Abstract
Crops are an integral part of the agricultural industry in South Asian countries as they have fertile soil and advantageous weather conditions. However, the crops suffer from various plant diseases that influence the quality of the crops, which can be due to the lack of proper diagnosis and/or inefficient and untimely diagnoses of crop diseases. Diagnostic approaches of crop diseases are varied. Some can be diagnosed grossly, and some need especial laboratory efforts. Gross diagnosis approach is quite inaccurate and sometimes tricky. On the other hand, laboratory diagnosis is a time-consuming process and quite costly. In this project we developed an automated image processing method to enhance efficiency and accuracy in the gross assessment. This process accelerates the diagnosis process and can be considered as a preliminary method for diagnosis. We utilized deep learning to detect diseases in tomato crops using leaf images. The process involves building a convolutional neural network using a pre-trained VGG16 model that pre-processes the images according to its requirements and performs segmentation on images before training and testing the data. The model obtained an accuracy of 95%, taking only 30 min to run, train, test, and classify the 18,160 images to their respective 10 classes thus being very time-efficient and did not need a laptop with higher power processor, which makes the model accessible for other devices. Due to the low complexity of the model, it can be implemented on smaller devices without the need of a fast processor.
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References
Kitzes, J., et al.: Shrink and share: humanity’s present and future Ecological Footprint. Philos. Trans. R. Soc. B: Biol. Sci. 363(1491), 467–475 (2007)
FAO: How to Feed the World in 2050. Food and Agriculture Organization of the United Nations, Rome, Italy (2009). https://www.fao.org/fileadmin/templates/wsfs/docs/expert_paper/How_to_Feed_the_World_in_2050.pdf. Accessed 10 Jan 2022
Sujatha, R., Chatterjee, J.M., Jhanjhi, N.Z., Brohi, S.N.: Performance of Deep Learning vs machine learning in plant leaf disease detection. Microprocess. Microsyst. 80, 103615 (2021)
Palmgren, M., et al.: Are we ready for back-to-nature crop breeding? Trends Plant Sci. 20(3), 155–164 (2015)
Gunarathna, M.M., Rathnayaka, R.M.K.T., Kandegama, W.M.W.: Identification of an efficient deep leaning architecture for tomato disease classification using leaf images. J. Food Agric. 13(1), 33 (2020)
Malik, A.: Power Crisis in Pakistan: A Crisis of Governance? Pakistan Institute of Development Economics, Islamabad (2012)
Shrivastava, V., Pradhan, M., Minz, S., Thakur, M.: Rice plant disease classification using transfer learning of deep convolution neural network. ISPRS. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W6, pp. 631–635 (2019)
Phadikar, S., Sil, J., Das, A.K.: Rice diseases classification using feature selection and rule generation techniques. Comput. Electron. Agric. 90, 76–85 (2013)
Li, L., Zhang, S., Wang, B.: Plant disease detection and classification by deep learning – a review. IEEE Access 9, 56683–56698 (2021)
Rehman, A., et al.: Economic perspectives of major field crops of Pakistan: an empirical study. Pac. Sci. Rev. B: Hum. Soc. Sci. 1, 145–158 (2016)
Krishnaswamy Rangarajan, A., Purushothaman, R.: Disease classification in eggplant using pre-trained VGG16 and MSVM. Sci. Rep. 10, 2322 (2020)
Ramesh, S., et al.: Plant disease detection using machine learning. In: International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C), pp. 41–45 (2018)
Ramesh, S., Vydeki, D.: Rice Blast disease detection and classification using machine learning algorithm. In: 2nd International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE), pp. 255–259 (2018)
Ashqar, B., Abu-Naser, S.: Image-based tomato leaves diseases detection using deep learning. Int. J. Acad. Eng. Res. 2, 10–16 (2018)
Ahmed, K., Shahidi, T.R., Irfanul Alam, S.M., Momen, S.: Rice leaf disease detection using machine learning techniques. In: International Conference on Sustainable Technologies for Industry 4.0 (STI), pp. 1–5 (2019)
Kumari, C.U., Jeevan Prasad, S., Mounika, G.: Leaf disease detection: feature extraction with k-means clustering and classification with ANN. In: 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 1095–1098 (2019)
Panigrahi, K.P., et al.: Maize leaf disease detection and classification using machine learning algorithms. In: Advances in Intelligent Systems and Computing, pp. 659–669 (2020)
Ganatra, N., Patel, A.: A multiclass plant leaf disease detection using image processing and machine learning techniques. Int. J. Emerg. Technol. 11(2), 1082–1086 (2020)
Hassan, S.M., Maji, A.K., Jasiński, M., Leonowicz, Z., Jasińska, E.: Identification of plant-leaf diseases using CNN and transfer-learning approach. Electronics 10, 1388 (2021)
Geetharaman, G., Arunpandian, J.: Identification of plant leaf diseases using a 9-layer deep convolutional neural network. Comput. Electric. Eng. 76, 323–338 (2019)
Yang, H., Ni, J., Gao, J., Han, Z., Luan, T.: A novel method for peanut variety identification and classification by Improved VGG16. Sci. Rep. 11, 15756 (2021)
Softmax Function. https://deepai.org/machine-learning-glossary-and-terms/softmax-layer. Accessed 05 Jan 2022
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Tahir, H., Samimi, P. (2023). Tomato Plant Leaf Disease Identification and Classification Using Deep Learning. In: Saeed, F., Mohammed, F., Mohammed, E., Al-Hadhrami, T., Al-Sarem, M. (eds) Advances on Intelligent Computing and Data Science. ICACIn 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 179. Springer, Cham. https://doi.org/10.1007/978-3-031-36258-3_9
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