A Simple Deep Learning Application for Leaf Deficiency Classification
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PDF downloads: 7
Keywords:
Leaf Deficiency, Plant Deficiency, Deep Learning, Classification, MatlabAbstract
Leaf deficiencies caused by nutrient imbalances or environmental stresses significantly affect
plant growth, crop yield, and agricultural productivity. Traditional methods for identifying leaf deficiencies
rely on visual inspection or laboratory analysis, which are time-consuming, subjective, and unsuitable for
large-scale monitoring. In this study, a simple deep learning application is proposed for the automated
classification of leaf deficiencies using image data. The approach employs a convolutional neural network
(CNN) model designed to extract and learn discriminative features from leaf images corresponding to
common deficiencies such as nitrogen, potassium, magnesium, calcium, and dehydration stress. The dataset
consists of 10 images for each leaf deficiency class. The accuracy of the developed model is 72.22% and
an acceptable success rate is achieved with a limited dataset. The model is implemented in a simplified
framework to ensure ease of use and adaptability for agricultural practitioners with limited computational
expertise.
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Ali A, Ali S, Husnain M, Saad Missen MM, Samad A, Khan M. Detection of deficiency of nutrients in grape leaves using deep network. Math Probl Eng 2022;2022:3114525.
Ibrahim S, Hasan N, Sabri N, Abu Samah KAF, Rahimi Rusland M. Palm leaf nutrient deficiency detection using convolutional neural network (CNN). Int J Nonlinear Anal Appl 2022;13:1949–56.
Barbedo JGA. Detection of nutrition deficiencies in plants using proximal images and machine learning: A review. Comput Electron Agric 2019;162:482–92.
Simon EW. The symptoms of calcium deficiency in plants. New Phytol 1978;80:1–15.
Kadioglu A, Terzi R. A dehydration avoidance mechanism: leaf rolling. Bot Rev 2007;73:290–302.
Guo W, Nazim H, Liang Z, Yang D. Magnesium deficiency in plants: An urgent problem. Crop J 2016;4:83–91.
Zhao D, Reddy KR, Kakani VG, Reddy VR. Nitrogen deficiency effects on plant growth, leaf photosynthesis, and hyperspectral reflectance properties of sorghum. Eur J Agron 2005;22:391–403.
Hu W, Lu Z, Meng F, Li X, Cong R, Ren T, et al. The reduction in leaf area precedes that in photosynthesis under potassium deficiency: the importance of leaf anatomy. New Phytol 2020;227:1749–63.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436–44.
Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning. vol. 1. MIT press Cambridge; 2016.
Huang G-B. What are extreme learning machines? Filling the gap between Frank Rosenblatt’s dream and John von Neumann’s puzzle. Cognit Comput 2015;7:263–78.
Tappert CC. Who is the father of deep learning? 2019 Int. Conf. Comput. Sci. Comput. Intell., IEEE; 2019, p. 343–8.
Naskath J, Sivakamasundari G, Begum AAS. A study on different deep learning algorithms used in deep neural nets: MLP SOM and DBN. Wirel Pers Commun 2023;128:2913–36.
Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, et al. The history began from alexnet: A comprehensive survey on deep learning approaches. ArXiv Prepr ArXiv180301164 2018.
Majib MS, Rahman MM, Sazzad TMS, Khan NI, Dey SK. Vgg-scnet: A vgg net-based deep learning framework for brain tumor detection on mri images. IEEE Access 2021;9:116942–52.
Sarwinda D, Paradisa RH, Bustamam A, Anggia P. Deep learning in image classification using residual network (ResNet) variants for detection of colorectal cancer. Procedia Comput Sci 2021;179:423–31.
Kaur M, Mohta A. A review of deep learning with recurrent neural network. 2019 Int. Conf. Smart Syst. Inven. Technol., IEEE; 2019, p. 460–5.
Hua Y, Zhao Z, Li R, Chen X, Liu Z, Zhang H. Deep learning with long short-term memory for time series prediction. IEEE Commun Mag 2019;57:114–9.
Wang H, Li J, Li Z. AI-generated text detection and classification based on BERT deep learning algorithm. ArXiv Prepr ArXiv240516422 2024.
Zhang M, Li J. A commentary of GPT-3 in MIT Technology Review 2021. Fundam Res 2021;1:831–3.
Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E. Deep learning for computer vision: A brief review. Comput Intell Neurosci 2018;2018:7068349.
Otter DW, Medina JR, Kalita JK. A survey of the usages of deep learning for natural language processing. IEEE Trans Neural Networks Learn Syst 2020;32:604–24.
Noda K, Yamaguchi Y, Nakadai K, Okuno HG, Ogata T. Audio-visual speech recognition using deep learning. Appl Intell 2015;42:722–37.
Grigorescu S, Trasnea B, Cocias T, Macesanu G. A survey of deep learning techniques for autonomous driving. J F Robot 2020;37:362–86.
Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med 2019;25:24–9.
ŞAHiN E, Arslan NN, Özdemir D. Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning. Neural Comput Appl 2025;37:859–965.
Kim P. Matlab deep learning. With Mach Learn Neural Networks Artif Intell 2017;130:151.
Paluszek M, Thomas S. MATLAB machine learning toolboxes. Pract. MATLAB Deep Learn. A Proj. approach, Springer; 2020, p. 25–41.
Abbas S, Javed MT, Ali Q, Azeem M, Ali S. Nutrient deficiency stress and relation with plant growth and development. Eng. Toler. Crop plants against abiotic Stress, CRC Press; 2021, p. 239–62.
Römheld V. Diagnosis of deficiency and toxicity of nutrients. Marschner’s Miner. Nutr. High. plants, Elsevier; 2012, p. 299–312.
Kamilaris A, Prenafeta-Boldú FX. Deep learning in agriculture: A survey. Comput Electron Agric 2018;147:70–90.
Santos L, Santos FN, Oliveira PM, Shinde P. Deep learning applications in agriculture: A short review. Iber. Robot. Conf., Springer; 2019, p. 139–51.
Garg D, Alam M. Deep learning and IoT for agricultural applications. Internet Things Concepts Appl., Springer; 2020, p. 273–84.
Azimi S, Kaur T, Gandhi TK. A deep learning approach to measure stress level in plants due to Nitrogen deficiency. Measurement 2021;173:108650.
Ballester P, Araujo R. On the performance of GoogLeNet and AlexNet applied to sketches. Proc. AAAI Conf. Artif. Intell., vol. 30, 2016.
Al-Qizwini M, Barjasteh I, Al-Qassab H, Radha H. Deep learning algorithm for autonomous driving using googlenet. 2017 IEEE Intell. Veh. Symp., IEEE; 2017, p. 89–96.
