A Computer Based Image Processing Approach to Identify Rice Blast

• Published in: 2020 2nd International Conference on Sustainable Technologies for Industry 4.0 (STI) pp. 1 - 5
• Main Authors: Shahriar Sazzad, T. M., Anwar, Ayrin, Islam, Sabrin, Mila, Sumaiya Afroz, Jannat Oishwee, Sahrima, Anjum, Afia
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19-12-2020
• Subjects: agriculture; agrochemicals; available approaches; bacteria; Color; color image segmentation; color images; common rice plant disease; computerized approach; crops; diseases; fatal rice plant disease; Filter; fungal diseases rice blast; huge damage; image colour analysis; image processing; image processing steps; image segmentation; Mean-shift; microorganisms; Military computing; Morphology; Noise reduction; pesticides this deadly plant disease; plant diseases; proper care; proper pesticides; Rice Blast; rice blast disease; rice crops; rice plant diseases
• DOI: 10.1109/STI50764.2020.9350507

Fungus and bacteria are the main cause of rice plant diseases. Among all fungal diseases rice blast is considered as one of the most common and fatal rice plant disease. Without proper care and use of pesticides this deadly plant disease can cause huge damage for rice crops. Detection of rice blast disease at the early stage can help farmers to use proper pesticides and can save their crops and hence a computerized approach is necessary. Currently a good number of approaches available but none of them seems to provide a suitable solution in terms of identification accuracy. A suitable approach has been presented in this study where both input and output images are color images. Various image processing steps were considered in this study which includes enhancement, noise reduction, color image segmentation, and color features for identification. CNN classifier was applied for validation purpose. In compare to existing available approaches this study proposed approach is capable of providing better results in terms of accuracy which is 97.50%.