Harnessing Deep Learning for Plant Nutrition: VGG Architecture for Precision Detection of Nutrient Deficiency
Keywords:
VGG-19, RESTNET50V2, Random Search CVAbstract
The precise management of plant nutrition is paramount for ensuring optimal crop growth, yield, and overall agricultural sustainability. Traditional methods of assessing nutrient deficiency in plants frequently rely on labor-intensive, human error-prone manual observation. The use of deep learning algorithms has become a viable method for improving and automating the identification of nutrient shortages in crops in recent years. In this work, we examine the effectiveness of using deep learning, specifically the VGG (Visual Geometry Group) architecture, for precision detection of nutrient deficiency in plants. We leverage large datasets of plant images depicting various stages of nutrient deficiency to train and fine-tune the VGG model. Through extensive experimentation and analysis, we demonstrate the model's capability to accurately identify and diagnose nutrient deficiencies across different crops and growth stages. Furthermore, we explore potential future research directions, including fine-tuning pre-trained models, multi-scale analysis techniques, integration with sensor technologies, and enhancing model interpretability. Our findings highlight the transformative potential of deep learning in revolutionizing plant nutrition management, offering scalable and efficient solutions to enhance agricultural productivity and sustainability.
Downloads
References
Jayme Garcia Arnal Barbedo, Detection of nutrition deficiencies in plants using proximal images and machine learning: A review. 2019 Elsevier Computers and Electronics in Agriculture 162 (2019) 482–492.https://doi.org/10.1016/j.compag.2019.04.035
K.A.M. Han and U. Watchareeruetai, “Classification of nutrient deficiency in black gram using deep convolutional neural networks,” Proceedings of the 2019 16th IEEE International Joint Conference on Computer Science and Software Engineering (JCSSE), Oct 2019.
Tanya Makkar, et al., “A Computer Vision Based Comparative Analysis of Dual Nutrients (Boron, Calcium) Deficiency Detection System for Apple Fruit,” in Proceedings of 2018 4th IEEE International Conference on Computing Communication and Automation (ICCCA), July 2019.
Mustafa Merchant, Vishwajeet Paradkar, Meghna Khanna, and Soham Gokhale, “Mango Leaf Deficiency Detection Using Digital Image Processing and Machine Learning,” In Proceedings of 2018 3rd IEEE International Conference for Convergence in Technology (I2CT), April 2018.
Roy D. Tipones, and Jennifer C. Dela Cruz, “Nutrient Management with Automated Leaf Color Level Assessment and Ambient Light Neutralizer using SVM Classifier,” in Proceedings of 2019 IEEE International Symposium on Multimedia and Communication Technology (ISMAC), Jan 2020.
Susanto B. Sulistyo, W. L. Woo, S. S. Dlay and Bin Gao, Building a Globally Optimized Computational Intelligent Image Processing Algorithm for on-site inference of Nitrogen in Plants. In Proceeding of IEEE Intelligent Systems 2018. DOI:10.1109/MIS.2018.111144506.
Aditi Shah, Prerna Gupta, and Y. M. Ajgar, “Macro-Nutrient Deficiency Identification in Plants Using Image Processing and Machine Learning,” in Proceedings of 2018 3rd IEEE International Conference for Convergence in Technology (I2CT), November 2018.
José Sosa, Janidet Ramírez, Luis Vives, and Guillermo Kemper, “An Algorithm For Detection of Nutritional Deficiencies from Digital Images of Coffee Leaves Based on Descriptors and Neural Networks,” in Proceedings of 2019 XXII IEEE Precision-aware Learning Approach for Detection of Nutrient Deficiency in Plants18 Symposium on Image, Signal Processing and Artificial Vision (STSIVA), June 2019.
Choi Jae-Won, Tin Tran Trung, Tu Le Huynh Thien, Park Geon-Soo, Chien Van Dang, and Kim Jong-Wook, “A Nutrient Deficiency Prediction Method Using Deep Learning on Development of Tomato Fruits,” in Proceedings of 2018 IEEE International Conference on Fuzzy Theory and Its Applications (iFUZZY), July 2019.
Ukrit Watchareeruetai, Pavit Noinongyao, Chaiwat Wattanapaiboonsuk, Puriwat Khantiviriya, Sutsawat Duang, “Identification of Plant Nutrient Deficiencies Using Convolutional Neural Networks,” in Proceedings of 2018 IEEE International Electrical Engineering Congress (iEECON), May 2019.
Kestrilia R. Prilianti, Ivan C. Onggara, Marcelinus A.S. Adhiwibawa, Tatas H.P. Brotosudarmo, and Syaiful Anam, “Multispectral Imaging and Convolutional Neural Network for Photosynthetic Pigments Prediction,” in Proceedings of 2018 5th IEEE International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), July 2019.
Itamar F. Salazar-Reque, Adison G. Pacheco, Ricardo Y. Rodriguez, Jinmy G. Lezama, and Samuel G. Huamán, “An image processing method to automatically identify Avocado leaf state,” in Proceedings of 2019 XXII IEEE Symposium on Image, Signal Processing and Artificial Vision (STSIVA), June 2019.
Rayner Harold Montes Condori, Liliane Maria Romualdo, Odemir Martinez Bruno, Pedro Henrique de Cerqueira Luz, “Comparison between Traditional Texture Methods & Deep Learning Descriptors for Detection of Nitrogen Deficiency in Maize Crops” in Proceedings of 2017 IEEE Workshop of Computer Vision. DOI 10.1109/WVC.2017.00009.
Siti Rahayu Md Amin, Roslag Awang, “Automated Detection of Nitrogen Status on Plants: Performance of Image Processing Techniques” in Proceeding of 2018 IEEE 16th Student Conference on Research & Development (SCOReD) Nov 2018.
Kadipa Aung Myo Han and Ukrit Watchareeruetai, “Black Gram Plant Nutrient Deficiency Classification in Combined Images Using Convolutional Neural Network,” in Proceedings of 8th IEEE International Electrical Engineering Congress (iEECON), April 2020.
Arief Rais Bahtiar, Pranowo, Albertus Joko Santoso, and Jujuk Juhariah, “Deep Learning Detected Nutrient Deficiency in Chili Plant,” in Proceedings of 2020 8th IEEE International Conference on Information and Communication Technology (ICoICT), 2020.
Nurbaity Sabri, Nurul Shafekah Kassim, Shafaf Ibrahim, Rosniza Roslan, Nur Nabilah Abu Mangshor, Zaidah Ibrahim, “Nutrient deficiency detection in maize (Zea mays L.) leaves using image processing” 2020 International Journal of Artificial Intelligence (IJ-AI), Vol. 9, No. 2, June 2020, pp. 304-309 ISSN: 2252- 8938, DOI: 10.11591/ijai.v9.i2.pp304-309.
Prabira Kumar Sethy, Nalini Kanta Barpanda, Amiya Kumar Rath, Santi Kumari Behera, “Nitrogen Deficiency Prediction of Rice Crop Based on Convolutional Neural Network”, 2020 Springer Journal of Ambient Intelligence and Humanized Computing, https://doi.org/10.1007/s12652-020-01938-8.
Trung-Tin Tran, Jae-Won Choi, Thien-Tu Huynh Le and Jong-Wook Kim, A Comparative Study of Deep CNN in Forecasting & Classifying the Macronutrient Deficiencies on Development of Tomato Plant. 2019 MDPI Appl. Sci. 9,1601; doi:10.3390/app9081601.
Lucas Prado Osco, Ana Paula Marques Ramos, Mayara Maezano Faita Pinheiro, Érika Akemi Saito Moriya, Nilton Nobuhiro Imai, et.al” A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements. 2020 MDPI Remote Sens.12,906; doi:10.3390/rs12060906.
Xin Xiong, Jingjin Zhang, Doudou Guo, Liying Chang, Danfeng Huang, Non- Invasive Sensing of Nitrogen in Plant using Digital Images & Machine Learning for Brassica Campestris ssp. Chinensis L. 2019 MDPI Sensors19,2448;doi:10.3390/s19112448
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
