The current study’s results support the tendency of herbicide treated plots’ soil temperatures to have a more direct relationship with local air temperatures, whereas mulched plots were more insulated to ambient conditions . Generally, landscape fabric and woodchip mulch tended to perform similarly, having high August temperatures and the ability to retain higher soil temperatures later into the season. Herbicide treated plots had high August temperatures with less ability to retain temperatures later in the season, and tended to more directly follow ambient temperatures compared to plots receiving some kind of mulch. Indirectly, this may be beneficial for woody plants with a temperature dependent acclimation process for winter dormancy, and may partially explain the delay reported for bud-break in 2009, though no soil temperature data was taken during the spring . Alternatively, this effect may be deleterious if it shortens an already short growing season by delaying early vine growth, or delaying acclimation to winter in the fall, though these were not supported by the presented vine establishment data or bud hardiness data of this study.
To implement weed management strategies, managers need tools to help them distinguish crop from weeds and at times, weeds from other weeds. The latter is important in determining which combination of chemical, mechanical, and/or biological control techniques to use for weed control. In 2012, agricultural producers in the United States spent $13.7 billion for agricultural chemicals . Targeted chemical spraying reduces the amount of herbicide applied, thus decreasing cost and protecting the environment. Palmer amaranth, redroot pigweed, and velvet leaf infestations occur in soybean fields throughout the eastern U.S. The weeds produce numerous seeds and establish large populations in soybean fields,cannabis vertical farming thus reducing yields. Dense populations of Palmer amaranth and red root pig weeds can damage agricultural equipment during harvesting. Populations of Palmer amaranth and red root pig weed have become resistant to some commonly used herbicides, such as glyphosate.Therefore, managers need effective tools to identify Palmer amaranth, red root pigweed, and velvet leaf to implement the correct strategy to control them. Remote sensing systems measuring light reflectance properties of plants have shown good potential for weed crop discrimination, including soybean and weed discrimination The success of remotely sensed data for crop and weed discrimination is dependent on the spectral sensitivity of the recording device and the algorithms used to process the data.
Vegetation indices derived from various mathematical combinations of hyperspectral and multispectral data have shown promise as tools for agricultural application, including determining canopy water content and water stress of crops assessing insect and disease infestations differentiating crops from weeds -, and assessing nutrient status of plants The advantages of using vegetation indices over single wavebands include enhancing differences in the spectral properties of plants, while diminishing the influence of relief, nonphotosynthesizing elements of plants, atmosphere, soil background, shadow, and viewing and illumination geometry on spectral data Gaps exist on using vegetation indices for crop weed discrimination and weed to weed discrimination, especially in soybean production systems. Random forest has gain popularity as a tool in numerous disciplines including remote sensing. It is a nonparametric ensemble method that uses numerous classification trees to predict or determine the class of an unknown sample , hence the name random forest.
The algorithm selects a random number of samples from the database provided by the analyst and then uses the samples to develop a decision tree. The same process is repeated for each tree in the forest; a different model is constructed for each tree. Samples randomly selected to derive the model for each tree are referred to as “in-bag” samples, and the samples not used to create the model are called “out-of-bag” samples. Typically, 2/3 and 1/3 of the samples are used as “in-bag” samples and “out-of-bag” samples, respectively. Based on the “in-bag” and “out-of-bag” concept, random forest does not need a separate testing set to evaluate the model. To test classification or prediction accuracy, decision trees in which a sample is “out-of-bag” are predicted or classified with trees in which the sample is not “in-bag”.