Early Detection Of Nutrient Deficiency In Plants Using Convolutional Neural Algorithm Network (CNN) Algorithm Based On Leaf Image Processing

Kurniawati Tamsir, Ema Utami

Abstract


Precision agriculture is one of the modern solutions to increase the efficiency and yield of agricultural production. This study proposes an ensemble model based on ResNet , DenseNet , and EfficientNet to detect nutrient deficiencies in lettuce plants, especially nitrogen, phosphorus, and potassium. This model combines the advantages of deep learning architecture with a weighted average ensemble approach to produce more accurate and reliable predictions. Experiments were conducted on a lettuce plant image dataset covering various nutrient deficiency conditions. The test results show that the proposed ensemble model achieves an accuracy of 1.0000 (100%) , indicating excellent performance in identifying nutrient deficiency symptoms. The advantage of this model lies in the unique combination of features obtained from each constituent model, which complement each other in producing the final prediction. This study proves the great potential of deep learning in supporting precision plant nutrient management, with practical applications that have the potential to reduce the time and cost of monitoring in the field. For further development, it is recommended to test this model on a larger and more varied dataset to improve generalization to various field conditions


Keywords


Deep Learning, Ensemble Learning, Resnet, Densenet, Efficientnet, Precision Agriculture, Plant Nutrient Deficiencies.

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References


Akhtar, N., et al. (2024). "Internet of Things Assisted Plant Disease Detection and Crop Management using Deep Learning for Sustainable Agriculture". IEEE Journals & Magazines. https://ieeexplore.ieee.org/document/10522672 .

Istiqomah, N., & Murinto, M. (2024). Classification of Rice Plant Diseases Based on Leaf Images Using Convolutional Neural Network (CNN). JSTIE (Jurnal Sarjana Teknik Informatika) (E-Journal) , 12 (1),18.

Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., & Tang, X. (2019). Residual attention network for image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41 (10), 2349–2364. https://doi.org/10.1109/TPAMI.2018.2858826 .

Lili Ayu Wulandhari. et al. (2019).” Plant Nutrient Deficiency Detection Using Deep Convolutional Neural Network”. Icic International 2019.

Zhao, H., Gallo, O., Frosio, I., & Kautz, J. (2019). Loss functions for image restoration with neural networks. IEEE Transactions on Computational Imaging, 5 (1), 37–48. https://doi.org/10.1109/TCI.2018.2886931

Tan, M., & Le, Q. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning (ICML) , 6105–6114. https://arxiv.org/abs/1905.11946

Kim, D., Heo, B., & Han, D. (2024). DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs. arXiv preprint arXiv:2403.19588 . https://arxiv.org/abs/2403.19588

Ju, R.-Y., Lin, T.-Y., & Chiang, J.-S. (2021). New Pruning Method Based on DenseNet Network for Image Classification. arXiv preprint arXiv:2108.12604 . https://arxiv.org/abs/2108.12604

Zhang, C., Benz, P., Argaw, D.M., Lee, S., Kim, J., Rameau, F., Bazin, J.-C., & Kweon, I.S. (2020). ResNet or DenseNet? Introducing Dense Shortcuts to ResNet. arXiv preprint:2010.12496 . https://arxiv.org/abs/2010.12496

Jiang, S., Ma, Y., & Wang, H. (2021). Deep learning for plant stress classification using CNNs. Computers and Electronics in Agriculture , 190, 106429. https://doi.org/10.1016/j.compag.2021.106429

Zhou, T., Han, G., & Wang, Y. (2020). A CNN-based framework for plant disease recognition. Computers and Electronics in Agriculture , 177, 105713. https://doi.org/10.1016/j.compag.2020.105713

Chen, J., Hou, Z., & Zhang, Z. (2019). Plant stress detection using ResNet. Frontiers in Plant Science , 10, 1341. https://doi.org/10.3389/fpls.2019.01341

Jiang, C., Jiang, C., Chen, D., & Hu, F. (2021). Densely Connected Neural Networks for Nonlinear Regression. arXivpreprint:2108.00864 . https://arxiv.org/abs/2108.00864

Wang, H., Li, M., & Sun, Y. (2019). A comparative study on deep learning for plant classification. Expert Systems with Applications , 124, 346–354. https://doi.org/10.1016/j.eswa.2019.01.010

Li, X., Yang, Q., & Wang, Z. (2020). Plant leaf using classification transfer learning and CNNs. Computers and Electronics in Agriculture , 170, 105232. https://doi.org/10.1016/j.compag.2020.105232

Krešo, I., Krapac, J., & Šegvić, S. (2019). Efficient Ladder-style DenseNets for Semantic Segmentation of Large Images. arXivpreprint:1905.05661 . https://arxiv.org/abs/1905.05661

Abai, Z., & Rajmalwar, N. (2019). DenseNet Models for Tiny ImageNet Classification. arXiv preprint arXiv:1904.10429 . https://arxiv.org/abs/1904.10429

Huang, G., Liu, Z., Pleiss, G., Van Der Maaten, L., & Weinberger, K. Q. (2020). Convolutional Networks with Dense Connectivity. arXiv preprint arXiv:2001.02394 . https://arxiv.org/abs/2001.02394

Ju, R.-Y., Lin, T.-Y., & Chiang, J.-S. (2021). New Pruning Method Based on DenseNet Network for Image Classification. arXivpreprint:2108.12604 . https://arxiv.org/abs/2108.12604

Zhang, C., Benz, P., Argaw, D.M., Lee, S., Kim, J., Rameau, F., Bazin, J.-C., & Kweon, I.S. (2020). ResNet or DenseNet? Introducing Dense Shortcuts to ResNet. arXiv preprintarXiv:2010.12496 . https://arxiv.org/abs/2010.12496

Zhang, Y., Wu, X., & Wang, W. (2020). A comprehensive review of deep learning-based plant disease detection techniques. Computers and Electronics in Agriculture , 175.105622. https://doi.org/10.1016/j.compag.2020.105622

Jiang, C., Jiang, C., Chen, D., & Hu, F. (2021).Densely Connected Neural Networks for Nonlinear Regression. arXivpreprint:2108.00864 . https://arxiv.org/abs/2108.00864

Simarmata, AM, Salim, P., & Waruwu, NJ (2022). Densenet Architecture Implementation for Organic and Non-Organic Waste. Sinkron: Journal and Research of Informatics Engineering, 7 (4).

Susanto, A., Sari, CA, Rachmawanto, EH, Mulyono, IUW, & Yaacob, NM (2021). A Comparative Study of Javanese Script Classification with GoogleNet, DenseNet, ResNet, VGG16 and VGG19. Sinkron: Journal and Research of Informatics Engineering, 7 (2).

Yu, J., Jiang, S., & Zhang, X. (2021). Ensemble deep learning for plant nutrient deficiency classification. Computers and Electronics in Agriculture , 186, 106139. https://doi.org/10.1016/j.compag.2021.106139

Li, W., Chen, J., & Zhao, X. (2022). Plant disease identification using CNN-based frameworks. Plant Methods , 18(5), 1–12. https://doi.org/10.1186/s13007-022-00766-6

Dosovitskiy, A., Beyer, L., & Kolesnikov, A. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929




DOI: https://doi.org/10.33387/protk.v12i2.9522

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