Symptoms Based Image Predictive Analysis for Citrus Orchards Using Machine Learning Techniques: A Review
Article Sidebar
Main Article Content
Abstract
In Agriculture, orchards are the deciding factor in the country’s economy. There are many orchards, and citrus and sugarcane will cover 60 percent of them. These citrus orchards satisfy the necessity of citrus fruits and citrus products, and these citrus fruits contain more vitamin C. The citrus orchards have had some problems generating good yields and quality products. Pathogenic diseases, pests, and water shortages are the three main problems that plants face. Farmers can find these problems early on with the support of machine learning and deep learning, which may also change how they feel about technology. By doing this in agriculture, the farmers can cut off the major issues of yield and quality losses. This review gives enormous methods for identifying and classifying plant pathogens, pests, and water stresses using image-based work. In this review, the researchers present detailed information about citrus pathogens, pests, and water deficits. Methods and techniques that are currently available will be used to validate the problem. These will include pre-processing for intensification, segmentation, feature extraction, and selection processes, machine learning-based classifiers, and deep learning models. In this work, researchers thoroughly examine and outline the various research opportunities in the field. This review provides a comprehensive analysis of citrus plants and orchards; Researchers used a systematic review to ensure comprehensive coverage of this topic.
Article Details
References
S. Aravindhan, M. R. Tamjis, and C. Engineering, “Leaf Disease Detection by Using Convolutional Pretrained Model,” Int. J. Commun. Networks Inf. Secur., vol. 14, no. 1, pp. 114–120, 2022.
“IPM Strategies for Citrus — Vikaspedia,” Ministry of Electronics and Information Technology, Government of India initiative, 2023. https://vikaspedia.in/agriculture/crop-production/integrated-pest-managment/ipm-for-fruit-crops/ipm-strategies-for-citrus (accessed Aug. 03, 2023).
P. Kaur, S. Harnal, R. Tiwari, S. Upadhyay, S. Bhatia, and A. Mashat, “Recognition of Leaf Disease Using Hybrid Convolutional,” Sensors, vol. 22, 2022.
Y. R. P. and J. L. Usman Afzaal , Bhuwan Bhattarai, “An Instance Segmentation Model for Strawberry Diseases,” Sensors, vol. 21, 2021, doi: 10.3390/s21196565.
S. Sankaran, J. M. Maja, S. Buchanon, and R. Ehsani, “Huanglongbing (Citrus Greening) Detection Using Visible, Near Infrared and Thermal Imaging Techniques,” sensors, no. 13, pp. 2117–2130, 2013, doi: 10.3390/s130202117.
S. R. Anthay, A. Chokkalingam, K. B. Jeyashanker, and B. Natarajan, “An analysis on micronutrient deficiency in plant leaf and soil using digital image processing,” Indones. J. Electr. Eng. Comput. Sci., vol. 26, no. 1, pp. 568–575, 2022, doi: 10.11591/ijeecs.v26.i1.pp568-575.
“TNAU Agritech Portal?:: Crop Protection(Diseases),” Tamil nadu Agricultural University, 2015. https://agritech.tnau.ac.in/crop_protection/crop_prot_crop diseases_fruits_citrus.html (accessed Aug. 03, 2023).
P. M. Kwabena, B. A. Weyori, and A. A. Mighty, “Gabor capsule network for plant disease detection,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 10, pp. 388–395, 2020, doi: 10.14569/IJACSA.2020.0111048.
Z. Liu, X. Xiang, J. Qin, Y. Tan, Q. Zhang, and N. N. Xiong, “Image recognition of citrus diseases based on deep learning,” Comput. Mater. Contin., vol. 66, no. 1, pp. 457–466, 2021, doi: 10.32604/cmc.2020.012165.
S. F. Syed-ab-rahman, M. Hesam, and H. Mukesh, “Citrus disease detection and classification using end-to-end anchor-based deep learning model,” Appl. Intell., pp. 927–938, 2022.
S. Dananjayan, Y. Tang, J. Zhuang, C. Hou, and S. Luo, “Assessment of state-of-the-art deep learning based citrus disease detection techniques using annotated optical leaf images,” Comput. Electron. Agric., vol. 193, no. January, p. 106658, 2022, doi: 10.1016/j.compag.2021.106658.
S. Janarthan and S. Thuseethan, “Deep Metric Learning Based Citrus Disease Classification With Sparse Data,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.3021487.
“TNAU Agritech Portal?:: Crop Protection(Pest),” Tamil nadu Agricultural University, 2015. https://agritech.tnau.ac.in/crop_protection/crop_prot_crop_insectpest _-Citrus.html (accessed Aug. 03, 2023).
K. Wakamori, R. Mizuno, G. Nakanishi, and H. Mineno, “Multimodal neural network with clustering-based drop for estimating plant water stress,” Comput. Electron. Agric., vol. 168, no. November 2019, pp. 3–5, 2020, doi: 10.1016/j.compag.2019.105118.
L. Aversano, M. L. Bernardi, and M. Cimitile, “Water stress classification using Convolutional Deep Neural Networks,” J. Univers. Comput. Sci., vol. 28, no. 3, pp. 311–328, 2022, doi: 10.3897/jucs.80733.
A. Majid et al., “An integrated deep learning framework for fruits diseases classification,” Comput. Mater. Contin., vol. 71, no. 1, pp. 1387–1402, 2022, doi: 10.32604/cmc.2022.017701.
M. T. Li and S. H. Lee, “A Study on Small Pest Detection Based on a CascadeR-CNN-Swin Model,” Comput. Mater. Contin., vol. 72, no. 3, pp. 6155–6165, 2022, doi: 10.32604/cmc.2022.025714.
A. Elaraby, W. Hamdy, and S. Alanazi, “Classification of Citrus Diseases Using Optimization Deep Learning Approach,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/9153207.
Bobbinpreet et al., “MRMR Based Feature Vector Design for Efficient Citrus Disease Detection,” Comput. Mater. Contin., vol. 72, no. 3, pp. 4771–4787, 2022, doi: 10.32604/cmc.2022.023150.
U. Yasmeen et al., “Citrus diseases recognition using deep improved genetic algorithm,” Comput. Mater. Contin., vol. 71, no. 2, pp. 3667–3684, 2022, doi: 10.32604/cmc.2022.022264.
A. R. Luaibi, T. M. Salman, and A. H. Miry, “Detection of citrus leaf diseases using a deep learning technique,” Int. J. Electr. Comput. Eng., vol. 11, no. 2, pp. 1719–1727, 2021, doi: 10.11591/ijece.v11i2.pp1719-1727.
O. Csillik, J. Cherbini, R. Johnson, A. Lyons, and M. Kelly, “Identification of citrus trees from unmanned aerial vehicle imagery using convolutional neural networks,” Drones, vol. 2, no. 4, pp. 1–16, 2018, doi: 10.3390/drones2040039.
P. Sharma, Y. Paul, S. Berwal, and W. Ghai, “Performance analysis of deep learning CNN models for disease detection in plants using image segmentation,” Inf. Process. Agric., vol. 7, no. 4, pp. 566–574, 2020, doi: 10.1016/j.inpa.2019.11.001.
A. Umamageswari, S. Deepa, and K. Raja, “Measurement?: Sensors An enhanced approach for leaf disease identification and classification using deep learning techniques,” Meas. Sensors, vol. 24, no. September, p. 100568, 2022, doi: 10.1016/j.measen.2022.100568.
D. A. Noola and D. R. Basavaraju, “Revue d ’ Intelligence Artificielle Corn Leaf Disease Detection with Pertinent Feature Selection Model Using Machine Learning Technique with Efficient Spot Tagging Model,” Int. Inf. Eng. Technol. Assoc., vol. 35, no. 6, pp. 477–482, 2021.
S. I. Prottasha, S. Mohsin, and S. Reza, “A classification model based on depthwise separable convolutional neural network to identify rice plant diseases,” Int. J. Electr. Comput. Eng., vol. 12, no. 4, pp. 3642–3654, 2022, doi: 10.11591/ijece.v12i4.pp3642-3654.
J. Sujithra, “CRUN-Based Leaf Disease Segmentation and Morphological-Based Stage Identification,” Math. Probl. Eng., vol. 2022, 2022.
P. K. Jayapal et al., “applied sciences Analysis of RGB Plant Images to Identify Root Rot Disease in Korean Ginseng Plants Using Deep Learning,” Appl. Sci., vol. 12, 2022, doi: 10.3390/ app12052489.
M. A. Khan et al., “applied sciences Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection,” Appl. Sci., vol. 12, 2022, doi: 10.3390/app12020593.
J. Flor, V. Oraño, E. A. Maravillas, C. Jordan, and G. Aliac, “Classification of Jackfruit Fruit Damage Using Color Texture Features and Backpropagation Neural Network,” Int. Adv. Eng. Inf. Technol., vol. 10, no. 5, pp. 1813–1820, 2020.
T. Sharma and R. Mittal, “Classification of Plant leaf diseases?: A Deep Learning Method,” Int. J. Innov. Technol. Explor. Eng., no. 1, pp. 5195–5197, 2019, doi: 10.35940/ijitee.A9230.119119.
J. Fernandes et al., “Deep computer vision system for cocoa classification,” Multimed. Tools Appl., vol. 5, pp. 41059–41077, 2022.
A. K. Rangarajan and R. Purushothaman, “Disease Classification in Eggplant Using Pre-trained VGG16 and,” Sci. Rep., pp. 1–11, 2020, doi: 10.1038/s41598-020-59108-x.
T. A. Shriram, J. S. Raju, M. Hari, B. Santhi, and G. R. Brindha, “Farmer-Friendly Mobile Application for Automated Leaf Disease Detection of Real-Time Augmented Data Set using Convolution Neural Networks,” J. Comput. Sci., vol. 16, no. 2, 2020, doi: 10.3844/jcssp.2020.158.166.
M. Z. Ur Rehman et al., “Classification of citrus plant diseases using deep transfer learning,” Comput. Mater. Contin., vol. 70, no. 1, pp. 1401–1417, 2021, doi: 10.32604/cmc.2022.019046.
N. Khasawneh and E. Faouri, “applied sciences Automatic Detection of Tomato Diseases Using Deep Transfer Learning,” Appl. Sci., vol. 12, 2022, doi: 10.3390/ app12178467.
M. Javier, S. Baron, A. L. Gomez, J. Enrique, and E. Diaz, “applied sciences Supervised Learning-Based Image Classification for the Detection of Late Blight in Potato Crops,” Appl. Sci., vol. 12, 2022, doi: doi.org/10.3390/ app12189371.
K. Gangadharan, G. R. N. Kumari, D. Dhanasekaran, and K. Malathi, “Detection and classification of various pest attacks and infection on plants using RBPN with GA based PSO algorithm,” Indones. J. Electr. Eng. Comput. Sci., vol. 20, no. 3, pp. 1278–1288, 2020, doi: 10.11591/ijeecs.v20.i3.pp1278-1288.
S. Nalini et al., “Paddy Leaf Disease Detection Using an Optimized Deep Neural Network,” Comput. Mater. Contin., vol. 68, no. 1, 2021, doi: 10.32604/cmc.2021.012431.
G. Balram, “Crop Field Monitoring and Disease Detection of Plants in Smart Agriculture using Internet of Things,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 7, pp. 819–826, 2022.
J. Hang, D. Zhang, P. Chen, J. Zhang, and B. Wang, “Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network,” Sensors, vol. 19, pp. 1–14, 2019.
F. L. Feng Cao, “Fast Detection of Sclerotinia Sclerotiorum on Oilseed,” Sensors, vol. 18, 2018, doi: 10.3390/s18124464.
S. Islam, S. Sultana, F. Al Farid, N. Islam, and M. Rashid, “Multimodal Hybrid Deep Learning Approach to Detect Tomato Leaf Disease Using Attention Based Dilated Convolution,” Sensors, vol. 22, pp. 1–31, 2022, doi: 10.3390/s22166079.
M. A. Azim, M. K. Islam, M. Rahman, and F. Jahan, “An effective feature extraction method for rice leaf disease classification,” TELKOMNIKA Telecommun. Comput. Electron. Control, vol. 19, no. 2, pp. 463–470, 2021, doi: 10.12928/TELKOMNIKA.v19i2.16488.
M. V. Madhavan, D. N. H. Thanh, A. Khamparia, S. Pande, R. Malik, and D. Gupta, “Recognition and classification of pomegranate leaves diseases by image processing and machine learning techniques,” Comput. Mater. Contin., vol. 66, no. 3, pp. 2939–2955, 2021, doi: 10.32604/cmc.2021.012466.
S. K. Upadhyay and A. Kumar, “Traitement du Signal Early-Stage Brown Spot Disease Recognition in Paddy Using Image Processing and Deep Learning Techniques,” Int. Inf. Eng. Technol. Assoc., vol. 38, no. 6, pp. 1755–1766, 2021.
A. S. Zamani et al., “Performance of Machine Learning and Image Processing in Plant Leaf Disease Detection,” J. Food Qual., vol. 2022, pp. 1–7, 2022, doi: 10.1155/2022/1598796.
H. Li, C. Chen, S. Zhao, and Z. Lyu, “Color disease leaf image segmentation using NAMS superpixel algorithm,” Technol. Heal. Care, vol. 26, pp. 151–156, 2018, doi: 10.3233/THC-174525.
L. M. Tassis, E. Tozzi, D. Souza, and R. A. Krohling, “Original papers A deep learning approach combining instance and semantic segmentation to identify diseases and pests of coffee leaves from in-field images,” Comput. Electron. Agric., vol. 186, no. January, 2021, doi: 10.1016/j.compag.2021.106191.
T. Thi, H. Giang, T. Q. Khai, D. Im, and Y. Ryoo, “Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D Images,” Sensors, vol. 22, 2022.
M. Attique et al., “CCDF?: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features,” Comput. Electron. Agric., vol. 155, no. September, pp. 220–236, 2018, doi: 10.1016/j.compag.2018.10.013.
X. E. Pantazi, D. Moshou, and A. A. Tamouridou, “Automated leaf disease detection in di ff erent crop species through image features analysis and One Class Classi fi ers,” Comput. Electron. Agric., vol. 156, no. November 2018, pp. 96–104, 2019, doi: 10.1016/j.compag.2018.11.005.
V. G. Krishnan, J. Deepa, P. V. Rao, V. Divya, and S. Kaviarasan, “An automated segmentation and classification model for banana leaf disease detection,” J. Appl. Biol. Biotechnol., vol. 10, no. 01, pp. 213–220, 2022, doi: 10.7324/JABB.2021.100126.
A. Ennouni et al., “Early Detection and Classification Approach for Plant Diseases based on MultiScale Image Decomposition,” J. Comput. Sci., vol. 17, no. 3, 2021, doi: 10.3844/jcssp.2021.284.295.
C. B. Wetterich, R. Kumar, S. Sankaran, J. Belasque, R. Ehsani, and L. G. Marcassa, “A comparative study on application of computer vision and fluorescence imaging spectroscopy for detection of citrus huanglongbing disease in USA and Brazil,” Opt. InfoBase Conf. Pap., vol. 2013, no. Ccm, 2013, doi: 10.1364/fio.2013.jw3a.26.
U. Arasakumaran, S. D. Johnson, D. Sara, and R. Kothandaraman, “Traitement du Signal An Enhanced Identification and Classification Algorithm for Plant Leaf Diseases Based on Deep Learning,” Int. Inf. Eng. Technol. Assoc., vol. 39, no. 3, pp. 1013–1018, 2022.
D. N, L. R. P, G. G. AR, R. Kanadasan, and M. H.Alsharif, “SS symmetry Categorizing Diseases from Leaf Images Using a Hybrid Learning Model,” Symmetry (Basel)., vol. 13, pp. 1–14, 2021, doi: 10.3390/ sym13112073.
O. Almutiry et al., “A Novel Framework for Multi-Classification of Guava Disease,” Comput. Mater. Contin., vol. 69, no. 2, 2021, doi: 10.32604/cmc.2021.017702.
K. Prema and C. M. Belinda, “Smart Farming?: IoT Based Plant Leaf Disease Detection and Prediction using Deep Neural Network with Image Processing,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 9, pp. 3081–3083, 2019, doi: 10.35940/ijitee.I7707.078919.
S. Kaur, G. Babbar, and Gagandeep, “Image processing and classification, a method for plant disease detecion,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 9 Special Issue, pp. 868–871, 2019, doi: 10.35940/ijitee.I1139.0789S19.
F. N. Al-wesabi, A. A. Albraikan, A. M. Hilal, M. M. Eltahir, M. A. Hamza, and A. S. Zamani, “Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture,” Comput. Mater. Contin., vol. 70, no. 3, 2022, doi: 10.32604/cmc.2022.021299.
Wiharto, F. H. Nashrullah, E. Suryani, U. Salamah, N. P. T. Prakisya, and S. Setyawan, “Texture-based feature extraction using gabor filters to detect diseases of tomato leaves,” Rev. d’Intelligence Artif., vol. 35, no. 4, pp. 331–339, 2021, doi: 10.18280/ria.350408.
A. Di Nisio, F. Adamo, G. Acciani, and F. Attivissimo, “Fast Detection of Olive Trees A ff ected by Xylella,” Sensors, vol. 20, 2020.
S. Lei, J. Luo, X. Tao, and Z. Qiu, “Remote sensing detecting of yellow leaf disease of arecanut based on uav multisource sensors,” Remote Sens., vol. 13, no. 22, pp. 1–22, 2021, doi: 10.3390/rs13224562.
A. M. Abdu, M. M. Mokji, and U. U. Sheikh, “Machine learning for plant disease detection?: an investigative comparison between support vector machine and deep learning,” Int. J. Artif. Intell., vol. 9, no. 4, pp. 670–683, 2020, doi: 10.11591/ijai.v9.i4.pp670-683.
D. D. Hema, S. Dey, and A. Saha, “Mulberry Leaf Disease Detection using Deep Learning,” Int. J. Eng. Adv., vol. 8958, no. 1, pp. 3366–3371, 2019, doi: 10.35940/ijeat.A1521.109119.
M. M. Alqahtani et al., “Sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection,” Comput. Mater. Contin., vol. 74, no. 1, 2023, doi: 10.32604/cmc.2023.025280.
C. Wang, W. Suk, L. Xiangjun, Z. Daeun, and C. Hao, “Detection and counting of immature green citrus fruit based on the Local Binary Patterns ( LBP ) feature using illumination ? normalized images,” Precis. Agric., vol. 19, no. 6, pp. 1062–1083, 2018, doi: 10.1007/s11119-018-9574-5.
E. Sánchez-DelaCruz, J. P. Salazar López, D. Lara Alabazares, E. Tello Leal, and M. Fuentes-Ramos, “Deep learning framework for leaf damage identification,” Concurr. Eng. Res. Appl., vol. 29, no. 1, pp. 25–34, 2021, doi: 10.1177/1063293X21994953.
M. Türko?lu and D. Hanbay, “Plant disease and pest detection using deep learning-based features,” Turkish J. Electr. Eng. Comput. Sci., vol. 27, pp. 1636–1651, 2019, doi: 10.3906/elk-1809-181.
H. U. Rehman, S. Anwar, and M. Tufail, “Ingénierie des Systèmes d ’ Information Machine Vision Based Plant Disease Classification Through Leaf Imagining,” Int. Inf. Eng. Technol. Assoc., vol. 25, no. 4, pp. 437–444, 2020, doi: 10.18280/isi.250405.
M. Azath, M. Zekiwos, and A. Bruck, “Deep Learning-Based Image Processing for Cotton Leaf Disease and Pest Diagnosis,” J. Electr. Comput. Eng., vol. 2021, 2021, doi: 10.1155/2021/9981437.
S. V Kogilavani and S. Malliga, “Deep Learning based Model for Plant Disease Detection,” Int. J. Innov. Technol. Explor. Eng., vol. 3075, no. 12, pp. 3416–3420, 2019, doi: 10.35940/ijitee.L2585.1081219.
H. Amin and A. Darwish, “End-to-End Deep Learning Model for Corn Leaf Disease Classification,” IEEE Access, vol. 10, pp. 31103–31115, 2022, doi: 10.1109/ACCESS.2022.3159678.
I. T. Plata, E. B. Panganiban, D. B. Alado, A. C. Taracatac, B. B. Bartolome, and F. R. E. Labuanan, “Comparative Performance Analysis of Real-Time Methods for Cassava Phytoplasma Disease ( CPD ) Detection based on Deep Learning Neural Networks,” Int. J. Emerg. Technol. Adv. Eng., vol. 12, no. 02, pp. 55–64, 2022, doi: 10.46338/ijetae0222.
W. Alosaimi, H. Alyami, and M. I. Uddin, “PeachNet?: Peach Diseases Detection for Automatic Harvesting,” Comput. Mater. Contin., vol. 67, no. 2, 2021, doi: 10.32604/cmc.2021.014950.
U. Afzaal, “An Instance Segmentation Model for Strawberry Diseases,” Sensors, vol. 21, 2021, doi: 10.3390/s21196565.
B. Jabir, L. Rabhi, and N. Falih, “RNN- and CNN-based weed detection for crop improvement: An overview,” Foods Raw Mater., vol. 9, no. 2, pp. 387–396, 2021, doi: 10.21603/2308-4057-2021-2-387-396.
C. S. Pereira and R. Morais, “Deep Learning Techniques for Grape Plant Species Identification in Natural Images,” Sensors, vol. 19, 2019.
S. Zhang, S. Zhang, C. Zhang, X. Wang, and Y. Shi, “Cucumber leaf disease identi fi cation with global pooling dilated convolutional neural network,” Comput. Electron. Agric., vol. 162, no. April, pp. 422–430, 2019, doi: 10.1016/j.compag.2019.03.012.
J. Shin, Y. K. Chang, B. Heung, T. Nguyen-quang, G. W. Price, and A. Al-mallahi, “Original papers A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves,” Comput. Electron. Agric., vol. 183, no. February, p. 106042, 2021, doi: 10.1016/j.compag.2021.106042.
A. Yee-rendon, I. Torres-pacheco, A. S. Trujillo-lopez, K. P. Romero-bringas, and J. R. Millan-almaraz, “Identification in Jalapeño Pepper ( Capsicum annuum ) Leaves Using CNNs-Based Model,” Plants, vol. 10, 2021, doi: 10.3390/plants10101977.
E. Fujita, H. Uga, S. Kagiwada, and H. Iyatomi, “A Practical Plant Diagnosis System for Field Leaf Images and Feature Visualization,” Int. J. Eng. Technol., vol. 7, pp. 49–54, 2018.
P. Dhiman et al., “A Novel Deep Learning Model for Detection of Severity Level of the Disease in Citrus Fruits,” Electron., vol. 11, no. 3, pp. 1–14, 2022, doi: 10.3390/electronics11030495.
A. Khan, U. Nawaz, A. Ulhaq, and R. W. Robinson, “Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens,” PLoS One, vol. 15, no. 12 December, pp. 1–23, 2020, doi: 10.1371/journal.pone.0243243.
A. Mckenzie-gopsill, T. Esau, F. Abbas, and B. Acharya, “Detection of a Potato Disease ( Early Blight ) Using Artificial Intelligence,” Remote Sens, vol. 13, 2021, doi: 10.3390/rs13030411.
R. F. Caldeira and E. Al., “Identification of Cotton Leaf Lesions Using Deep Learning Techniques,” Sensors, vol. 21, 2021, doi: 10.3390/s21093169.
Y. H. Gu, H. Yin, D. Jin, R. Zheng, and S. J. Yoo, “Improved Multi-Plant Disease Recognition Method Using Deep Convolutional Neural Networks in Six Diseases of Apples and Pears,” agriculture, vol. 12, 2022.
I. Haque et al., “Analysis of Recognition Performance of Plant Leaf Diseases Based on Machine Vision Techniques,” J. Human, Earth, Futur., vol. 3, no. 1, pp. 129–137, 2022.
M. Anbarasi, K. Garg, S. D. Garg, Y. B. S, and S. D. M. A, “An Android Application for Plant Leaf Disease Detection using Convolution Neural Network,” Int. J. Innov. Technol. Explor. Eng., vol. 3075, no. 12, pp. 4133–4137, 2019, doi: 10.35940/ijitee.L3649.1081219.
N. K. Trivedi et al., “Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network,” Sensors, vol. 21, 2021, doi: 10.3390/s21237987.
A. Khattak, M. U. Asghar, U. Batool, and M. Z. Asghar, “Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network Model,” IEEE Access, vol. 9, pp. 112942–112954, 2021, doi: 10.1109/ACCESS.2021.3096895.
A. P. J. Geetharamani G., “Identification of plant leaf diseases using a nine-layer deep convolutional neural network,” Comput. Electr. Eng., vol. 76, pp. 323–338, 2019.
Q. Wang, F. Qi, M. Sun, J. Qu, and J. Xue, “Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques,” Comput. Intell. Neurosci., vol. 2019, 2019.