Root morphology and anatomy are important plant traits that could potentially influence seedling vigour, resource acquisition and susceptibility to early‐season stress. Therefore, the objective of the current experiment was to evaluate the effects of cultivar and growth temperature on seedling root growth and anatomical characteristics in cotton. To address this objective, experiments were conducted with six modern cotton cultivars, expected to have differences in seedling vigour, grown under optimal (30/20°C) and suboptimal (20/15°C) day/night temperature regimes. Root morphology and taproot cross‐sectional anatomy were evaluated two weeks after planting. Cultivars with the most vigorously growing root systems produced 73% more secondary roots, 68% more total root length, 74% more surface area and 72% higher total root volume than the least vigorous cultivars. The cultivar with the greatest production of secondary roots also exhibited a somewhat uncommon hexarch arrangement of vascular bundles in taproot cross sections. Thus, we suggest that this difference in root anatomy may be a determinant of genotypic differences in lateral root development. In response to low temperature, taproot length, total root length, secondary root formation, root surface area and root volume declined substantially relative to optimal temperature conditions (35%–75% declines). This reinforces the need to ensure optimal temperature conditions at planting or to identify cultivars with improved performance under suboptimal early‐season conditions. Conversely, root diameter responded positively to low growth temperatures, and cold‐induced increases in root thickness were associated with increases in the number and cross‐sectional area of root cells.
Using UAV and multispectral images has contributed to identifying field variability and improving crop management through different data modeling methods. However, knowledge on application of these tools to manage peanut maturity variability is still lacking. Therefore, the objective of this study was to compare and validate linear and multiple linear regression with models using artificial neural networks (ANN) for estimating peanut maturity under irrigated and rainfed conditions. The models were trained (80% dataset) and tested (20% dataset) using results from the 2018 and 2019 growing seasons from irrigated and rainfed fields. In each field, plant reflectance was collected weekly from 90 days after planting using a UAV-mounted multispectral camera. Images were used to develop vegetation indices (VIs). Peanut pods were collected on the same dates as the UAV flights for maturity assessment using the peanut maturity index (PMI). The precision and accuracy of the linear models to estimate PMI using VIs were, in general, greater in irrigated fields with R2 > 0.40 than in rainfed areas, which had a maximum R2 value of 0.21. Multiple linear regressions combining adjusted growing degree days (aGDD) and VIs resulted in decreased RMSE for both irrigated and rainfed conditions and increased R2 in irrigated areas. However, these models did not perform successfully in the test process. On the other hand, ANN models that included VIs and aGDD showed accuracy of R2 = 0.91 in irrigated areas, regardless of using Multilayer Perceptron (MLP; RMSE = 0.062) or Radial Basis Function (RBF; RMSE = 0.065), as well as low tendency (1:1 line). These results indicated that, regardless of the ANN architecture used to predict complex and non-linear variables, peanut maturity can be estimated accurately through models with multiple inputs using VIs and aGDD. Although the accuracy of the MLP or RBF models for irrigated and rainfed areas separately was high, the overall ANN models using both irrigated and rainfed areas can be used to predict peanut maturity with the same precision.
AIM:In this paper, using a mathematical model, we will show that for special exchanged photons, the Hamiltonian of a collection of neurons tends to a constant number and all activities is stopped. These photons could be called as the dead photons. To this aim, we use concepts of Bio-BIon in Izhikevich Neuron model.METHODS:In a neuron, there is a page of Dendrite, a page of axon’s terminals and a tube of Schwann cells, axon and Myelin Sheath that connects them. These two pages and tube form a Bio-Bion. In a Bio-Bion, exchanging photons and some charged particles between terminals of dendrite and terminals of axon leads to the oscillation of neurons and transferring information. This Bion produces the Hamiltonian, wave equation and action potential of Izhikevich Neuron model. Also, this Bion determines the type of dependency of parameters of Izhikevich model on temperature and frequency and obtains the exact shape of membrane capacitance, resting membrane potential and instantaneous threshold potential.RESULTS:Under some conditions, waves of neurons in this BIon join to each other and potential shrinks to a delta function. Consequently, total Hamiltonian of the system tends to a constant number and system of neuron act like a dead system. Finally, this model indicates that all neurons have the ability to produce similar waves and signals like waves of the mind.CONCLUSION:Generalizing this to biology, we can claim that neurons out of the brain can produce signals of minding and imaging and thus mind isn’t confined to the brain.
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