Effect of Pesticides on Crop Health Monitoring Based on NDVI Using Machine Learning

The aim of this research paper is to evaluate the crucial impact of pesticides. This control measure is an important agricultural input for improving crop health. However, due to the nature of agriculture, we need developments in machine learning which are urgently required to analyze it. The utilization of pesticides is important in preventing severe damage from pest and disease outbreaks. This work suggests the application of remote sensing finding devices like drones with multispectral imaging, which can be used to obtain Normalized Difference Vegetation Index (NDVI), with the aim of measuring the status of the crops' health as a function of the chlorophyll content of the plants. The model being presented is based on the use of IoT sensors, drone images, and machine learning algorithms to supervise the energy status of the plants in the field. Preliminary imaging data based on NDVI will highlight the images of drones conditions. However, in this research offers solutions to the problem of diverse resolution images that need to be registered in order to create a mosaic. In the end, the results will illustrate how pesticides impact the efficiency of crops, which will help in developing sustainable agriculture solutions that are environmentally friendly.