Agriculture not solely provides food for the human however it is additionally a giant supply for the economy of any country. Insects and pests harm the crops and, thus, square measure terribly dangerous for the general growth of the crop. Early tormentor detection may be a major challenge in agriculture field. The best means, to manage the tormentor infection is that the use of pesticides. However, the excessive use of pesticides square measure harmful to plants, animals still as masses associate degree automatic approach for early tormentor detection. The techniques of digital image process square measure extensively applied to agricultural science and it have nice perspective particularly within the plant protection field, that ultimately ends up in crops management. This paper deals with a new variety of early detection of pests‘ system. pictures of the leaves plagued by pests‘ square measure non heritable by employing a photographic camera. associate degree automatic system is needed which may not solely examine the crops to notice tormentor infestation however can also classify the sort of pests on crops. YOLO algorithmic rule is employed for tormentor detection and Support Vector Machine (SVM) is employed for classification of pictures with and while not pests supported the image options.

index Terms

YOLO and SVM Algorithm, Pest Detection, Plant protection, Machine Learning