complicating the training of machine learning models. We propose a diffusion-based generative model to
address this problem and fill the gaps in the data. Our approach captures feature and time correlations through a two-stage imputation process. Our model outperforms state-of-the-art imputation methods and is more scalable in GPU resources.
address this problem and fill the gaps in the data. Our approach captures feature and time correlations through a two-stage imputation process. Our model outperforms state-of-the-art imputation methods and is more scalable in GPU resources.
13.
Ranjan Sapkota; Achyut Paudel; Manoj Karkee
2024, (arXiv:2411.11285).
Abstract | Links | BibTeX | Tags: Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
@misc{sapkota_zero-shot_2024,
title = {Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development},
author = {Ranjan Sapkota and Achyut Paudel and Manoj Karkee},
url = {http://arxiv.org/abs/2411.11285},
doi = {10.48550/arXiv.2411.11285},
year = {2024},
date = {2024-11-01},
urldate = {2024-11-01},
publisher = {arXiv},
abstract = {Currently, deep learning-based instance segmentation for various applications (e.g., Agriculture) is predominantly performed using a labor-intensive process involving extensive field data collection using sophisticated sensors, followed by careful manual annotation of images, presenting significant logistical and financial challenges to researchers and organizations. The process also slows down the model development and training process. In this study, we presented a novel method for deep learning-based instance segmentation of apples in commercial orchards that eliminates the need for labor-intensive field data collection and manual annotation. Utilizing a Large Language Model (LLM), we synthetically generated orchard images and automatically annotated them using the Segment Anything Model (SAM) integrated with a YOLO11 base model. This method significantly reduces reliance on physical sensors and manual data processing, presenting a major advancement in "Agricultural AI". The synthetic, auto-annotated dataset was used to train the YOLO11 model for Apple instance segmentation, which was then validated on real orchard images. The results showed that the automatically generated annotations achieved a Dice Coefficient of 0.9513 and an IoU of 0.9303, validating the accuracy and overlap of the mask annotations. All YOLO11 configurations, trained solely on these synthetic datasets with automated annotations, accurately recognized and delineated apples, highlighting the method's efficacy. Specifically, the YOLO11m-seg configuration achieved a mask precision of 0.902 and a mask mAP@50 of 0.833 on test images collected from a commercial orchard. Additionally, the YOLO11l-seg configuration outperformed other models in validation on 40 LLM-generated images, achieving the highest mask precision and mAP@50 metrics. Keywords: YOLO, SAM, SAMv2, YOLO11, YOLOv11, Segment Anything, YOLO-SAM},
note = {arXiv:2411.11285},
keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition},
pubstate = {published},
tppubtype = {misc}
}
Currently, deep learning-based instance segmentation for various applications (e.g., Agriculture) is predominantly performed using a labor-intensive process involving extensive field data collection using sophisticated sensors, followed by careful manual annotation of images, presenting significant logistical and financial challenges to researchers and organizations. The process also slows down the model development and training process. In this study, we presented a novel method for deep learning-based instance segmentation of apples in commercial orchards that eliminates the need for labor-intensive field data collection and manual annotation. Utilizing a Large Language Model (LLM), we synthetically generated orchard images and automatically annotated them using the Segment Anything Model (SAM) integrated with a YOLO11 base model. This method significantly reduces reliance on physical sensors and manual data processing, presenting a major advancement in "Agricultural AI". The synthetic, auto-annotated dataset was used to train the YOLO11 model for Apple instance segmentation, which was then validated on real orchard images. The results showed that the automatically generated annotations achieved a Dice Coefficient of 0.9513 and an IoU of 0.9303, validating the accuracy and overlap of the mask annotations. All YOLO11 configurations, trained solely on these synthetic datasets with automated annotations, accurately recognized and delineated apples, highlighting the method's efficacy. Specifically, the YOLO11m-seg configuration achieved a mask precision of 0.902 and a mask mAP@50 of 0.833 on test images collected from a commercial orchard. Additionally, the YOLO11l-seg configuration outperformed other models in validation on 40 LLM-generated images, achieving the highest mask precision and mAP@50 metrics. Keywords: YOLO, SAM, SAMv2, YOLO11, YOLOv11, Segment Anything, YOLO-SAM
14.
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.
15.
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.
16.
Supriya Savalkar; Michael Pumphrey; Kimberly Campbell; Fabio Scarpare; Tanvir Ferdousi; Samarth Swarup; Claudio Stöckle; Kirti Rajagopalan
Earlier planting in a future climate fails to replicate historical production conditions for US spring wheat. Journal Article
In: 2024, (ISSN: 2693-5015).
Abstract | Links | BibTeX | Tags:
@article{savalkar_earlier_2024,
title = {Earlier planting in a future climate fails to replicate historical production conditions for US spring wheat.},
author = {Supriya Savalkar and Michael Pumphrey and Kimberly Campbell and Fabio Scarpare and Tanvir Ferdousi and Samarth Swarup and Claudio St\"{o}ckle and Kirti Rajagopalan},
url = {https://www.researchsquare.com/article/rs-4940971/v1},
doi = {10.21203/rs.3.rs-4940971/v1},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
publisher = {Research Square},
abstract = {Global warming can increase crop heat stress exposure, adversely affecting crop yields and quality. Earlier planting is widely considered in climate change literature as a potential adaptation strategy by shifting the growing season to cooler periods. However, the effectiveness of earlier planting in achieving overall temperature exposures during the crop growth season that are at least as favorable as historical conditions remains unclear. Our objective is to comprehensively assess the potential effectiveness of earlier planting as an adaptation strategy by addressing two key questions: How effective is earlier planting in reducing exposure to excessively high temperatures across different growth stages? What are the associated tradeoffs in temperature exposure, and can historical conditions be matched with this adaptation strategy? We focus on major US spring wheat growing states as a case study, analyzing lethal, critical, suboptimal, and optimal temperature thresholds by growth stage to quantify the impact of earlier planting. Our findings indicate that while earlier planting does reduce exposure to critical and lethal high-temperature categories during some reproductive stages, it generally fails to replicate historical production conditions for the majority of the US spring wheat production region (contributing 85% of the current production). This trend persists across all future time frames and emission scenarios. The Pacific Northwest US presents an exception; however, even in this region, certain early and late growth stages may experience worse-than-historical conditions requiring management. The planting window with historically equivalent temperature exposures also narrows from 11 weeks to 1-7 weeks, presenting additional logistical challenges. Given that the Pacific Northwest US accounts for less than 15% of the national spring wheat production, at a national scale, current production levels are unlikely to be sustained by primarily relying on earlier planting as an adaptation strategy. This is a critical consideration, especially given that many climate change assessments frequently list earlier planting as an effective adaptation strategy without the capacity to fully explore its tradeoffs. Exploring additional adaptation strategies to maintain current national production levels is important.},
note = {ISSN: 2693-5015},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Global warming can increase crop heat stress exposure, adversely affecting crop yields and quality. Earlier planting is widely considered in climate change literature as a potential adaptation strategy by shifting the growing season to cooler periods. However, the effectiveness of earlier planting in achieving overall temperature exposures during the crop growth season that are at least as favorable as historical conditions remains unclear. Our objective is to comprehensively assess the potential effectiveness of earlier planting as an adaptation strategy by addressing two key questions: How effective is earlier planting in reducing exposure to excessively high temperatures across different growth stages? What are the associated tradeoffs in temperature exposure, and can historical conditions be matched with this adaptation strategy? We focus on major US spring wheat growing states as a case study, analyzing lethal, critical, suboptimal, and optimal temperature thresholds by growth stage to quantify the impact of earlier planting. Our findings indicate that while earlier planting does reduce exposure to critical and lethal high-temperature categories during some reproductive stages, it generally fails to replicate historical production conditions for the majority of the US spring wheat production region (contributing 85% of the current production). This trend persists across all future time frames and emission scenarios. The Pacific Northwest US presents an exception; however, even in this region, certain early and late growth stages may experience worse-than-historical conditions requiring management. The planting window with historically equivalent temperature exposures also narrows from 11 weeks to 1-7 weeks, presenting additional logistical challenges. Given that the Pacific Northwest US accounts for less than 15% of the national spring wheat production, at a national scale, current production levels are unlikely to be sustained by primarily relying on earlier planting as an adaptation strategy. This is a critical consideration, especially given that many climate change assessments frequently list earlier planting as an effective adaptation strategy without the capacity to fully explore its tradeoffs. Exploring additional adaptation strategies to maintain current national production levels is important.
17.
Ranjan Sapkota; Dawood Ahmed; Manoj Karkee
Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments Journal Article
In: Artificial Intelligence in Agriculture, vol. 13, pp. 84–99, 2024, ISSN: 2589-7217.
Abstract | Links | BibTeX | Tags: Artificial intelligence, Automation, Deep learning, Machine Learning, Machine vision, Mask R-CNN, Robotics, YOLOv8
@article{sapkota_comparing_2024,
title = {Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments},
author = {Ranjan Sapkota and Dawood Ahmed and Manoj Karkee},
url = {https://www.sciencedirect.com/science/article/pii/S258972172400028X},
doi = {10.1016/j.aiia.2024.07.001},
issn = {2589-7217},
year = {2024},
date = {2024-09-01},
urldate = {2024-09-01},
journal = {Artificial Intelligence in Agriculture},
volume = {13},
pages = {84\textendash99},
abstract = {Instance segmentation, an important image processing operation for automation in agriculture, is used to precisely delineate individual objects of interest within images, which provides foundational information for various automated or robotic tasks such as selective harvesting and precision pruning. This study compares the one-stage YOLOv8 and the two-stage Mask R-CNN machine learning models for instance segmentation under varying orchard conditions across two datasets. Dataset 1, collected in dormant season, includes images of dormant apple trees, which were used to train multi-object segmentation models delineating tree branches and trunks. Dataset 2, collected in the early growing season, includes images of apple tree canopies with green foliage and immature (green) apples (also called fruitlet), which were used to train single-object segmentation models delineating only immature green apples. The results showed that YOLOv8 performed better than Mask R-CNN, achieving good precision and near-perfect recall across both datasets at a confidence threshold of 0.5. Specifically, for Dataset 1, YOLOv8 achieved a precision of 0.90 and a recall of 0.95 for all classes. In comparison, Mask R-CNN demonstrated a precision of 0.81 and a recall of 0.81 for the same dataset. With Dataset 2, YOLOv8 achieved a precision of 0.93 and a recall of 0.97. Mask R-CNN, in this single-class scenario, achieved a precision of 0.85 and a recall of 0.88. Additionally, the inference times for YOLOv8 were 10.9 ms for multi-class segmentation (Dataset 1) and 7.8 ms for single-class segmentation (Dataset 2), compared to 15.6 ms and 12.8 ms achieved by Mask R-CNN's, respectively. These findings show YOLOv8's superior accuracy and efficiency in machine learning applications compared to two-stage models, specifically Mask-R-CNN, which suggests its suitability in developing smart and automated orchard operations, particularly when real-time applications are necessary in such cases as robotic harvesting and robotic immature green fruit thinning.},
keywords = {Artificial intelligence, Automation, Deep learning, Machine Learning, Machine vision, Mask R-CNN, Robotics, YOLOv8},
pubstate = {published},
tppubtype = {article}
}
Instance segmentation, an important image processing operation for automation in agriculture, is used to precisely delineate individual objects of interest within images, which provides foundational information for various automated or robotic tasks such as selective harvesting and precision pruning. This study compares the one-stage YOLOv8 and the two-stage Mask R-CNN machine learning models for instance segmentation under varying orchard conditions across two datasets. Dataset 1, collected in dormant season, includes images of dormant apple trees, which were used to train multi-object segmentation models delineating tree branches and trunks. Dataset 2, collected in the early growing season, includes images of apple tree canopies with green foliage and immature (green) apples (also called fruitlet), which were used to train single-object segmentation models delineating only immature green apples. The results showed that YOLOv8 performed better than Mask R-CNN, achieving good precision and near-perfect recall across both datasets at a confidence threshold of 0.5. Specifically, for Dataset 1, YOLOv8 achieved a precision of 0.90 and a recall of 0.95 for all classes. In comparison, Mask R-CNN demonstrated a precision of 0.81 and a recall of 0.81 for the same dataset. With Dataset 2, YOLOv8 achieved a precision of 0.93 and a recall of 0.97. Mask R-CNN, in this single-class scenario, achieved a precision of 0.85 and a recall of 0.88. Additionally, the inference times for YOLOv8 were 10.9 ms for multi-class segmentation (Dataset 1) and 7.8 ms for single-class segmentation (Dataset 2), compared to 15.6 ms and 12.8 ms achieved by Mask R-CNN's, respectively. These findings show YOLOv8's superior accuracy and efficiency in machine learning applications compared to two-stage models, specifically Mask-R-CNN, which suggests its suitability in developing smart and automated orchard operations, particularly when real-time applications are necessary in such cases as robotic harvesting and robotic immature green fruit thinning.
18.
Basavaraj R. Amogi; Nisit Pukrongta; Lav R. Khot; Bernardita V. Sallato
Edge compute algorithm enabled localized crop physiology sensing system for apple (textitMalus domestica Borkh.) crop water stress monitoring Journal Article
In: Computers and Electronics in Agriculture, vol. 224, pp. 109137, 2024, ISSN: 0168-1699.
Abstract | Links | BibTeX | Tags: AI, Farm Ops
@article{amogi_edge_2024,
title = {Edge compute algorithm enabled localized crop physiology sensing system for apple (textitMalus domestica Borkh.) crop water stress monitoring},
author = {Basavaraj R. Amogi and Nisit Pukrongta and Lav R. Khot and Bernardita V. Sallato},
url = {https://www.sciencedirect.com/science/article/pii/S0168169924005283},
doi = {10.1016/j.compag.2024.109137},
issn = {0168-1699},
year = {2024},
date = {2024-09-01},
urldate = {2024-09-01},
journal = {Computers and Electronics in Agriculture},
volume = {224},
pages = {109137},
abstract = {Elevated air temperature (\>35 ℃) combined with intense solar radiation can cause heat stress related damage to apple (Malus domestica Borkh.) fruits (e.g., sunburn) and increase tree evapotranspiration demand. Current heat stress mitigation techniques (e.g., evaporative cooling and netting) may protect fruits but can skew the tree evapotranspiration rates, preventing precision under-tree irrigation. A detailed understanding of heat stress mitigation techniques on tree fruit water status is critical for optimized irrigation scheduling and reduced crop losses. This study aimed to quantify water stress using a localized edge-compute-enabled crop physiology sensing system (CPSS), developed previously for fruit heat stress management. The CPSS is capable of acquiring thermal infrared and RGB images of the scene at predetermined interval. In this study, the edge compute algorithm on CPSS was amended to estimate crop water stress index (CWSI). Developed algorithm was validated for its accuracy in predicting the crop water stress under four different heat stress mitigation techniques namely: conventional overhead sprinklers, foggers, netting, and combinations of foggers and netting. A CPSS node was deployed in each treatment for acquiring thermal infrared and RGB images. Acquired imagery data were used to estimate CWSI using the modified algorithm. The algorithm-estimated CWSI showed significant negative correlation with stem water potential measurements (r = -0.8, p \< 0.01). The heat stress mitigation techniques had varying effects on sensitivity of estimated CWSI. Algorithm estimated CWSI was most sensitive to changes in water stress under fogging (r = 0.76) and least sensitive under neeting (r = -0.65). Overall, the use of real-time CWSI estimates in conjunction with heat stress monitoring could help improve precision irrigation management, enabling timely actuation of the under tree drip irrigation in apple orchards.},
keywords = {AI, Farm Ops},
pubstate = {published},
tppubtype = {article}
}
Elevated air temperature (>35 ℃) combined with intense solar radiation can cause heat stress related damage to apple (Malus domestica Borkh.) fruits (e.g., sunburn) and increase tree evapotranspiration demand. Current heat stress mitigation techniques (e.g., evaporative cooling and netting) may protect fruits but can skew the tree evapotranspiration rates, preventing precision under-tree irrigation. A detailed understanding of heat stress mitigation techniques on tree fruit water status is critical for optimized irrigation scheduling and reduced crop losses. This study aimed to quantify water stress using a localized edge-compute-enabled crop physiology sensing system (CPSS), developed previously for fruit heat stress management. The CPSS is capable of acquiring thermal infrared and RGB images of the scene at predetermined interval. In this study, the edge compute algorithm on CPSS was amended to estimate crop water stress index (CWSI). Developed algorithm was validated for its accuracy in predicting the crop water stress under four different heat stress mitigation techniques namely: conventional overhead sprinklers, foggers, netting, and combinations of foggers and netting. A CPSS node was deployed in each treatment for acquiring thermal infrared and RGB images. Acquired imagery data were used to estimate CWSI using the modified algorithm. The algorithm-estimated CWSI showed significant negative correlation with stem water potential measurements (r = -0.8, p < 0.01). The heat stress mitigation techniques had varying effects on sensitivity of estimated CWSI. Algorithm estimated CWSI was most sensitive to changes in water stress under fogging (r = 0.76) and least sensitive under neeting (r = -0.65). Overall, the use of real-time CWSI estimates in conjunction with heat stress monitoring could help improve precision irrigation management, enabling timely actuation of the under tree drip irrigation in apple orchards.
19.
Mohsen Farajijalal, Samira Malek, Arash Toudeshki, Joshua H. Viers, Reza Ehsani
Data-Driven Model to Improve Mechanical Harvesters for Nut Trees Proceedings Article
In: ASABE, 2024.
Abstract | Links | BibTeX | Tags:
@inproceedings{noauthor_data-driven_nodateb,
title = {Data-Driven Model to Improve Mechanical Harvesters for Nut Trees},
author = {Mohsen Farajijalal, Samira Malek, Arash Toudeshki, Joshua H. Viers, Reza Ehsani},
url = {https://doi.org/10.13031/aim.202400858},
year = {2024},
date = {2024-08-02},
publisher = {ASABE},
abstract = {The economic impact of California‘s agricultural sector is the most important in the United States. California produces more than 80% of the world‘s almonds. Currently, the trunk shaker harvesting machine is the most widely used for harvesting almond trees in California. During the harvest, the operator, based on their experience, set the shaking parameters such as duration and shaking frequency. Manual adjustments by operators lead to variability in fruit removal, and in some cases, it could cause tree damage. This study aimed to develop a data-driven mathematical model required to build an intelligent tree shaker machine capable of optimizing shaking parameters autonomously. A sensor system to monitor force distribution throughout the tree canopy was designed and implemented. Data was collected from an almond orchard in California during the summer of 2023. A quadratic mathematical model was developed using machine learning regression methods to estimate relationships between trunk diameter, acceleration, and shaking duration. The findings show that trunk diameter positively correlates with acceleration and shaking time duration. Our research demonstrates the potential for intelligent harvesting machines that can adjust parameters based on real-time sensor data, ultimately improving fruit removal efficiency, and potentially reducing tree damage.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The economic impact of California‘s agricultural sector is the most important in the United States. California produces more than 80% of the world‘s almonds. Currently, the trunk shaker harvesting machine is the most widely used for harvesting almond trees in California. During the harvest, the operator, based on their experience, set the shaking parameters such as duration and shaking frequency. Manual adjustments by operators lead to variability in fruit removal, and in some cases, it could cause tree damage. This study aimed to develop a data-driven mathematical model required to build an intelligent tree shaker machine capable of optimizing shaking parameters autonomously. A sensor system to monitor force distribution throughout the tree canopy was designed and implemented. Data was collected from an almond orchard in California during the summer of 2023. A quadratic mathematical model was developed using machine learning regression methods to estimate relationships between trunk diameter, acceleration, and shaking duration. The findings show that trunk diameter positively correlates with acceleration and shaking time duration. Our research demonstrates the potential for intelligent harvesting machines that can adjust parameters based on real-time sensor data, ultimately improving fruit removal efficiency, and potentially reducing tree damage.
20.
Dawood Ahmed, Ranjan Sapkota, Martin Churuvija, Matthew Whiting, Manoj Karkee
Slicing-Aided Hyper Inference for Enhanced Fruit Bud Detection and Counting in Apple Orchards during Dormant Season Proceedings Article
In: ASABE, 2024.
Abstract | Links | BibTeX | Tags:
@inproceedings{noauthor_slicing-aided_nodateb,
title = {Slicing-Aided Hyper Inference for Enhanced Fruit Bud Detection and Counting in Apple Orchards during Dormant Season},
author = {Dawood Ahmed, Ranjan Sapkota, Martin Churuvija, Matthew Whiting, Manoj Karkee},
url = {https://doi.org/10.13031/aim.202401055},
year = {2024},
date = {2024-08-02},
publisher = {ASABE},
abstract = {To meet the global food demand amidst the growing population, there‘s a critical need to augment productivity in the specialty crop industry. However, workforce shortage in agriculture presents a significant challenge to increasing agricultural productivity, emphasizing the need for automating various farm operations such as harvesting, canopy management, and crop-load management practices. A critical aspect of crop-load management is determining existing crop-load along with target yield, which can be used for optimizing crop-load in orchards. Buds, the initial phase of the flowering and fruiting process, are critical for early crop-load estimation (number of fruiting sites per branch or tree) and can facilitate informed pruning decisions to achieve the desired crop-load. Yet, the detection of these buds poses a huge challenge due to their miniature size and variable environmental conditions (including lighting) of commercial orchards. This research explores the performances of a Convolutional Neural Network (CNN) model and a Detection Transformer (DETR) model in detecting fruiting buds in apple orchards, using standard inference and a hyper inferencing technique. Additionally, the system quantifies buds and employs an association algorithm to correlate each bud to its respective branch, facilitating the estimation of the number of buds per branch. Despite the challenges due to the size of buds and environmental complexity, the proposed system demonstrated precision of 80.2 %, recall of 80.1 %, and f1-score of 80.15 % in detecting fruit buds, using a YOLOv8 model. The use of the Slicing Aided Hyper Inferencing Technique improved the overall recall score of the model. Additionally, the system quantified buds in a frame for the most salient branch using the branch-bud association with an accuracy of 73%. This study demonstrated a promising workflow in detecting and counting fruiting buds in a dynamic orchard environment to facilitate current manual pruning operation as well as future studies on automated pruning.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
To meet the global food demand amidst the growing population, there‘s a critical need to augment productivity in the specialty crop industry. However, workforce shortage in agriculture presents a significant challenge to increasing agricultural productivity, emphasizing the need for automating various farm operations such as harvesting, canopy management, and crop-load management practices. A critical aspect of crop-load management is determining existing crop-load along with target yield, which can be used for optimizing crop-load in orchards. Buds, the initial phase of the flowering and fruiting process, are critical for early crop-load estimation (number of fruiting sites per branch or tree) and can facilitate informed pruning decisions to achieve the desired crop-load. Yet, the detection of these buds poses a huge challenge due to their miniature size and variable environmental conditions (including lighting) of commercial orchards. This research explores the performances of a Convolutional Neural Network (CNN) model and a Detection Transformer (DETR) model in detecting fruiting buds in apple orchards, using standard inference and a hyper inferencing technique. Additionally, the system quantifies buds and employs an association algorithm to correlate each bud to its respective branch, facilitating the estimation of the number of buds per branch. Despite the challenges due to the size of buds and environmental complexity, the proposed system demonstrated precision of 80.2 %, recall of 80.1 %, and f1-score of 80.15 % in detecting fruit buds, using a YOLOv8 model. The use of the Slicing Aided Hyper Inferencing Technique improved the overall recall score of the model. Additionally, the system quantified buds in a frame for the most salient branch using the branch-bud association with an accuracy of 73%. This study demonstrated a promising workflow in detecting and counting fruiting buds in a dynamic orchard environment to facilitate current manual pruning operation as well as future studies on automated pruning.
