Defect Exposure in Citrus using Machine Learning

Authors: Akshay Somnath Gaikwad, Aditya Manish Limje, Aditya Snehalkumar, Vitthal Madhavrao Jawde, Prof. Smita T. Patil

Country: India

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Abstract: Orange (Citrus) plants such as lemon are mainly affected by citrus canker (causing the leaves to drop and unripe fruit to fall to the ground) disease which affects the fruit production of the plants. Early canker disease distinguishing proof is one of the troublesome answers for expanding the plant generation. Previous methods intends to recognize and order the infection malady precisely from the influenced leaf pictures by embracing picture handling methods to distinguish plant leaf sicknesses from computerized pictures. In proposed project, an image recognition method of citrus diseases based on deep learning is proposed. They have developed a citrus image dataset comprising six prevalent citrus diseases. The deep learning network is used to train and learn these images, which can effectively identify and classify crop diseases. In the experiment, a Deep Learning model serves as the primary network and is compared with other network models in terms of speed, model size, and accuracy. The results indicate that their method reduces prediction time and model size while maintaining a high classification accuracy. Finally, The results suggest that the method employed by the researchers reduces prediction time and model size while maintaining a high classification accuracy.

Keywords: Machine Learning, Image Processing, Segmentation, Deep CNN

Paper Id: 230654

Published On: 2024-05-22

Published In: Volume 12, Issue 3, May-June 2024

Cite This: Defect Exposure in Citrus using Machine Learning - Akshay Somnath Gaikwad, Aditya Manish Limje, Aditya Snehalkumar, Vitthal Madhavrao Jawde, Prof. Smita T. Patil - IJIRMPS Volume 12, Issue 3, May-June 2024.

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