Machine learning algorithms: Essential tools for crop yield estimation in modern agriculture

The agriculture industry is changing thanks to machine learning and artificial intelligence. Using very accurate machine learning algorithms, Crop yield predictions are being made. A greater level of production and efficiency in the agriculture sector is being made possible by the use of machine learning. Here are a few instances of agricultural production estimation using machine learning algorithms:

Assuming a linear connection between crop yield and the input variables such as temperature, rainfall, and fertilizer linear regression is a straightforward and popular approach. A baseline model for estimating production in the agriculture sector can be obtained by linear regression, yet it might not be able to represent the complex and nonlinear patterns in the data.

Based on specific parameters, such as the maximum or lowest value of a variable, decision trees are algorithms that divide the data into more manageable and homogenous groupings. Decision trees can give an intuitive and graphical depiction of the estimated crop production, but depending on the size and depth of the tree, they may be overfit or underfit.

Random forests are algorithms that take many decision trees and use an average or voting mechanism to combine their predictions. Because random forests can manage missing values and outliers and minimize the volatility and bias of individual trees, they can increase the resilience and accuracy of agricultural yield estimation.

Support vector machines are algorithms that choose the best hyperplane for classifying the data into distinct groups, such as high or low yield. Given their ability to handle high-dimensional and sparse data and their ability to apply various kernel functions to capture complicated and nonlinear patterns in the data, support vector machines can offer a potent and versatile model for estimating crop production.

Neural networks, which are composed of several layers of linked nodes or neurons that process and transmit information, are algorithms that simulate the composition and operation of the human brain. Because they can handle complicated and nonlinear patterns in the data and learn from them to alter their weights and parameters, neural networks can offer an advanced and adaptable model for estimating crop production.