Category: Agricultural modernization with forecasting stages and machine learning

Here we have used three methods: We have used the parameters of mean absolute error (MAE) and mean square error (MSE) and compared different models. We can see from the given bar graph that a good result is shown by deep neural network and also the MAE and MSE have lowest value in the represented…

We know that agriculture is an important part of gross domestic product. This project cussed in the advantage of insurance companies so they have efficient insurance coverage. In this project, we have taken two test datasets. One is 2CSV files and another is image dataset. CSV file has lots of features like temperature, humanity, pressure…

Since this chapter is related to forecasting, we must have an idea of the development of agricultural stages so as to link these stages with machine learning. During the past 100 years, there was an indifference in the technology and most of the development is seen in the last 5 years (Figure 5.9). Hundred years ago About…

AI technique is used in different sectors from home to offices, and presently in agriculture also. In the agricultural field, the use of machine learning increases the productivity and quality of the crop. Retailers The seed retailers use this agriculture technology to churn the data to create better crops. While the pest control companies are…

Here, the agent learns the property of behaviors to the environment by the performance of the act and checks the results of action. Application of machine learning in different sectors:

Unsupervised learning is a machine learning model that finds the hint in the unlabeled data. So, in the previous example to identify what the circle is, what the triangle is, and what the square is, it looks at the dimensions of the figure or preferably it looks together at the number of corners. Several models…

Supervised learning is a method used to enable machines to classify/predict object problems or situations based on the data fed to the machine. Example Suppose we take data of circle, triangle and square labels in the labeled data. We have a training model and we know the answer. It is very important in supervised learning…

In machine learning agriculture, it learns through agricultural processes to derive methods. In machine learning we have those types of datasets which depend on examples. An individual example is also used in examples of datasets. Characteristics of these datasets are known as variables or helpers. These features can also be described as numerical, binary and…

Machine learning is developing with technologies of big data and other fast computer devices. In the field of agriculture, machine learning is creating some new opportunities to understand the different types of data processes related to environmental functions. Machine learning can be converted to a scientific formulation which will give the capability to learn without…

Machine learning technology is growing day by day in different sectors for analysis and prediction with the help of training data. So, training data are a key factor for machine learning. It tells us about the use of AI, so it is also used in agriculture. Before applying any data for prediction through machine learning,…