DAVIS-Ag: A Synthetic Plant Dataset for Developing Domain-Inspired Active Vision in Agricultural Robots

In agricultural environments, viewpoint planning can be a critical functionality for a robot with visual sensors to obtain informative observations of objects of interest (e.g., fruits) from complex structures of plant with random occlusions. Although recent studies on active vision have shown some potential for agricultural tasks, each model has been designed and validated on a unique environment that would not easily be replicated for benchmarking novel methods being developed later. In this paper, hence, we introduce a dataset for more extensive research on Domain-inspired Active VISion in Agriculture (DAVIS-Ag). To be specific, we utilized our open-source "AgML" framework and the 3D plant simulator of "Helios" to produce 502K RGB images from 30K dense spatial locations in 632 realistically synthesized orchards of strawberries, tomatoes, and grapes. In addition, useful labels are provided for each image, including (1) bounding boxes and (2) pixel-wise instance segmentations for all identifiable fruits, and also (3) pointers to other images that are reachable by an execution of action so as to simulate the active selection of viewpoint at each time step. Using DAVIS-Ag, we show the motivating examples in which performance of fruit detection for the same plant can significantly vary depending on the position and orientation of camera view primarily due to occlusions by other components such as leaves. Furthermore, we develop several baseline models to showcase the "usage" of data with one of agricultural active vision tasks--fruit search optimization--providing evaluation results against which future studies could benchmark their methodologies. For encouraging relevant research, our dataset is released online to be freely available at: https://github.com/ctyeong/DAVIS-AgComment: 8 pages, 5 figures, 4 table

End-to-end deep learning for directly estimating grape yield from ground-based imagery

Yield estimation is a powerful tool in vineyard management, as it allows growers to fine-tune practices to optimize yield and quality. However, yield estimation is currently performed using manual sampling, which is time-consuming and imprecise. This study demonstrates the application of proximal imaging combined with deep learning for yield estimation in vineyards. Continuous data collection using a vehicle-mounted sensing kit combined with collection of ground truth yield data at harvest using a commercial yield monitor allowed for the generation of a large dataset of 23,581 yield points and 107,933 images. Moreover, this study was conducted in a mechanically managed commercial vineyard, representing a challenging environment for image analysis but a common set of conditions in the California Central Valley. Three model architectures were tested: object detection, CNN regression, and transformer models. The object detection model was trained on hand-labeled images to localize grape bunches, and either bunch count or pixel area was summed to correlate with grape yield. Conversely, regression models were trained end-to-end to predict grape yield from image data without the need for hand labeling. Results demonstrated that both a transformer as well as the object detection model with pixel area processing performed comparably, with a mean absolute percent error of 18% and 18.5%, respectively on a representative holdout dataset. Saliency mapping was used to demonstrate the attention of the CNN model was localized near the predicted location of grape bunches, as well as on the top of the grapevine canopy. Overall, the study showed the applicability of proximal imaging and deep learning for prediction of grapevine yield on a large scale. Additionally, the end-to-end modeling approach was able to perform comparably to the object detection approach while eliminating the need for hand-labeling

Novel algorithms for high-resolution prediction of canopy evapotranspiration in grapevine

Developing low-cost technology for custom water delivery to individual or small groups of plants is a critical next step to advance precision irrigation. Current systems for estimating evapotranspiration (ET), or plant water use, work on the scale of a full vineyard (e.g., 3–5 acres) or the scale of a single vine, but at a cost that prohibits monitoring past a small number of representative vines. To develop and evaluate low-cost ET sensors for individual grapevines, we used three head-pruned Zinfandel vines in pots and placed them on load cells to collect continuous weights indicative of actual ET. We mounted research-grade sensors for humidity, temperature, and wind speed on each vine and saved data at 2-minute intervals during three growing seasons. We developed three models based on first principles (Convective Mass Transfer or Mass Balance approaches) or simple correlations to predict actual single-plant ET from these data. We present here the results of a multi-year trial at the UC-Davis RMI vineyard to illustrate the performance of each of the models for ET estimation. Relative model performance was assessed by comparing model predictions to ground truth data provided by measurements from load cells–including assessments of estimated instantaneous ET rate, estimated cumulative water use over a one-hour window surrounding solar noon, and estimated cumulative water use over a full 24-hour period. The three algorithms developed consistently performed well, with single vine ET rate predictions showing a strong linear relationship with ground truth (range in r2 over three seasons CMT r2 = 0.61–0.86; MB r2 = 0.07–0.91; EM r2 = 0.57–0.92). The MB approach, which includes two measurements of relative humidity and temperature, was the most variable, likely due to the impact of sensor placement. In all seasons, we also examined the trend in the plant scaling factor found in each model, deemed As, which, based on model theory, is a function of vine size. Taken together, these results suggest that high-resolution irrigation (HRI) models are a promising new method for ET estimation at the single plant level

LCA and Forest Biorefining: Environmental Assessment of a Modified OSB Mill and an Integrated Partial Equilibrium Framework for Policy Analysis

Over the next decade, wood will likely experience substantial growth as an energy feedstock in the US driven largely by the national desire for energy independence and a cleaner fuel source. The two studies contained here within center on the topic of life cycle assessment (LCA) and its application to forest-based energy production. LCA is a tool for estimating resource consumption and environmental impacts associated with a product system. The first study is a technology-based LCA of an emerging forest biorefinery. More specifically, an LCA of wood panels, ethanol and acetic acid co-produced at an oriented strandboard (OSB) biorefinery is performed. This study applies an existing LCA methodology to inform the research and design process by identifying environmental strengths and weaknesses of the OSB biorefinery. Under the baseline assumptions, the OSB biorefinery performs well on toxicity-related measures compared to a conventional gasoline, acetic acid, and OSB system. Global warming potential (GWP), however, increases compared to the conventional system. Several alternative scenarios are formulated to account for uncertainty about the manufacturing process. Based on the most sensitive parameters a target production process is identified that offers substantial reductions in both toxicity and GWP measures. The second study addresses broader policy-level questions regarding the future of forest-based bioenergy consumption in the US: (1) What are the potential market effects and related environmental impacts of widespread adoption of forest-product biorefining and (2) How might various federal biomass restrictions alter these effects? A common economic technique called partial equilibrium (PE) modeling is integrated with LCA to address these questions. More specifically, the output from an existing PE model called the US Forest Products Module (USFPM) is characterized using life cycle inventory (LCI) data. The integrated framework developed is called USFPM-LCA. Potential economic and environmental effects of two competing policy definitions for renewable biomass are analyzed using USFPM-LCA: the Energy Independence and Security Act (EISA) of 2007 and the 2008 Farm Bill. The USFPM projections suggest that even when fuel feedstock demand rises dramatically, similar sources of wood are utilized under both EISA 2007 and Farm Bill 2008 definitions—primarily softwood pulpwood and logging residues from the southern US. Additionally, mill fuel residues will likely be used to some degree. Likewise, the subsequent impacts on domestic and international production of other forest products are similar for both definitions. After characterizing USFPM output using LCI data, three types of indirect greenhouse gas (GHG) impacts were identified. The first results from the substitution of ethanol for gasoline; the second from substituting natural gas for mill fuel residues; and the third due to changes in magnitude and location of production among all forest product sectors. Although substituting wood-based ethanol for gasoline has the potential to reduce GHGs by over 60%, if mill fuel residues are sold by manufacturers as fuel feedstock these environmental benefits could easily be offset. The indirect GHG impacts due to changes in the magnitude of production, or parallel product impacts, appear to relatively small at about 5-8% of gasoline\u27s life cycle impacts

Comparative life cycle assessment of biofuel produced in two forest product biorefineries

A comparative life cycle assessment (LCA) of bioethanol produced within kraft pulp and paper and oriental strand board (OSB) biorefineries was performed. The system boundary was from cradle-to-gate, including timber management and harvesting, feedstock extraction and panel production, and transportation. Regarding the OSB biorefinery, LCA data sources include secondary studies on timber harvesting and OSB manufacturing, laboratory-scale studies on hot water extraction of hemicellulose, and adjusted studies on bioethanol production. Results from an existing KPP LCA study by Bhander et al. are compared to novel results for the OSB biorefinery. Preliminary results are presented and future direction is discussed. This is an abstract of a paper presented at the AIChE 2010 Spring National Meeting (San Antonio, TX 3/21-25/2010)

Standardizing and Centralizing Datasets for Efficient Training of Agricultural Deep Learning Models

In recent years, deep learning models have become the standard for agricultural computer vision. Such models are typically fine-tuned to agricultural tasks using model weights that were originally fit to more general, non-agricultural datasets. This lack of agriculture-specific fine-tuning potentially increases training time and resource use, and decreases model performance, leading to an overall decrease in data efficiency. To overcome this limitation, we collect a wide range of existing public datasets for 3 distinct tasks, standardize them, and construct standard training and evaluation pipelines, providing us with a set of benchmarks and pretrained models. We then conduct a number of experiments using methods that are commonly used in deep learning tasks but unexplored in their domain-specific applications for agriculture. Our experiments guide us in developing a number of approaches to improve data efficiency when training agricultural deep learning models, without large-scale modifications to existing pipelines. Our results demonstrate that even slight training modifications, such as using agricultural pretrained model weights, or adopting specific spatial augmentations into data processing pipelines, can considerably boost model performance and result in shorter convergence time, saving training resources. Furthermore, we find that even models trained on low-quality annotations can produce comparable levels of performance to their high-quality equivalents, suggesting that datasets with poor annotations can still be used for training, expanding the pool of currently available datasets. Our methods are broadly applicable throughout agricultural deep learning and present high potential for substantial data efficiency improvements