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PremiseDespite the economic significance of insect damage to plants (i.e., herbivory), long‐term data documenting changes in herbivory are limited. Millions of pressed plant specimens are now available online and can be used to collect big data on plant–insect interactions during the Anthropocene. MethodsWe initiated development of machine learning methods to automate extraction of herbivory data from herbarium specimens by training an insect damage detector and a damage type classifier on two distantly related plant species (Quercus bicolorandOnoclea sensibilis). We experimented with (1) classifying six types of herbivory and two control categories of undamaged leaf, and (2) detecting two of the damage categories for which several hundred annotations were available. ResultsDamage detection results were mixed, with a mean average precision of 45% in the simultaneous detection and classification of two types of damage. However, damage classification on hand‐drawn boxes identified the correct type of herbivory 81.5% of the time in eight categories. The damage classifier was accurate for categories with 100 or more test samples. DiscussionThese tools are a promising first step for the automation of herbivory data collection. We describe ongoing efforts to increase the accuracy of these models, allowing researchers to extract similar data and apply them to biological hypotheses.
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Automated Classification of Power Plants by Generation Type
Generation type of power plant (e.g. steam, wind) is an important attribute in power grid and energy market studies such as bidding strategy, audit of generation mix, and accounting for load- generation matching. Recently, regional transmission organizations (RTOs) and independent system operators (ISOs) are increasingly redacting a wide range of grid and market data attributes to protect their participants’ business interests. Lack of this information can prevent important power grid research. We propose techniques to infer power plant generation types based on publicly-available market data. We develop and evaluate these techniques on data available from the Midcontinent Independent System Operator (MISO). Evaluation shows successful classification of power plants, achieving 100% precision and 99.5% recall for wind plants, and 91.7% overall accuracy. On the basis of generated power, our classification shows 100% precision and 99.8% recall for wind plants and 93.2% overall accuracy. Our ultimate goal is to generalize to a wide range of RTOs/ISOs. We explore three feature types (bid pattern, capability, and opera- tion), and evaluate their classification value for MISO. We also assess applicability to other RTOs/ISOs based on available market data. These studies inform the efficacy of the features for generation-type inference in other RTOs/ISOs.
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- PAR ID:
- 10155310
- Date Published:
- Journal Name:
- The Eleventh ACM International Conference on Future Energy Systems (e-Energy ’20)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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