Pest Detection Using Random Forest

In the agriculture sector, plants illnesses that cause crop destruction are to blame for financial losses. Whereas pesticides have been employed to alter agricultural yield, their excessive usage has a detrimental impact on the ecosystem. Determining the need for pesticides depends significantly on the ability to identify illnesses and distinguish them from nutritious deficiencies. The traditional approaches to identifying plant diseases need lengthy, laborious chemical procedures to be completed in a lab. Using machine learning and image processing techniques, this research describes an autonomous method for identifying however, it is still challenging to rapidly diagnose them since the required infrastructure is lacking in many regions of the world. Since the introduction of exact techniques, impressive accomplishments have been made within the area of categorizing images of leaves. This article makes use of Random Forest to discriminate between healthy and diseased leaves using the obtained data sets. Our plan calls for the creation of the dataset, features extraction, classifiers training, and categorization as execution activities. Ill and healthy leaf datasets were developed, and Random Forest was used to merge and train the datasets to categorize photographs of sick and healthy leaves. In conclusion, by employing machine learning to learn from those enormous data sets that are freely accessible, we may clearly detect the sickness existing in plants on a large scale.