Agricultural Pest and Disease Detection in Banana Plant
DOI:
https://doi.org/10.18311/jmmf/2022/31992Keywords:
Image processing, TensorFlow lite, RGB 3D Color model, gray scale image, Object detectionAbstract
Agriculture is the primary source in providing food for the entire nation. Consequently, agriculture is the fundamental origin of food supply. The contributions of agriculture include increase in employment opportunity and economy of the nation. According to IBEF, in India 58% of entire population depends on agriculture as their main occupation. Currently 81.1% of the total agricultural production is produced by livestock farmers. There will be 50% of loss in yield because of pest and disease. The disease in the plant excitants farmers to use unsuitable pesticide which causes unfavorable consequences. This may lead to the reduction in soil and food quality. Besides it has an adverse impact on human life. Nevertheless, farmers are heedless of these effects. The diseases which are naturally created will cause a serious impact on yields also it will bring down the quality of the food and soil. The symptoms which can cause the less yield are infinitesimal and because of the less human vision potentiality it is difficult to recognize the disease. Plant diseases will require genuine identification and proper categorization of the crops. The developed advanced methodology will identify the diseases, percentage of spread area, pesticide name with the quantity of the pesticide require to heal particular disease using image processing technique.
Downloads
Metrics
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Barbedo, J.G.A. and Castro, G.B. “Influence of image quality on the identification of psyllids using convolutional neural networks”, Biosyst. Eng., Vol.182, pp. 151–158, 2019. DOI: https://doi.org/10.1016/j.biosystemseng.2019.04.007
Dawei et al., “Recognition pest by image- based transfer learning”, J. Sci. Food Agric., Vol. 99, pp. 4524–4531, 2019. DOI: https://doi.org/10.1002/jsfa.9689
Ferentinos and Konstantinos P., “Deep learning models for plant disease detection and diagnosis,” Computers and Electronics in Agriculture, vol. 145, pp. 311-318, 2018. DOI: https://doi.org/10.1016/j.compag.2018.01.009
Jiang et al.,” Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks”, Volume: 7, pp. 06 May 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2914929
Jiao et al., “An anchor-free convolutional neural network for multi-categories agricultural pest detection”, Comput. Electron. Agric., Vol.174, pp. 505-522, 2020. DOI: https://doi.org/10.1016/j.compag.2020.105522
Kamilaris et al., “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70-90, 2018. DOI: https://doi.org/10.1016/j.compag.2018.02.016
Liu, H and Chahl, J.S.,” A multispectral machine vision system for invertebrate detection on green leaves”, Comput. Electron. Agric., Vol. 150, pp. 279–288, 2018. DOI: https://doi.org/10.1016/j.compag.2018.05.002
Moses et al., “Identification and quantification of major insect pests of rice and their natural enemies,” Current Journal of Applied Science and Technology, vol. 32, no. 2, pp. 1–10, 2019. DOI: https://doi.org/10.9734/CJAST/2019/46433
Krishnaswamy et al., “Tomato crop disease classification using pre trained deep learning algorithm”, Procedia computer science, vol. 133, pp. 1040-1047,2018. DOI: https://doi.org/10.1016/j.procs.2018.07.070
Rather and Deo, “Inheritance pattern and gene action of brown planthopper (Nilaparvata lugens Stäl.) resistance in newly identified donors of rice (Oryza sativa L.),” Cereal Research Communications, vol. 46, no. 4, pp. 679–685, 2018. DOI: https://doi.org/10.1556/0806.46.2018.037
Shen, Y et al., “Detection of stored-grain insects using deep learning”, Comput. Electron. Agric., Vol. 145, pp. 319–325, 2018. DOI: https://doi.org/10.1016/j.compag.2017.11.039
Srinivasan et al., “Development and validation of an integrated pest management strategy for the control of major insect pests on yard-long bean in Cambodia,” Crop Protection, vol. 116, no. 2, pp. 82–91, 2019. DOI: https://doi.org/10.1016/j.cropro.2018.10.015
Y. Oo and Htun, “Plant Leaf Disease Detection and Classification using Image Processing”, International Journal of Research and Engineering, vol. 5, no. 9, pp. 516-523, 2018. DOI: https://doi.org/10.21276/ijre.2018.5.9.4
Yue et al., “Deep recursive super resolution network with Laplacian Pyramid for better agricultural pest surveillance and detection”, Comput. Electron. Agric. Vol. 150, pp.26–32, 2018. DOI: https://doi.org/10.1016/j.compag.2018.04.004