Identification of Citrus Fruit Defect using Computer Vision System

Agronomy production accounts for a significant portion of the Indian economy. Since there is a high demand for fruits in the market, fruit disease detection is a critical component, the incorporation of computer vision systems in agriculture also deal with the identification and classification of fruit diseases. There is an increasing need for detecting flaws in various fruits, which is laborious and ineffective. The procedure identifies citrus fruit illnesses using several classifiers and assesses the accuracy and effectiveness of each algorithm. This is an involuntary recognition system and the procedure is both labor-intensive and cost-effective. This research work has performed a comparative analysis of several classifiers such as KNN, SVM, and DT by providing the image of a sick fruit as an input. By using the proposed classicification model, upto 97% accuracy can be obtained. The proposed system has major advantages such as user friendliness, good reliability etc. The primary goal of the proposed strategy is to increase the consumer knowledge about fruit illness and whether or not they should be consumed.