Publications

@article{fern_agaid_nodate,

title = {AgAID Institute\textemdashAI for agricultural labor and decision support},

author = {Alan Fern and Margaret Burnett and Joseph Davidson and Janardhan Rao Doppa and Paola Pesantez-Cabrera and Ananth Kalyanaraman},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/aaai.12156},

doi = {10.1002/aaai.12156},

issn = {2371-9621},

year  = {2024},

date = {2024-02-16},

urldate = {2024-02-16},

journal = {AI Magazine},

volume = {n/a},

number = {n/a},

abstract = {The AgAID Institute is a National AI Research Institute focused on developing AI solutions for specialty crop agriculture. Specialty crops include a variety of fruits and vegetables, nut trees, grapes, berries, and different types of horticultural crops. In the United States, the specialty crop industry accounts for a multibillion dollar industry with over 300 crops grown just along the U.S. west coast. Specialty crop agriculture presents several unique challenges: they are labor-intensive, are easily impacted by weather extremities, and are grown mostly on irrigated lands and hence are dependent on water. The AgAID Institute aims to develop AI solutions to address these challenges, particularly in the face of workforce shortages, water scarcity, and extreme weather events. Addressing this host of challenges requires advancing foundational AI research, including spatio-temporal system modeling, robot sensing and control, multiscale site-specific decision support, and designing effective human\textendashAI workflows. This article provides examples of current AgAID efforts and points to open directions to be explored.},

note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/aaai.12156},

keywords = {AI, Farm Ops, Humans, Labor, Water},

pubstate = {published},

tppubtype = {article}

}

@article{10.1371/journal.pwat.0000184,

title = {An invisible water surcharge: Climate warming increases crop water demand in the San Joaquin Valley’s groundwater-dependent irrigated agriculture},

author = {Kelley Moyers and John T. Abatzoglou and Alvar Escriva-Bou and Josu\'{e} Medell\'{i}n-Azuara and Joshua H. Viers},

url = {https://doi.org/10.1371/journal.pwat.0000184},

doi = {10.1371/journal.pwat.0000184},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {PLOS Water},

volume = {3},

number = {3},

pages = {1-17},

publisher = {Public Library of Science},

abstract = {California’s bountiful San Joaquin Valley (SJV), a critical region for global fruit and nut production, has withstood two severe, multi-year droughts in the past decade, exacerbated by record-breaking high temperature and evaporative demand. We employed climate data and crop coefficients to estimate the crop water demand in the SJV over the past forty years. Our approach, using crop coefficients for Penman-Montieth modeled evapotranspiration, focused on the climate effects on crop water demand, avoiding the confounding factors of changing land use and management practices that are present in actual evapotranspiration. We demonstrate that increases in crop water demand explain half of the cumulative deficits of the agricultural water balance since 1980, exacerbating water reliance on depleting groundwater supplies and fluctuating surface water imports. We call this phenomenon of climate-induced increased crop water demand an invisible water surcharge. We found that in the past decade, this invisible water surcharge on agriculture has increased the crop water demand in the SJV by 4.4% with respect to the 1980\textendash2011 timeframe\textemdashmore than 800 GL per year, a volume as large as a major reservoir in the SJV. Despite potential agronomic adaptation and crop response to climate warming, increased crop water demand adds a stressor to the sustainability of the global fruit and nut supply and calls for changes in management and policies to consider the shifting hydroclimate.},

keywords = {Water},

pubstate = {published},

tppubtype = {article}

}

@article{sapkota_immature_2024,

title = {Immature Green Apple Detection and Sizing in Commercial Orchards using YOLOv8 and Shape Fitting Techniques},

author = {Ranjan Sapkota and Dawood Ahmed and Martin Churuvija and Manoj Karkee},

url = {https://ieeexplore.ieee.org/document/10474021},

doi = {10.1109/ACCESS.2024.3378261},

issn = {2169-3536},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {IEEE Access},

pages = {1\textendash1},

abstract = {Detecting and estimating size of apples during the early stages of growth is crucial for predicting yield, pest management, and making informed decisions related to crop-load management, harvest and post-harvest logistics, and marketing. Traditional fruit size measurement methods are laborious and time-consuming. This study employs the state-of-the-art YOLOv8 object detection and instance segmentation algorithm in conjunction with geometric shape fitting techniques on 3D point cloud data to accurately determine the size of immature green apples (or fruitlet) in a commercial orchard environment. The methodology utilized two RGB-D sensors: Intel RealSense D435i and Microsoft Azure Kinect DK. Notably, the YOLOv8 instance segmentation models exhibited proficiency in immature green apple detection, with the YOLOv8m-seg model achieving the highest AP@0.5 and AP@0.75 scores of 0.94 and 0.91, respectively. Using the ellipsoid fitting technique on images from the Azure Kinect, we achieved an RMSE of 2.35 mm, MAE of 1.66 mm, MAPE of 6.15 mm, and an R-squared value of 0.9 in estimating the size of apple fruitlets. Challenges such as partial occlusion caused some error in accurately delineating and sizing green apples using the YOLOv8-based segmentation technique, particularly in fruit clusters. In a comparison with 102 outdoor samples, the size estimation technique performed better on the images acquired with Microsoft Azure Kinect than the same with Intel Realsense D435i. This superiority is evident from the metrics: the RMSE values (2.35 mm for Azure Kinect vs. 9.65 mm for Realsense D435i), MAE values (1.66 mm for Azure Kinect vs. 7.8 mm for Realsense D435i), and the R-squared values (0.9 for Azure Kinect vs. 0.77 for Realsense D435i). This study demonstrated the feasibility of accurately sizing immature green fruit in early growth stages using the combined 3D sensing and shape-fitting technique, which shows promise for improved precision agricultural operations such as optimal crop-load management in orchards.},

note = {Conference Name: IEEE Access},

keywords = {AI, Labor},

pubstate = {published},

tppubtype = {article}

}

@article{bhattarai_design_2024,

title = {Design, integration, and field evaluation of a robotic blossom thinning system for tree fruit crops},

author = {Uddhav Bhattarai and Qin Zhang and Manoj Karkee},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.22330},

doi = {10.1002/rob.22330},

issn = {1556-4967},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Journal of Field Robotics},

volume = {41},

number = {5},

pages = {1366\textendash1385},

abstract = {The United States (US) apple industry relies heavily on semi-skilled manual labor force for essential field operations such as training, pruning, blossom and green fruitlet thinning, and harvesting. Blossom thinning is one of the crucial crop-load management practices to achieve desired crop load, fruit quality, and return bloom. While several techniques such as chemical and mechanical thinning are available for large-scale blossom thinning, such approaches often yield unpredictable thinning results and may damage the canopy, spurs, and leaf tissue. Hence, growers still depend on laborious, labor-intensive, and expensive manual hand blossom thinning for desired thinning outcomes. This research presents a robotic solution for precision blossom thinning in apple orchards using a deep learning-based computer vision system, a six-degrees-of-freedom UR5e robotic manipulator, and an electrically actuated miniature end-effector. The integrated robotic system was evaluated in a commercial apple orchard which showed promising results for targeted and selective blossom thinning. Two thinning approaches, center and boundary thinning, were investigated to evaluate the system's ability to remove varying proportions of flowers from apple flower clusters. During boundary thinning, the end-effector was actuated around the cluster boundary, while center thinning involved end-effector actuation only at the cluster centroid for a fixed duration of 2 s. Field evaluation results showed that the boundary thinning approach thinned 67.2% of flowers from the targeted clusters with a cycle time of 9.0 s per cluster, whereas the center thinning approach thinned 59.4% of flowers with a cycle time of 7.2 s per cluster. Upon further improvement for commercial adoption, the proposed system could help address problems faced by apple growers with current hand, chemical, and mechanical blossom thinning approaches.},

note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.22330},

keywords = {agricultural automation, agricultural robotics, artificial intelligence in agriculture, blossom thinning, robotic thinning},

pubstate = {published},

tppubtype = {article}

}

@article{kiraga_reference_2024,

title = {Reference Evapotranspiration Estimation Using Genetic Algorithm-Optimized Machine Learning Models and Standardized Penman\textendashMonteith Equation in a Highly Advective Environment},

author = {Shafik Kiraga and R. Troy Peters and Behnaz Molaei and Steven R. Evett and Gary Marek},

url = {https://www.mdpi.com/2073-4441/16/1/12},

doi = {10.3390/w16010012},

issn = {2073-4441},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Water},

volume = {16},

number = {1},

pages = {12},

abstract = {Accurate estimation of reference evapotranspiration (ETr) is important for irrigation planning, water resource management, and preserving agricultural and forest habitats. The widely used Penman\textendashMonteith equation (ASCE-PM) estimates ETr across various timescales using ground weather station data. However, discrepancies persist between estimated ETr and measured ETr obtained from weighing lysimeters (ETr-lys), particularly in advective environments. This study assessed different machine learning (ML) models in comparison to ASCE-PM for ETr estimation in highly advective conditions. Various variable combinations, representing both radiation and aerodynamic components, were organized for evaluation. Eleven datasets (DT) were created for the daily timescale, while seven were established for hourly and quarter-hourly timescales. ML models were optimized by a genetic algorithm (GA) and included support vector regression (GA-SVR), random forest (GA-RF), artificial neural networks (GA-ANN), and extreme learning machines (GA-ELM). Meteorological data and direct measurements of well-watered alfalfa grown under reference ET conditions obtained from weighing lysimeters and a nearby weather station in Bushland, Texas (1996\textendash1998), were used for training and testing. Model performance was assessed using metrics such as root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R2). ASCE-PM consistently underestimated alfalfa ET across all timescales (above 7.5 mm/day, 0.6 mm/h, and 0.2 mm/h daily, hourly, and quarter-hourly, respectively). On hourly and quarter-hourly timescales, datasets predominantly composed of radiation components or a blend of radiation and aerodynamic components demonstrated superior performance. Conversely, datasets primarily composed of aerodynamic components exhibited enhanced performance on a daily timescale. Overall, GA-ELM outperformed the other models and was thus recommended for ETr estimation at all timescales. The findings emphasize the significance of ML models in accurately estimating ETr across varying temporal resolutions, crucial for effective water management, water resources, and agricultural planning.},

note = {Number: 1 

Publisher: Multidisciplinary Digital Publishing Institute},

keywords = {advective environments, aerodynamic components, genetic algorithm, Machine Learning, radiation components, reference evapotranspiration},

pubstate = {published},

tppubtype = {article}

}

@article{sapkota_integrating_2024,

title = {Integrating YOLO11 and Convolution Block Attention Module for Multi-Season Segmentation of Tree Trunks and Branches in Commercial Apple Orchards},

author = {Ranjan Sapkota and Manoj Karkee},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {arXiv preprint arXiv:2412.05728},

keywords = {},

pubstate = {published},

tppubtype = {article}

}