Comparative Analysis of Smart Pesticide Recommendation System sing ML/AI

The agriculture sector is crucial in meeting societal requirements in a world where there is food insecurity and malnutrition. One of the biggest issues that crops confront is pests. Pests are creatures that live in undesirable places and harm humans, animals, plants, etc. Pests have the potential to harm plants and impede growth. The quantity and quality of the crops are directly impacted. Pre-and post-harvest pests and illnesses are thought to be responsible for at least 30 to 40% of the world’s agricultural output being lost. In an effort to manage or control diseases and pests, humans also devote a lot of time and resources to developing and applying new pesticides. These pesticides may have negative effects on the environment and people’s health when used improperly. Given that pest infestations significantly reduce crop quality and cause major crop damage, farmers have a tough time recognizing agricultural insects. The disadvantage of traditional insect identification is that it requires qualified taxonomists to rightly recognize insects on the basis of physical traits. This article gives a comparative analysis of various methods for picture classification and pest identification. The article chooses the most effective method for classifying pest images and utilizes machine learning to suggest pesticides that are accessible in India that adhere to ISO criteria.