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Early Disease Classification of Mango Leaves Using Feed-Forward Neural Network and Hybrid Metaheuristic Feature Selection
Open accessAbstract
Plant disease, especially crop plants, is a major threat to global food security since many diseases directly affect the quality of the fruits, grains, and so on, leading to a decrease in agricultural productivity. Farmers have to observe and determine whether a leaf was infected by naked eyes. This process is unreliable, inconsistent, and error prone. Several works on deep learning techniques for detecting leaf diseases had been proposed. Most of them built their models based on limited resolution images using convolutional neural networks (CNNs). In this research, we aim at detecting early disease on plant leaves with small disease blobs, which can only be detected with higher resolution images, by an artificial neural network (ANN) approach. After a pre-processing step using a contrast enhancement method, all the infested blobs are segmented for the whole dataset. A list of several measurement-based features that represents the blobs are chosen and then selected based on their influences on the model's performance using a wrapper-based feature selection algorithm, which is built based on a hybrid metaheuristic. The chosen features are used as inputs for an ANN. We compare the results obtained using our methods with another approach using popular CNN models (AlexNet, VGG16, ResNet-50) enhanced with transfer learning. The ANN's results are better than those of CNNs using a simpler network structure (89.41% vs 78.64%, 79.92%, and 84.88%, respectively). This shows that our approach can be implemented on low-end devices such as smartphones, which will be of great assistance to farmers on the field.
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“In 2020, some researchers have utilized same-domain TL and achieved excellent results [86–88, 157].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [157]
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Cites
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“After performing contrast enhancement step, we investigate four popular CNN models, namely AlexNet [30], VGG16 [31], ResNet [32] (ResNet-50 variant).”
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“After performing contrast enhancement step, we investigate four popular CNN models, namely AlexNet [30], VGG16 [31], ResNet [32] (ResNet-50 variant).”
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A Review: Disease Detection in Wheat Plant using Conventional and Machine Learning Algorithms
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“[23], 2020 CNN Plant Village Dataset Accuracy [86.”
BackgroundA Novel Ensemble Classification Model for Plant Disease Detection Based on Leaf Images
Saraswathi, E., & Banu, J. F. (2023) · International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) · doi:10.1109/iceconf57129.2023.10083902 · Cited by 9
“The algorithm for plant disease classification was learned using the affected leaf region rather than the complete leaf [11].”
An Ensemble Classifier-Based Model Development for Mango Leaf Diseases Using Hybrid Feature Approach
Garg, R., Sandhu, A. K., & Kaur, B. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-1682-1_28 · Cited by 7
Efficient fruit disease diagnosis on resource-constrained agriculture devices
Iftikhar, S., Khattak, H. A., Saadat, A., Ameer, Z., & Zakarya, M. (2024) · Journal of the Saudi Society of Agricultural Sciences · doi:10.1016/j.jssas.2024.07.002 · Cited by 7
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Automatic Detection of Leaf Diseases in Hibiscus Plants Using Live Image Dataset with User Interface
Sriram, S., Jaishwal, R. K., Regmi, S., Vijayaraj, N., & Thangavel, M. (2024) · International Conference on Cybernation and Computation (CYBERCOM) · doi:10.1109/cybercom63683.2024.10803253 · Cited by 7
“The image undergoes several convolution and max-pooling operations to extract features, followed by fully connected neural network [20] layers.”
Islam, S., Hasan, M. Z., & Kabir, S. A. (2023) · International Conference on Sustainable Technologies for Industry 5.0 (STI) · doi:10.1109/sti59863.2023.10465227 · Cited by 8
“Pham et al. [8] proposed a multi-class mango leaf disease classification using deep neural networks.”
BackgroundForecasting Anthracnose Severity Levels in Mango Leaf using Hybrid Models
Mir, T. A., Banerjee, D., Chauhan, R. S., Singh, M. P., & Yamsani, N. (2024) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct61223.2024.10543795 · Cited by 7
“This demonstrates that the technique can be used with low-end gadgets, such as cell phones, which will be very beneficial to farmers as they polish their abilities in the field[4].”
MethodDetection of Corn Leaf Blight Using Convolutional Neural Networks
Hemavathi, S., Jancy, P. L., Fathima Mubarakkaa, M., & Selvi, V. M. (2022) · International Conference on Smart Systems and Inventive Technology (ICSSIT) · doi:10.1109/icssit53264.2022.9716333 · Cited by 7
Sharmila, G., & Rajamohan, K. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-16-9416-5_24 · Cited by 7
A novel plant leaf disease detection by adaptive fuzzy C-Means clustering with deep neural network
Vijayaganth, V., & Krishnamoorthi, M. (2022) · Journal of Experimental & Theoretical Artificial Intelligence · doi:10.1080/0952813x.2022.2108146 · Cited by 7
“APGWO (Pham et al., 2020) can be used on low-end devices like smart-phones and also leads to faster performance.”
BackgroundMethodInfluentialSurvey on Crop Disease Detection and Identification Based on Deep Learning
Faye, D., & Diop, I. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-23116-2_18 · Cited by 7
Integrating CNN and Random Forest for Accurate Classification of Mango Leaf Diseases
Choudhary, S., Choudhary, M., Kaur, S., & Kukreja, V. (2024) · International Conference on Automation and Computation (AUTOCOM) · doi:10.1109/autocom60220.2024.10486108 · Cited by 6
“In this research, the authors [11] focus on early-stage disease detection on plant leaves, presenting a multi-class classification of mango leaf diseases using deep neural networks.”
BackgroundBenchmarking ML and DL Models for Mango Leaf Disease Detection: A Comparative Analysis
Ghosh, H., Rahat, I. S., Lenka, R., Mohanty, S. N., & Chauhan, D. K. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-55486-5_8 · Cited by 6
Jagatheeswari., M., & Rao, Y. V. R. (2022) · IEEE International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA) · doi:10.1109/icccmla56841.2022.9988759 · Cited by 6
“N et al [5] suggested utilising deep neural networks to classify mango leaf diseases.”
BackgroundMango Tree Disease Health Classification Using High Resolution Image Through Deep learning
Sharma, S., & Sabharwal, M. (2022) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n56670.2022.10074014 · Cited by 6
Jayashree, S., & Sumalatha, V. (2023) · SN Computer Science · doi:10.1007/s42979-023-01908-9 · Cited by 6
“T. N. Pham et al. [5] presented an Artificial Neural Network (ANN) approach to detect early disease in plant leaves with small disease nodules only with high-resolution images.”
Background(2023) · International journal of intelligent engineering and systems · doi:10.22266/ijies2023.1031.03 · Cited by 6
Pandiyaraju, V., Venkatraman, S., Abeshek, A., Aravintakshan, S. A., & Pavan Kumar, S. (2024) · International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS) · doi:10.1109/adics58448.2024.10533513 · Cited by 6
Kumar, M. R., Kandukuri, P., Srinivas, V. S., Mukkapati, N., & Kumar, D. N. V. S. (2025) · CABI Agriculture and Bioscience · doi:10.1079/ab.2025.0022 · Cited by 5
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Low-Cost Hardware Design Approach for Long Short-Term Memory (LSTM)
Khalil, K., Mohaidat, T., & Bayoumi, M. (2023) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas46773.2023.10182030 · Cited by 5
“architectures of neural networks perform well on different types of data: Feed-Forward Deep Networks (FFNN) [3, 4], Recurrent Neural Networks (RNN) [5, 6], and Convolutional Neural Networks (CNN) [7, 8].”
MethodVeling, S. S., & Mohite-Patil, T. B. (2023) · International Journal of Image and Graphics · doi:10.1142/s0219467824500396 · Cited by 5
EfficientNet based Convolutional Neural Network for Visual Plant Disease Detection
Kumar, D. R., Ishak, M. K., & Maruzuki, M. I. F. (2022) · International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) · doi:10.1109/ecti-con54298.2022.9795496 · Cited by 5
“(3) Kernel sizes in EfficientNetV2 is smaller.”
Plant diseases detection using an improved CNN model
Vijayaganth, V., Srithar, S., Krishnamoorthi, M., & Vijaya Kumar, T. (2022) · International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) · doi:10.1109/icses55317.2022.9914109 · Cited by 5
“[3]used the artificial neural network (ANN) approach to detect early plant leaves disease in blobs, which can detect only highresolution images.”
BackgroundMethodLohi, S. A., & Bhatt, C. (2023) · International Conference on Innovative Trends in Information Technology (ICITIIT) · doi:10.1109/icitiit57246.2023.10068649 · Cited by 5
“A survey of similar models [2, 3, 4] along with their nuances, context-specific advantages, crop-specific limitations, and method specific future research scopes is discussed in the next section of this text.”
BackgroundEnhancing Crop Health by Identifying Leaf Disease with High Accuracy Using CNN
Suvarna, T. S. L., Sai, N. N., Rao, S. D., Gowtham, G., & Anoosha, C. (2023) · International Conference on Secure Cyber Computing and Communications (ICSCCC) · doi:10.1109/icsccc58608.2023.10176621 · Cited by 5
“An open-source web application called Jupyter IDE (Integrated Development Environment) provides an interactive computing environment for programming, data science, and data visualization using Python, R, and Julia programming languages [2].”
MethodTomato Foliage Disease Recognition System Using Random Forest and Convolutional Neural Networks
Loganayaki, T., & Poongothai, M. (2023) · Algorithms for intelligent systems · doi:10.1007/978-981-99-1431-9_26 · Cited by 5
MangoFruitDDS: A Standard Mango Fruit Diseases Dataset Made in Africa
Faye, D., Diop, I., Mbaye, N., Dione, D., & Diedhiou, M. M. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-48930-3_18 · Cited by 5
TOMATO PLANT DISEASE CLASSIFICATION USING LOCAL PATTERNS
Agarwal, M. (2023) · Advances in Electrical and Electronic Engineering · doi:10.15598/aeee.v21i4.5036 · Cited by 5
Detection of Mango Leaves Disease using CSUBW based Residual Attention with EfficientNet
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“Our categorization, shown in Figure 1, includes these four types as well [20].”
MethodComputer Aided Corn Leaf Disease Identification System
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An Intelligent and Precise Agriculture Model in Sustainable Cities Based on Visualized Symptoms
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Radish Growth Stage Recognition Based on GAN and Deep Transfer Learning
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A Hybrid Metaheuristic Algorithm for Intelligent Nurse Scheduling
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Self-citationPlant Disease Classification using Alex NetPreprint
Kewat, K. (2023) · Research Square · doi:10.21203/rs.3.rs-2612739/v1 · Cited by 5
Tuteja, G., Al‐Yarimi, F. A. M., Ikram, A., Gupta, R., Rehman, A. U., Singh, J., Noya, I. D., & López, L. A. D. (2026) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1720471 · Cited by 4
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A Study of Deep Learning Techniques on Oilseed Crops
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“2020 [24] Mango 450 YES No Deep learning CNN RESNET-50 YES YES 84.”
BackgroundClassification of Mango Leaf Disease using Machine Learning to Enhance Yield Productivity
Deepali Joshi, H. P. (2024) · Journal of Electrical Systems · doi:10.52783/jes.1147 · Cited by 4
“Ly Van Tran [9], Authors have worked on Anthracnose, Powdery Mildew, and Gall Mildew.”
BackgroundMokkapati, R., & Dasari, V. L. (2023) · International Conference on Innovative Mechanisms for Industry Applications · doi:10.1109/icimia60377.2023.10426237 · Cited by 4
“[18] Kumar and Teerath introduced Binary-Classifiers-Enabled Filters for Semi-Supervised Learning (BSSL).”
BackgroundS2AM: a sustainable smart agriculture model for crop protection based on deep learning
Sharma, A., & Sharma, P. (2024) · Journal of Plant Diseases and Protection · doi:10.1007/s41348-024-00934-w · Cited by 4
Sreehari, E., & Babu, L. D. D. (2024) · IEEE Access · doi:10.1109/access.2024.3512878 · Cited by 4
Cotton Leaf Disease Detection using Convolutional Neural Networks (CNN)
Sharmila, S. P., Bhargavi, R., Anusha, R. Y., Anusha, K., & Divya, B. (2023) · International Conference on Electronics and Renewable Systems (ICEARS) · doi:10.1109/icears56392.2023.10085551 · Cited by 4
Image Classification of Mangoes Using CNN VGG16 and AlexNet
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Ulaş, F., Lahlali, R., Laasli, S., Aasım, M., Dababat, A. A., Sameeullah, M., & İmren, M. (2025) · Physiological and Molecular Plant Pathology · doi:10.1016/j.pmpp.2025.103098 · Cited by 3
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Hossan, M. F., Riyad, T., Islam, S. J., Hasan, H., Faruk, A. B., & Sagor, S. (2025) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit68739.2025.11491607 · Cited by 3
“The combination of CNNs like ResNet101 and ViTs, along with transfer learning, holds great potential for improving plant disease detection, particularly for crops like jackfruit, leading to better disease management practices, increased crop yields, and more sustainable agriculture [17][18] .”
Hybrid IGWO-Dingo optimized DeMoHybridNet model for multi-class leaf disease identification
Palei, S., Mohapatra, P., Mallick, S. R., & Diwan, P. (2026) · Scientific Reports · doi:10.1038/s41598-026-53185-0 · Cited by 3
“The proposed model demonstrates superior performance, achieving an accuracy of 99.35% across various datasets, outperforming prior works using methods like VGG16 13,37 , SVM 9,38 , DenseNet201 14 , and other optimization techniques (e.g., PSO, TLBO, and ABC) 5,9,18,38 .”
InfluentialMango Disease Classification using CNN
Saurav, Pathak, N. K., Raizada, A., & Bala, N. (2023) · International Conference on Integrated Intelligence and Communication Systems (ICIICS) · doi:10.1109/iciics59993.2023.10421401 · Cited by 3
“In this paper drafted by TAN NHAT PHAM [3], a deep neural network-based multi-class classification of mango leaf disease is suggested.”
MethodSahu, K., Singh, M. K., Niranjan, M., & Joshi, P. C. (2024) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70046 · Cited by 3
“In 2020, Pham, Tran, and Dao [20] have focused on detecting the initial disorder on the crop leaves with little disorder blobs that could be identified with better resolution pictures by the ANN model.”
DCTnet: a double-channel transformer network for peach disease detection using UAVs
Zhang, J., Li, D., Shi, X., Wang, F., Li, L., & Chen, Y. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01749-w · Cited by 4
Artificial Intelligence in Precision Agriculture: A Review
Mustaza, S. M., Pauzi, N. A. M., Zainal, N., Moubark, A. M., & Zaman, M. H. M. (2025) · Jurnal Kejuruteraan · doi:10.17576/jkukm-2025-37(3)-34 · Cited by 3
Pahurkar, A. B., & Deshmukh, R. (2022) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds53736.2022.9761034 · Cited by 3
A Review of Various Plant Disease Detection Algorithms
Ramanjot, & Mittal, U. (2022) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito56286.2022.9964538 · Cited by 3
“(2020) [19] presented an ANN to categorize illnesses in mango leaves.”
BackgroundPrediction of Fruits and Vegetable Diseases Using Machine Learning and IoT
Kumar, D., Kamlesh, K. N., Kumar, A., Banerjee, S., & Vishal, K. (2023) · International Conference on Information Systems and Computer Networks (ISCON) · doi:10.1109/iscon57294.2023.10112097 · Cited by 3
“[10] A data analysis algorithm is chosen based on the data's amount, nature, and expected insights.”
MethodVGG16 feature selection using PCA-big bang big algorithm
Sharma, R. A., & Singh, A. J. (2023) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-222517 · Cited by 3
SADCNN-ORBM: a hybrid deep learning model based citrus disease detection and classification
Saini, A. K., Bhatnagar, R., & Srivastava, D. K. (2024) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v14i2.pp2191-2201 · Cited by 3
“To control these diseases, a large number of chemicals and fungicides are used, which results in environmental pollution and economic losses [10], [11].”
BackgroundApplication of support vector classifier for mango leaf disease classification
Deena, G., Raja, K., Azhagiri, M., Breen, W. A., & Prema, S. (2023) · THE SCIENTIFIC TEMPER · doi:10.58414/scientifictemper.2023.14.4.16 · Cited by 3
Big Data Analytics for Yield Prediction in Precision Agriculture
Vasudevan, N., & Karthick, T. (2024) · Agricultural sciences. · doi:10.5772/intechopen.114165 · Cited by 3
Mishra, G., Dev, H., & Singh, S. K. (2024) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon62832.2024.10983102 · Cited by 2
“Pham et al. [18] investigated feed-forward neural network (ANN) with a hybrid metaheuristic feature selection method to early classification of mango leaf diseases.”
Manogaran, N., Shankar, Y. B., Raja, R., Jayakumar, A. K., Murugan, S., Alkhayyat, A., & Jain, S. (2026) · Discover Computing · doi:10.1007/s10791-025-09826-5 · Cited by 2
Sahu, K., & Minz, S. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-25088-0_51 · Cited by 2
Detection of Disease in Apple Plant using its Images of Leaf Through KNN and Support Vector Machine
Kaur, A., Kalpanadevi, D., Gayathri, G. S., Shantanu, K., Rastogi, R., & Wankhede, C. A. (2023) · International Conference on Automation, Computing and Renewable Systems (ICACRS) · doi:10.1109/icacrs58579.2023.10405324 · Cited by 2
“[20]Support Vector Machines, Random Forests, K-Means Clustering are only few of the well-known and successful classic machine learning techniques.”
MethodAutomated Plant Disease Detection Using Deep Learning
Bebarta, D. K. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem33301 · Cited by 2
Identification of Leaf Diseases of Different Crops using modified ResNet50
Memon, M. S., Qabulio, M., Kumar, P., Soomro, A. K., & Memon, S. (2024) · The Asian Bulletin of Big Data Management · doi:10.62019/abbdm.v4i02.166 · Cited by 2
“Plant diseases (Darwin et al., 2021; PAN et al., 2022; Pham et al., 2020) lead to reductions in both quality and number.”
BackgroundA Mango Disease Prediction for Smart Agiculture using Machine Learning Algorithms
Lavanya, K., & Packialatha, A. (2024) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac61858.2024.10714657 · Cited by 2
“Lastly, the training data set for running (train, group) was constructed using the new model[10].”
MethodPonnila, P., & bazila banu, A. (2024) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec61587.2024.10722248 · Cited by 2
Performance evaluation of the classifiers based on features from cotton leaf images
Patil, B. M., & Burkpalli, V. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20670-z · Cited by 2
“The study was conducted on mangoes [1], grapes [2], rice [3], and cotton leaves [4].”
Crop Disease Detection using CNN Architecture VGG16
Raj, S., Kaur, S. P., Singh, N., & Gupta, M. (2024) · International Conference on Advances in Computing, Communication and Networking (ICAC2N) · doi:10.1109/icac2n63387.2024.10895694 · Cited by 2
“The CNN architecture VGG16 was created for image categorization and was based on transfer learning [12].”
Intelligent Disease Detection in Mango Leaves: A CNN-SVM Approach
Kaur, K., Aggarwal, A., Pandey, S., Bhela, S., & Goyal, G. (2025) · International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · doi:10.1109/icaiss61471.2025.11042118 · Cited by 2
“…model that works on the basis of combining the feature extraction property of CNN with the classification ability of SVM. Overcoming constraints of classical disease detection algorithms, the model thus improves classification performance and helps advance intelligent agricultural systems [6].”
Li, Z., & Javidan, S. M. (2025) · Smart Agricultural Technology · doi:10.1016/j.atech.2025.101282 · Cited by 2
Pragmatic Analysis of Fruit Processing Models for Quality Evaluation from a Statistical Perspective
Said, A. G., & Joshi, B. (2022) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon55314.2022.9909376 · Cited by 2
“Such deep learning models for Mango leaves classification can be observed in [10], wherein artificial neural networks (ANNs) are observed to have better accuracy than VGGNet-16, AlexNet, and, ResNet-50 models for detection of small disease blobs.”
MethodSmart Mango - Smart System for Mango Plantation
Withanaarachchi, S. P., Jayamanne, B. D. N., Niroshani, A., Aksham, M. Z. M., Hansika, M. M. D. J. T., & Lokuliyana, S. (2023) · International Conference on Advancements in Computing (ICAC) · doi:10.1109/icac60630.2023.10417084 · Cited by 2
“[14 ].”
The Importance of Interpretability in AI Systems and Its Implications for Deep Learning
Adnan, M. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-1738-9.ch003 · Cited by 2
Classification of Mango Disease Using Ensemble Convolutional Neural NetworkPreprint
Bezabiha, Y., Ayalew, A. M., Abuhayi, B. M., Demlie, T. N., & Ayenewe, E. A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4785206 · Cited by 2
An Interactive Interface for Plant Disease Prediction and Remedy Recommendation
Bhandarkar, M. S., Dewan, B., & Bansal, P. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-56703-2_5 · Cited by 1
An in-Depth Analysis for Machine-Vision-Based Mango Leaf Diseases Recognition
Das, P., Rahman, M. A., Habib, M. T., Khan, M. A. A., & Islam, M. M. (2025) · International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) · doi:10.1109/icmcsi64620.2025.10883360 · Cited by 1
“Pham et al. [12] utilized feed-forward neural networks and hybrid metaheuristic feature selection to classify mango leaves for early disease detection.”
Meta-Heuristic Enhanced Deep Learning Model for Mango Blossom and Stem Disease Classification
Veling, S. S., & Mohite-Patil Tanaji, B. (2025) · Journal of Artificial Intelligence and Capsule Networks · doi:10.36548/jaicn.2025.2.001 · Cited by 1
“Mango (Mangifera indica) is among the most widely cultivated tropical fruits and plays an important role in India's agriculture-driven economy [1].”
Mango Leaf Disease Classification Using Deep Learning Techniques
Deepa, C., Akshay Raj, M., Kumar, S., & Pavith Kumar, R. (2025) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict64420.2025.11004727 · Cited by 1
“These hybrid methods have been found to have potential in enhancing the robustness and accuracy of disease classification models, particularly under different environmental conditions and image quality [7].”
Tanishta, Goyal, V., & Arakeri, M. P. (2025) · International Conference on Next Generation Communication & Information Processing (INCIP) · doi:10.1109/incip64058.2025.11020297 · Cited by 1
Vijay, C. P., Pushpalatha, K. P., Janardhana, D. R., Kumar, H. S. R., & Puttegowda, K. (2025) · SN Computer Science · doi:10.1007/s42979-025-04504-1 · Cited by 1
“According to Trongtorkid et al., their proposed rule-based approach to detect plant leaf diseases in Barracuda mango achieved an accuracy of almost 90% [18].”
Semcm: semantic enhancement and multi-center modeling for maize leaf disease recognition
Tian, S., & Shang, J. (2026) · Complex & Intelligent Systems · doi:10.1007/s40747-026-02234-2 · Cited by 1
Efficient fruit disease diagnosis on resource-constrained agriculture devices
Iftikhar, S., Khattak, H. A., Saadat, A., Ameer, Z., & Zakarya, M. (2026) · Journal of the Saudi Society of Agricultural Sciences · doi:10.1007/s44447-025-00094-9 · Cited by 1
“Classifying Mango Leaves for multiple diseases utilizing a feedforward neural network and hybrid meta-heuristic feature selection solution has been performed Pham et al. (2020).”
Deep Learning and Neural Network Modeling to Optimize Tissue Culture of Medicinal Plants
Patial, A., Chandoliya, R., Kumari, K., Sharma, V., & Joshi, R. (2026) · CABI biotechnology series. · doi:10.1079/9781836991120.0018 · Cited by 1
Mango leaf disease classification using a hybrid model of ResNet50 and quantum machine learning
Reddy, K. V., Vinita, L. J., Vijitha, P. H., & Renold, A. P. (2026) · Frontiers in Sustainable Food Systems · doi:10.3389/fsufs.2026.1837661 · Cited by 1
El-Gendy, E. M., Ali, A. O., & Saafan, M. M. (2026) · Cluster Computing · doi:10.1007/s10586-026-06145-2 · Cited by 1
Chansanam, W., Elmalech, A., Kanyacome, S., Luekhong, P., Iam-On, N., & Boongoen, T. (2026) · Applied Sciences · doi:10.3390/app16146989 · Cited by 1
Thakre, P., & Chakole, J. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-026-03201-6 · Cited by 1
“2 illustrates the publicly available mango leaf datasets, including MangoLDB [5], MangoLeaf [6], MangoLeafDB [7 ], MangiFera2012 [ 8], BDMango [9] from Bangladesh; MangoFruitDDS [ 10] from Africa; and MangoLeaf [11], MangoLeaf5400 [12 ] from India.”
Support Vector Machine-based approach for Recognizing Bonsai Species using Leaf Image
Aminuddin, R., Maskan, F. A., Jalil, U. M. A., Fesol, S. F. A., & Ibrahim, S. (2022) · IEEE International Colloquium on Signal Processing & Its Applications (CSPA) · doi:10.1109/cspa55076.2022.9781913 · Cited by 1
“Typically, FNN is used to recognize patterns [13].”
MethodComputer Vision based Plant Disease Detection using Machine Learning Technique
(2023) · International Journal of Emerging Trends in Engineering Research · doi:10.30534/ijeter/2023/021172023 · Cited by 1
Design of EGTBoost Classifier for Automated External Skin Defect Detection in Mango Fruit
Jadhav, S., & Singh, J. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17191-y · Cited by 1
“Pham et al. [20] introduced a feed-forward neural network based on early disease identification of diseases.”
Identification of Tomato Crop Diseases using an Efficient CNN Model
Singh, T. (2023) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac58438.2023.10290427 · Cited by 1
Tomato leaf disease identification using machine learning
Rajan, V. K. A., Raj, T. V. A., Sunil, J., Waseem, J. M., & Shibili, T. (2023) · AIP conference proceedings · doi:10.1063/5.0107886 · Cited by 1
Sunilkumar, H. R., & Poornima, K. M. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-7383-5_3 · Cited by 1
Optimum Feature Selection Using Meta-heuristic Algorithms
Saraswat, M., & Tyagi, N. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-2082-8_31 · Cited by 1
Mobile Application Development for Plant Disease Detection using Deep Learning
Kasthuri, N., Ramyea, R., Venkateswaran, K., Elumalai, R., Arunprasshath, V. S., & Abhineeth, S. (2024) · International Conference on Electronics, Computing, Communication and Control Technology (ICECCC) · doi:10.1109/iceccc61767.2024.10592868 · Cited by 1
Skin disease detection using Efficient Net B0 Convolutional Neural Network
Mani, S. K., Jain, P., & Gayathri, M. (2024) · AIP conference proceedings · doi:10.1063/5.0217131 · Cited by 1
Metadata
- Authors
- Pham, T. N., Tran, L. V., & Dao, S. V. T.
- Affiliations
- Vietnam National University Ho Chi Minh City (VN)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 189960-189973
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3093991637
- Cited by
- 262
- References
- 39
- Citations by year
- 2026: 28 · 2025: 43 · 2024: 79 · 2023: 61 · 2022: 37 · 2021: 13 · 2020: 1
- FWCI
- 18.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Keywords
- mango leaf disease, plant disease detection, feedforward neural networks, wrapper-based feature selection, contrast enhancement, low-resolution images, transfer learning comparison
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