Abstract
Smart Farming with the transformative potential of Artificial Intelligence has emerged as a promising breakthrough in enhancing the agricultural productivity. The rapid in supercomputing facilities has redefined Deep Learning and its applications are seemingly limitless. It involves the use of deeper neural networks for greater learning capabilities eventually resulting in higher performance and precision. Its ability to discern patterns at higher calibre made it penetrate into almost all applications of computer vision including the field of Agriculture for Plant leaf disease detection, fruit counting, water management, weed and pest control. This research work deals with the prediction of Tomato leaf diseases based on image classification, using pre-trained deep convolutional networks. The accuracy of the model is evaluated using standard classification metrics namely Accuracy, Precision, Recall and F1-score. Furthermore, this paper makes a recommendation on the best optimizing function among three standard optimizers for classification of tomato leaf diseases based on performance metrics.
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References
Sangeetha, R., Mary Shanthi Rani, M.: Tomato leaf disease prediction using convolutional neural network. Int. J. Innov. Technol. Explor. Eng 9(1), 1348–1352 (2019)
Devi, M.K., Rani, M.M.S.: A comparative study of machine learning classifiers for diabetic retinopathy detection. In: Komanapalli, V.L.N., Sivakumaran, N., Hampannavar, S. (eds.) Advances in Automation, Signal Processing, Instrumentation, and Control. LNEE, vol. 700, pp. 735–742. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-8221-9_67
Rani, M.M.S., Shanmugavadivu, P.: 12. Deep learning based food image classification. In: Computational Intelligence, pp. 197–208. De Gruyter (2020)
Sangeetha, R., Mary Shanthi Rani, M.: A novel method for plant leaf disease classification using deep learning techniques. In: Gopi, E.S. (ed.) Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication. LNEE, vol. 749, pp. 631–643. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-0289-4_46
Sangeetha, R., Rani, M.M.S.: Tomato leaf disease prediction using transfer learning. In: Garg, D., Wong, K., Sarangapani, J., Gupta, S.K. (eds.) IACC 2020. CCIS, vol. 1368, pp. 3–18. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-0404-1_1
Geetharamani, G., Arun, P., Mohit, A., Suneet, K.G.: Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Comput. Elect. Eng. 76, 323–338 (2019)
Agarwal, M., Gupta, S., Biswas, K.K.: A new conv2d model with modified relu activation function for identification of disease type and severity in cucumber plant. Sustain. Comput. Inf. Syst. 30, 100473 (2021)
Agarwal, M., Gupta, S.K., Biswas, K.K.: Development of efficient CNN model for tomato crop disease identification. Sustain. Comput. Inf. Syst. 28, 100407 (2020)
Srivastava, S., Boyat, S., Sadistap, S.: A novel vision sensing system for tomato quality detection. Int. J. Food Sci. 2014, 1–11 (2014)
Sabrol, H., Kumar, S.: Fuzzy and neural network based tomato plant disease classification using natural outdoor images. Indian J. Sci. Technol. 9(44), 1–8 (2016)
Fuentes, A., Yoon, S., Kim, S., Park, D.: A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9), 2022 (2017)
Amara, J., Bassem, B., Alsayed, A.: A deep learning-based approach for banana leaf diseases classification. In: Datenbanksysteme für Business, Technologie und Web (BTW 2017)-Workshopband (2017)
Rangarajan, A.K., Purushothaman, R., Ramesh, A.: Tomato crop disease classification using pre-trained deep learning algorithm. Procedia Comput. Sci. 133, 1040–1047 (2018). https://doi.org/10.1016/j.procs.2018.07.070
Lu, Y., Yi, S., Zeng, N., Liu, Y., Zhang, Y.: Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267, 378–384 (2017)
Oppenheim, D., Shani, G.: Potato disease classification using convolution neural networks. Adv. Anim. Biosci. 8(2), 244–249 (2017)
Acknowledgement
The experiments are carried out at Advanced Image Processing Lab, Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), Dindigul & funded by DST-FIST.
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Sangeetha, R., Mary Shanthi Rani, M., Joseph, R. (2022). Optimized Deep Neural Network for Tomato Leaf Diseases Identification. In: Garg, D., Jagannathan, S., Gupta, A., Garg, L., Gupta, S. (eds) Advanced Computing. IACC 2021. Communications in Computer and Information Science, vol 1528. Springer, Cham. https://doi.org/10.1007/978-3-030-95502-1_42
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