Digital Transformation in Agriculture, Detection of Diseases on Tomato Leaves with Artificial Intelligence

Yıl 2024, , 54 - 60, 29.06.2024

Kadir Ertaş Muhammed Yıldırım

https://doi.org/10.46572/naturengs.1495317

Öz

Agriculture is an essential factor in the development of a country. For the power coming from agriculture to be effective, it is necessary to get productive results from agriculture. One of the most significant features that increase productivity in agriculture is that agriculture is done consciously. Knowing what kind of message, the planted material gives according to its shape and condition is of great importance for the efficiency of agriculture. This study aimed to detect diseases on tomato leaves using artificial intelligence techniques. The study extracted features from tomato leaf images using ResNet-50, DarkNet-53, GoogleNet, AlexNet, and MobileNet-V2 models. In this study, dimensionality reduction was performed using the mRMR (Minimum Redundancy Maximum Relevance) method to reduce the number of features and increase the performance rate by selecting essential features. Support Vector Machines (SVM) algorithm was used to classify diseases on tomato leaves. As a result of the analysis, we obtained an accuracy value of 88.9% by combining ResNet-50, MobileNet-V2, and DarkNet-53 pre-trained network architectures, which have high accuracy rates. Afterward, dimensionality reduction was performed using mRMR on this combined data, and as a result, the success rate was measured as 93.1%. As a result of the literature review, it was concluded that this study showed an effective and high performance for tomato leaf disease detection.

Anahtar Kelimeler

Artificial Intelligence, Conscious Agriculture, CNN, mRMR, Classification

Kaynakça

• Ferentinos, K.P., Deep learning models for plant disease detection and diagnosis. Computers and electronics in agriculture, 2018. 145: p. 311-318.
• Arsenovic, M., et al., Solving current limitations of deep learning based approaches for plant disease detection. Symmetry, 2019. 11(7): p. 939.
• He, K., et al. Deep residual learning for image recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
• Redmon, J. and A. Farhadi, Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018.
• Krizhevsky, A., I. Sutskever, and G.E. Hinton, ImageNet classification with deep convolutional neural networks. Communications of the ACM, 2017. 60(6): p. 84-90.
• Szegedy, C., et al. Going deeper with convolutions. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
• Howard, A.G., et al., Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017.
• Mohanty, S.P., D.P. Hughes, and M. Salathé, Using deep learning for image-based plant disease detection. Frontiers in plant science, 2016. 7: p. 215232.
• Tan, L., J. Lu, and H. Jiang, Tomato leaf diseases classification based on leaf images: a comparison between classical machine learning and deep learning methods. AgriEngineering, 2021. 3(3): p. 542-558.
• Sibiya, M. and M. Sumbwanyambe, A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering, 2019. 1(1): p. 119-131.
• Al-Amin, M., D.Z. Karim, and T.A. Bushra. Prediction of rice disease from leaves using deep convolution neural network towards a digital agricultural system. in 2019 22nd international conference on computer and information technology (ICCIT). 2019. IEEE.
• Suryawati, E., et al. Deep structured convolutional neural network for tomato diseases detection. in 2018 international conference on advanced computer science and information systems (ICACSIS). 2018. IEEE.
• Agarwal, M., et al., ToLeD: Tomato leaf disease detection using convolution neural network. Procedia Computer Science, 2020. 167: p. 293-301.
• Cheng, X., et al., Pest identification via deep residual learning in complex background. Computers and Electronics in Agriculture, 2017. 141: p. 351-356.
• Liu, Y., et al. Flower classification via convolutional neural network. in 2016 IEEE International Conference on Functional-Structural Plant Growth Modeling, Simulation, Visualization and Applications (FSPMA). 2016. IEEE.
• Url, https://www.kaggle.com/datasets/mamtag/tomato-village.
• Gehlot, M., R.K. Saxena, and G.C. Gandhi, “Tomato-Village”: a dataset for end-to-end tomato disease detection in a real-world environment. Multimedia Systems, 2023. 29(6): p. 3305-3328.
• Peng, H., F. Long, and C. Ding, Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on pattern analysis and machine intelligence, 2005. 27(8): p. 1226-1238.
• Hearst, M.A., et al., Support vector machines. IEEE Intelligent Systems and their applications, 1998. 13(4): p. 18-28.
• Rawat, W. and Z. Wang, Deep convolutional neural networks for image classification: A comprehensive review. Neural computation, 2017. 29(9): p. 2352-2449.
• Harjoseputro, Y., Classifying Javanese Letters with Convolutional Neural Network (CNN) Method. 2018.
• Vapnik, V., The nature of statistical learning theory. 2013: Springer science & business media.