Agricultural Production

 Overview

In the real world of agricultural production, plant disease identification is crucial. It examines

the growth, overall health of the plant and ensures that the agricultural planting will operate

as usual and provide a successful crop. The advancement of computer vision technologies in

recent years has increased the potential applications for plant disease diagnosis. The early

diagnosis of diseases in plants plays a significant role in agriculture which uses Artificial

Intelligence technologies like machine learning and deep learning. These AI methods help in

detecting the disease caused in plants due to pests, aphids, poor nutrition in the soil, weather

parameters, etc., These methods extract meaning and patterns from data to make accurate

predictions. As in the current scenario, different plants are prone to a greater number of

diseases that can be caused by pathogenic organisms like fungi, bacteria, viruses, insects,

protozoa and parasitic plants, by employing ML techniques. Further, this chapter briefs potato

crop disease classifications such as bacterial, fungal, and viral diseases. Different computer

vision technologies like Artificial Intelligence (AI), Image Processing for analysing data and

predicting the disease are presented. Later on, problem formulation, data collection, and

outline of the thesis following the summary of the chapter are presented.