lighting | Ag Data Commons

Data from: Plant strategies for maximizing growth during drought and drought recovery in Solanum melongena L. (eggplant)

This data represents the collection of physiological and biometric data of above- and below-ground plant traits in four species of Solanum melongena of Philippine origin (PHL 4841, PHL 2778, PHL 2789, and Mara). Half of the plants were subjected to significant water deficit, and half again of those deficit plants were allowed to recover after subsequent watering. This data is suitable to serve as a benchmark for trait values in S. melongena, as well as in studies of trait responses to terminal drought and episodic drought in agricultural settings.

Virtual Grower 3

Initially designed to help greenhouse growers determine heating costs and do simple simulations to figure out where heat savings could be achieved, it has slowly added features so that now, Virtual Grower can help not only identify those savings through different greenhouse designs, but predict crop growth, assist in scheduling, make real-time predictions of energy use, and see the impact of supplemental lighting on plant growth and development. In other words, the software can be a safety net and allow users to experiment with "what if" scenarios in a risk-free setting.

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PhotoSim

This program models the photosynthetic response of 13 floriculture crops to light, temperature, or carbon dioxide (CO2) and allows users to estimate the impact of adjusting their greenhouse environment. You can predict the impact on photosynthesis for different management changes (shading, supplemental high pressure sodium lighting, CO2 injection,or heating or cooling).

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Data from: Apple flower detection using deep convolutional networks

With the goal of automating bloom intensity estimation, a method a novel method for apple flower detection is presented in which a pre-trained convolutional neural network (CNN) is fine-tuned to become specially sensitive to flowers.

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