Machine Learning Models using Paprika Leaf Growth Forecast Based on Environmental and Energy Data

A Crop Growth Prediction Model Using Energy Data Based on Machine Learning in Smart Farms

“…This study has greenhouse climate variables that dataset correlated with warm summers and moderately cold winters. We calibrated paprika plant growth quality readings with a correlation between leaf growth, wind speed, dew point, input and output temperature, and CO 2 [ 26 ]. This paprika growth data gets a more efficient leaf growth level in the autumn and winter season because of the temperature.…”

“…It can perform well on a large database. The random forest gives a highly accurate output from the collection of decision trees [ 26 ]. Each decision tree draws the sample random data, and it predicts the accurate result at the end.…”

“…SVM is useful because it is quick and there is no danger of over-add-on the data. It provides accuracy even if the data is missing [ 26 , 33 ].…”

“…This will include your response and forecaster variables. Next, will qualify the system of your response variable [ 26 ]. We specify if nothing then the gradient boosting machine will try to guess.…”

“…Some commonly used distributions include “Bernoulli” logistic regression, “Gaussian” squared errors, “twist” t-distribution loss, and “poison” count outcomes. At last, we will specify the data and the ntree's statement [ 26 ]. By default, the gradient boosting machine model will assume 500 trees, which can provide a good estimate of our gradient boosting machine performance.…”