DETECTION AND CLASSIFICATION DISEASE OF CITRUS FRUIT
Authors: PRATIK GOSAVI, GANESH JADHAV, MANALI PATIL, JAGRUTI DAHIVADKAR, PROF.KUNAL R AHIRE
Full-text Research PDF File: View | Download
Abstract: Citrus shops similar as bomb are substantially affected by citrus canker complaint which affects the fruit product of the shops. Beforehand canker complaint identifying evidence is one of the worri-some answers for expanding the factory generation. former styles intend to fete and order the infection sickness precisely from the told splint filmland by embracing picture running styles to distinguish fac-tory splint affections from motorized filmland. In proposed design, an image recognition system of citrus conditions grounded on deep literacy is proposed. We erected a citrus image data set including six common citrus conditions. The deep literacy network is used to train and learn these images, which can effectively identify and classify crop conditions. In the trial, we use Deep Learning model as the primary network and compare it with other network models in the aspect of speed, model size, delica-cy. Results show that our system reduces the vaticination time consumption and model size while keeping a good bracket delicacy. Eventually, we bandy the significance of using machine literacy to identify and classify agrarian conditions in terminal, and put forward applicable suggestions.
Keywords: Machine Learning, Image Processing, Segmentation, Deep CNN.
Paper Id: 230175
Published On: 2023-05-27
Published In: Volume 11, Issue 3, May-June 2023
Cite This: DETECTION AND CLASSIFICATION DISEASE OF CITRUS FRUIT - PRATIK GOSAVI, GANESH JADHAV, MANALI PATIL, JAGRUTI DAHIVADKAR, PROF.KUNAL R AHIRE - IJIRMPS Volume 11, Issue 3, May-June 2023.