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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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“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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“APPROACH WITH CNNS 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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Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906
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A systematic review of deep learning techniques for plant diseases
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“Pham et al. (2020) used an ANN model to classify mango leaves in the PlantVillage dataset, achieving an 89.41% accuracy rate.”
InfluentialLeaf disease detection using machine learning and deep learning: Review and challenges
Sarkar, C., Gupta, D., Gupta, U. C., & Hazarika, B. B. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110534 · Cited by 290
Lin, Y., Ma, J., Wang, Q., & Sun, D. (2022) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2022.2131725 · Cited by 232
Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis
Liu, Z., Wang, S., Zhang, Y., Feng, Y., Liu, J., & Zhu, H. (2023) · Foods · doi:10.3390/foods12061242 · Cited by 210
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A Novel Wrapper–Based Feature Selection for Early Diabetes Prediction Enhanced With a Metaheuristic
Le, T. M., Vo, T. M., Pham, T. N., & Dao, S. V. T. (2020) · IEEE Access · doi:10.1109/access.2020.3047942 · Cited by 153
“The pseudo-code of APGWO is given below: The solution of the APGWO-wrapper is a binary array having a dimension of 1 × n, where n is the total number of features.”
MethodInfluentialSelf-citationNssibi, M., Manita, G., & Korbaa, O. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100559 · Cited by 163
Detection and Classification of Tomato Crop Disease Using Convolutional Neural Network
Sakkarvarthi, G., Sathianesan, G. W., Murugan, V. S., Reddy, A. J., Jayagopal, P., & Elsisi, M. (2022) · Electronics · doi:10.3390/electronics11213618 · Cited by 119
Meta Deep Learn Leaf Disease Identification Model for Cotton Crop
Memon, M. S., Kumar, P., & Iqbal, R. (2022) · Computers · doi:10.3390/computers11070102 · Cited by 115
Prabu, M., & Chelliah, B. J. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06726-9 · Cited by 103
“In this experiment, we have used NN with SVM [23], BBDC [24], ML [25], MCNN [26], ANN [27], CNN [29] and proposed method.”
BackgroundMethodInfluentialA diverse ensemble classifier for tomato disease recognition
Astani, M., Hasheminejad, M., & Vaghefi, M. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107054 · Cited by 99
Enhancing Mango Fruit Disease Severity Assessment with CNN and SVM-Based Classification
Banerjee, D., Kukreja, V., Hariharan, S., & Jain, V. K. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126397 · Cited by 96
“Results using CNNs that have a more basic network structure performed poorly than those derived from ANNs (89.41% vs. 78.64%, 79.92%, and 84.88%, respectively)[8].”
MethodInfluentialArtificial Cognition for Early Leaf Disease Detection using Vision Transformers
Thai, H.-T., Tran-Van, N.-Y., & Le, K.-H. (2021) · International Conference on Advanced Technologies for Communications · doi:10.1109/atc52653.2021.9598303 · Cited by 95
Gautam, V., Ranjan, R. K., Dahiya, P., & Kumar, A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16012-6 · Cited by 85
“The author compared the results of the ANN model with pre-defined CNN models [35].”
Plant Disease Detection and Classification: A Systematic Literature Review
Ramanjot, Mittal, U., Wadhawan, A., Singla, J., Jhanjhi, N. Z., Ghoniem, R. M., Ray, S. K., & Abdelmaboud, A. (2023) · Sensors · doi:10.3390/s23104769 · Cited by 81
“Pham et al. [51] suggested a model which achieved a testing accuracy of 85.45% for identifying diseases from mango leaves.”
BackgroundMethodInfluentialSrinidhi, V., Sahay, A., & Deeba, K. (2021) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc51019.2021.9418268 · Cited by 77
“GooLeNet [1] and AlexNet [11] architectures have less number of parameters and in order to increase accuracy, they are further improved by adding more pooling layers and/or an activation function between pairs of connected layers.”
BackgroundMethodIntelligent plant disease diagnosis using convolutional neural network: a review
Joseph, D. S., Pawar, P. M., & Pramanik, R. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14004-6 · Cited by 81
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“The study proposed by [47] applied a feedforward arti-ficial neural network, called multilayer perceptron (MLP) and a hybrid metaheuristic feature selection technique for detecting mango leaf diseases.”
MethodDaphal, S. D., & Koli, S. M. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e18261 · Cited by 71
Taji, K., Sohail, A., Shahzad, T., Khan, B. S., Khan, M. A., & Ouahada, K. (2024) · IEEE Access · doi:10.1109/access.2024.3389648 · Cited by 71
“In a specific study by Pham et al. [33], the focus was on early detection of diseases in plant leaves, particularly those with small disease blobs that required high resolution images.”
BackgroundMethodInfluentialAboneh, T., Rorissa, A., Srinivasagan, R., & Gemechu, A. (2021) · Technologies · doi:10.3390/technologies9030047 · Cited by 58
“Plant diseases directly affect the quality of fruits and grains, and they lead to a decrease in agricultural productivity [22].”
Light-Weight Deep Learning Model for Accelerating the Classification of Mango-Leaf Disease
Mahmud, B. U., Mamun, A. A., Hossen, M. J., Hong, G. Y., & Jahan, B. (2024) · Emerging Science Journal · doi:10.28991/esj-2024-08-01-03 · Cited by 58
“A Feed-Forward Neural Network (FFNN) with Hybrid Metaheuristic Feature Selection (HMFS) was used by Pham et al. [19] to classify the three mango diseases: anthracnose, gall midge, and powdery mildew.”
MethodA Feature Selection Approach for Fall Detection Using Various Machine Learning Classifiers
Le, T. M., Tran, L. V., & Dao, S. V. T. (2021) · IEEE Access · doi:10.1109/access.2021.3105581 · Cited by 51
Self-citationA vision-based method to estimate volume and mass of fruit/vegetable: Case study of sweet potato
Huynh, T., TonThat, L., & Dao, S. V. T. (2022) · International Journal of Food Properties · doi:10.1080/10942912.2022.2057528 · Cited by 48
“Lastly, we may incorporate models to classify potatoes’ defects using a deep learning model, such as RESNET, or using a hybrid framework of combining feature extraction and machine learning as in the study by Pham et al.[38]”
MethodSelf-citationMango Diseases Classification Solutions Using Machine Learning or Deep Learning: A Review
Faye, D., Diop, I., & Dione, D. (2022) · Journal of Computer and Communications · doi:10.4236/jcc.2022.1012002 · Cited by 41
“8 [6] 2020 Anthracnose, Gall Midge, and Powdery Mildew”
BackgroundMethodInfluentialNagachandrika, B., Prasath, R., & R, P. J. I. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106316 · Cited by 41
Plant leaf disease detection and classification using modified transfer learning models
Srivastava, M., & Meena, J. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16929-y · Cited by 39
“In addition to this paper, Pham et al. [9] suggested mango leaf illness detection by an ANN model to find micro disease blobs in the mango leaf.”
Classification of mango disease using ensemble convolutional neural network
Bezabh, Y. A., Ayalew, A. M., Abuhayi, B. M., Demlie, T. N., Ayenewe, E. A., & Mengistu, T. E. (2024) · Smart Agricultural Technology · doi:10.1016/j.atech.2024.100476 · Cited by 44
Mohapatra, M., Parida, A. K., Mallick, P. K., Zymbler, M. L., & Kumar, S. (2022) · Computers · doi:10.3390/computers11050082 · Cited by 38
“With the introduction of CV, ML, and AI technologies, advancement has been achieved in building automated models that enable the accurate and timely diagnosis of plant leaf disease [16,17].”
BackgroundMethodDFNet: Dense fusion convolution neural network for plant leaf disease classification
Faisal, M. A., Leu, J., Avian, C., Prakosa, S. W., & Köppen, M. (2023) · Agronomy Journal · doi:10.1002/agj2.21341 · Cited by 38
Bagga, M., & Goyal, S. (2024) · Urban Agriculture & Regional Food Systems · doi:10.1002/uar2.20053 · Cited by 48
“Rescaling is used to reduce the original image’s resolution to a more manageable 256 by 256 pixels (Pham et al., 2020).”
LeafSpotNet: A deep learning framework for detecting leaf spot disease in jasmine plants
Shwetha, V., Bhagwat, A., & Laxmi, V. (2024) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2024.02.002 · Cited by 41
Leaf Disease Identification: Enhanced Cotton Leaf Disease Identification Using Deep CNN Models
Sivakumar, P., Mohan, N. S. R., Kavya, P., & Teja, P. V. S. (2021) · IEEE International Conference on Intelligent Systems, Smart and Green Technologies (ICISSGT) · doi:10.1109/icissgt52025.2021.00016 · Cited by 31
Mango Leaf disease Classification using deep learning Hybrid Model
Jain, S., & Jaidka, P. (2023) · International Conference on Power, Instrumentation, Energy and Control (PIECON) · doi:10.1109/piecon56912.2023.10085869 · Cited by 30
“[11,12] Convolutional layers with iterative learning were used to categorize illnesses and automatically extract the leaf disease position feature.”
MethodMS-Net: a novel lightweight and precise model for plant disease identification
Quan, S., Wang, J., Jia, Z., Yang, M., & Xu, Q. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1276728 · Cited by 36
Deep Convolutional Neural Networks for South Indian Mango Leaf Disease Detection and Classification
Shaik, T., & Ilango, S. S. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.042496 · Cited by 28
Efficient and autonomous detection of olive leaf diseases using AI-enhanced MetaFormer
Paçal, İ., Kılıçarslan, S., Özdemir, B., Deveci, M., & Kadry, S. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11131-y · Cited by 28
“Pham et al. ( 2020) presented a model based on ANN on plant leaves of a higher resolution.”
A Deep Learning Based Smartphone Application for Detecting Mango Diseases and Pesticide Suggestions
Rahaman, M. N., Chowdhury, M. R., Rahman, M. A., Ahmed, H., Hossain, M. M., Rahman, M. H., Biswas, M. S., Kader, M. A., Noyan, T. A., & Biswas, M. (2023) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/1301104 · Cited by 27
“[10] Proposed a Mango Leaves Early Disease Classification method using Hybrid Metaheuristic Feature Selection with Feed-Forward Neural Networks.”
MethodSynergistic integration of metaheuristics and machine learning: latest advances and emerging trends
Zhang, R., Wang, J., Liu, C., Su, K., Ishibuchi, H., & Jin, Y. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11266-y · Cited by 31
Vadivel, T., & Suguna, R. (2021) · Acta Agriculturae Scandinavica Section B - Soil & Plant Science · doi:10.1080/09064710.2021.1976266 · Cited by 25
“They require less pre-prepartion than customary methodologies and perform programmes including extraction of tomato leaf infection location (Pham et al. 2020).”
BackgroundRetractedMango Leaf Stress Identification Using Deep Neural Network
Gautam, V., & Rani, J. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.025113 · Cited by 25
Enhanced detection of mango leaf diseases in field environments using MSMP-CNN and transfer learning
Chen, Y.-C., Wang, J.-C., Lee, M.-H., Liu, A.-C., & Jiang, J. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.109636 · Cited by 24
Lemon Leaf Disease Classification Using CNN-based Architectures with Transfer Learning
Pramanik, M. A., Khan, A. Z., Biswas, A. A., & Rahman, M. (2021) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt51525.2021.9579586 · Cited by 24
“Pham et al.[1] applied transfer learning techniques with enhanced CNN along with Artificial Neural Networks with Feature Selection (FS) on their comparatively smaller dataset (450 images) for the classification of diseases of mango leaves.”
BackgroundMethodInfluentialDevelopment Trends in Precision Agriculture and Its Management in China Based on Data Visualization
Song, C., Ma, W., Li, J., Qi, B., & Liu, B. (2022) · Agronomy · doi:10.3390/agronomy12112905 · Cited by 24
“[30] indicated that many diseases affect the quality of fruits and grains and pose a serious threat to global food security, while convolutional neural networks (CNN) and artificial neural networks (ANN) provide fast and accurate tools for identifying plant diseases.”
BackgroundVijay, C. P., & Pushpalatha, K. P. (2024) · Journal of Integrated Science and Technology · doi:10.62110/sciencein.jist.2024.v12.804 · Cited by 24
Detection of cotton leaf disease with machine learning model
Hyder, U., & Talpur, M. R. H. (2024) · Turkish Journal of Engineering · doi:10.31127/tuje.1406755 · Cited by 23
Alzubaidi, L., Fadhel, M. A., Hollman, F., Salhi, A., Santamaría, J., Duan, Y., Gupta, A., Cutbush, K., Abbosh, A. M., & Gu, Y. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10878-0 · Cited by 23
“Using the same approach, several published articles have shown the effectiveness of these solutions for medical images and other domains (Silva et al. 2021; Pham et al. 2020; Fan and Bu 2022; Al-kubaisi and Khamiss 2021; Cheng et al. 2021; Li et al. 2024).”
A Systematic Analysis of Various Techniques for Mango Leaf Disease Detection
Garg, R., Sandhu, A. K., & Kaur, B. (2023) · International Conference on Disruptive Technologies (ICDT) · doi:10.1109/icdt57929.2023.10150878 · Cited by 21
“TABLE I. COMPARISON OF VARIOUS TECHNIQUES OF IMAGE PROCESSING STEPS Phases Technique Reference Pre-processing Contrast Enhancement Tan Nhat Pham et al. [10], S. Wongsila et al. [34], K. Trang [26] Noise removal Singh et al. [22], R. Mia et al. [13] Segmentation Thresholding K. Swetha et al. [9], S.Veling [27] Edge bas…”
MethodInfluentialReview of plant leaf recognition
Wang, Z., Cui, J. H., & Zhu, Y. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10278-2 · Cited by 19
“Pham et al. (2020) selected several measurement-based features representing the spots and then used wrapper-based feature selection algorithms (based on a hybrid metaheuristic) to select these features based on their impact on model performance.”
MethodSahu, K., & Minz, S. (2023) · Sensing and Imaging · doi:10.1007/s11220-023-00428-3 · Cited by 22
“In 2020, Pham et al. [8] have improved an early disease detection system in plants with small disease blobs.”
InfluentialDetecting Mango Leaf Disease with VGG 16 Transfer Learning Model
Singh, R., Sharma, N. V., & Gupta, R. (2023) · IEEE International Conference on Industrial Electronics: Developments & Applications (ICIDeA) · doi:10.1109/icidea59866.2023.10295265 · Cited by 19
Classification of Medicinal Plant Leaves for Types and Diseases with Hybrid Deep Learning Methods
Kayaalp, K. (2024) · Information Technology And Control · doi:10.5755/j01.itc.53.1.34345 · Cited by 19
“It was aimed to classify the disease by using low resolution images of plant leaf diseases with the Pham et al. [20].”
BackgroundMethodDalvi, P., Kalbande, D. R., Rathod, S. S., Dalvi, H., & Agarwal, A. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3491938 · Cited by 22
Chillakuru, P., Divya, D., & Ananthajothi, K. (2022) · Cybernetics & Systems · doi:10.1080/01969722.2022.2151173 · Cited by 18
“(1) (Pham, Tran, and Dao 2020).”
Mango Leaf Disease Detection Based on Deep Learning Approach
Mohapatra, M., Parida, A. K., Mallick, P. K., & Padhy, N. (2022) · International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) · doi:10.1109/assic55218.2022.10088323 · Cited by 18
““Gall infestation, webbers attack, mango distortion, stem miner, anthracnose, alternaria leaf spots”, and other diseases of mango plants are widespread [13-16].”
BackgroundDeep transfer learning driven model for mango leaf disease detection
Singh, Y. P., Chaurasia, B. K., & Shukla, M. M. (2024) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-024-02480-y · Cited by 18
“Pham et al . used artificial neural networks (ANNs) to detect early disease in plant leaves by identifying microscopic disease blobs in mango leaf images (Pham et al. 2020).”
Javidan, S. M., Ampatzidis, Y. G., Banakar, A., Vakilian, K. A., & Rahnama, K. (2024) · AgriEngineering · doi:10.3390/agriengineering6040238 · Cited by 18
Salamai, A. A. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124490 · Cited by 17
Detecting True Medicinal Leaves Among Similar Leaves Using Computer Vision and CNN
Balasundaram, A., Dilip, G., Ashokkumar, S., Manickam, M. T., Gurunathan, K., & Kothandaraman, D. (2023) · National Academy Science Letters · doi:10.1007/s40009-023-01210-5 · Cited by 17
Hayıt, T., Endes, A., & HAYIT, F. (2023) · European Journal of Plant Pathology · doi:10.1007/s10658-023-02791-z · Cited by 17
“4 Feature extraction block diagram 2015; Kumari et al., 2019; Pham et al., 2020; Ranjan et al., 2015; Shah & Jain, 2019).”
Proposed ResVGG-Net Model for Mango Leaf Disease Classification and Agricultural Sustainability
Kant, V., Gupta, S., Gupta, D., Juneja, S., Gulzar, Y., & Niranjan Devanathan, R. (2025) · Applied Fruit Science · doi:10.1007/s10341-025-01667-2 · Cited by 16
“ResNet50 learns complex patterns effectively with residual learning, while the structured convolutional layers of VGG16 enable high-level feature extraction in a fine-grained manner (Pham et al. 2020).”
Automatic Rice Plant’s disease diagnosis using gated recurrent network
Patel, B., & Sharaff, A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14980-3 · Cited by 16
“Similar enhancements can be observed from [15, 27, 32], wherein Hybrid Metaheuristic Feature Selection, Data augmentation with multi-scale AlexNet (DMS-AlexNet), 29002 Multimedia Tools and Applications (2023) 82:28997–29016”
MethodPlant disease detection using a depth-wise separable-based adaptive deep neural network
Kaushik, I., Prakash, N., & Jain, A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19047-5 · Cited by 16
Enhancing Agricultural Disease Detection: A Multi‐Model Deep Learning Novel Approach
Hamid, M. K., Shah, S. K., Husnain, G., Ghadi, Y. Y., Maaytah, S. A. A., & Qahmash, A. (2025) · Engineering Reports · doi:10.1002/eng2.13113 · Cited by 15
Jain, A., & Dwivedi, R. K. (2025) · International Journal of Information Technology · doi:10.1007/s41870-025-02478-0 · Cited by 15
Talpur, N., Abdulkadir, S. J., Akhir, E. A. P., Hasan, M. H., Alhussian, H., & Abdullah, M. H. A. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.01.020 · Cited by 15
Vedhamuru, N., Gurubelli, Y., Bhookya, N. N., Malmathanraj, R., & Palanisamy, P. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3364126 · Cited by 14
“Using high-resolution images, an artificial neural network (ANN) technique has detected early disease on plant leaves with small disease blobs [11].”
BackgroundPlant Disease Detection for Guava and Mango using YOLO and Faster R-CNN
Shetty, K. U., Kutty, R. J., Donthi, K., Patil, A., & Subramanyam, N. (2024) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi60426.2024.10503209 · Cited by 14
“Tan Nhat Pham et al. [12] in their study tackled the challenge of early disease detection in mango leaves using a dual approach involving CNN models and ANNs.”
MethodLavanya, K., & Packialatha, A. (2024) · Journal of Phytopathology · doi:10.1111/jph.13290 · Cited by 14
Rabbi, M. O. F., Jahan, I., Rukhsara, L., & Rabeya, T. (2024) · International Conference on Electrical Engineering and Information Communication Technology · doi:10.1109/iceeict62016.2024.10534329 · Cited by 14
“To be honest, it is hard, takes a long time, and mistakes happen often [4].”
BackgroundMango Pest Detection Using Entropy-ELM with Whale Optimization Algorithm
Uchimuthu, M., & Chitra, S. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.028869 · Cited by 14
Patel, A., Ravikumar, R. N., Betgeri, S. S., Singhal, S., Singh, S. K., & Takodara, M. N. (2024) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet61516.2024.10593509 · Cited by 13
“For accurate diagnosis of mango leaf diseases, the process involves generating high-quality pathological images with the help of a nursing associate, alongside the development of a CNN model and framework [8].”
Random forest regressor applied in prediction of percentages of calibers in mango production
Collin, B. R. R., Xavier, D. D. L. A., Amaral, T. M., Silva, A. C. G. C., Costa, D. D. S., Amaral, F. M., & Oliva, J. T. (2024) · Information Processing in Agriculture · doi:10.1016/j.inpa.2024.12.002 · Cited by 13
Ünal, Y., & Türkoğlu, M. (2025) · El-Cezeri Fen ve Mühendislik Dergisi · doi:10.31202/ecjse.1420624 · Cited by 15
“Pham et al. [13] studied a dataset consisting of 450 mango leaf images.”
Smart System for Crop and Diseases Prediction using Random Forest and Resnet Architecture
Kavitha, T., Deepika, S., Nattaraj, K., Shanthini, P., & Puranaraja, M. (2022) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds53736.2022.9760813 · Cited by 13
Machine learning techniques implementation for detection of grape leaf disease
Shantkumari, M., & Uma, S. V. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14441-x · Cited by 13
“In [29], a feed-forward neural network is adopted for the early detection of leaf diseases.”
MethodNanoelectronics-enabled reservoir computing hardware for real-time robotic controls
Chen, M., An, X., Ki, S. J., Liu, X., Sekhon, N., Boyarov, A., Acharya, A., Tawil, J., Bederman, M., & Liang, X. (2025) · Science Advances · doi:10.1126/sciadv.adu2663 · Cited by 13
“However, these ANNs are not suitable for processing the data involving temporal information because they mainly function as feedforward networks, lacking device physics schemes for mapping or filtering the data with different temporal contexts ( 13 – 15 ).”
Crop Disease Prediction using Deep Learning Techniques - A Review
Sharma, G., & Shrivastava, G. K. (2022) · International Journal of Computer Science and Engineering · doi:10.14445/23488387/ijcse-v9i4p104 · Cited by 12
Raja, D., & Karthikeyan, M. (2023) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds56580.2023.10104602 · Cited by 12
“As well, conventional techniques are, highly dependent on professionals, manuals, and experience but almost all of them are labour-intensive, expensive, and timeconsuming which makes detection difficult [3].”
BackgroundIdentification of Diseases Affecting Mango Leaves Using Deep Learning Models
Shaik, T., & Ilango, S. S. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-47997-7_10 · Cited by 12
Novel Machine Learning Techniques for Classification of Rolling Bearings
Phan, Q. N. X., Le, T. M., Tran, H. M., Tran, L. V., & Dao, S. V. T. (2024) · IEEE Access · doi:10.1109/access.2024.3431040 · Cited by 14
“The promising future direction for this research is to expand it to other metaheuristics [36], [37], [38], [39], [40], [41], [42].”
Self-citationA hybrid metaheuristic algorithm for antimicrobial peptide toxicity prediction
Dao, S. V. T., Phan, Q. N. X., Tran, L. V., Le, T. M., & Tran, H. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-70462-y · Cited by 12
Self-citationSelvakumar, A., & Balasundaram, A. (2023) · The Imaging Science Journal · doi:10.1080/13682199.2023.2204036 · Cited by 12
“[19] used deep learning to detect the small infection blob using a huge-resolution image.”
BackgroundMethodRathor, A. S., Choudhury, S., Sharma, A., Shah, G., & Nautiyal, P. (2025) · Physics and Chemistry of the Earth Parts A/B/C · doi:10.1016/j.pce.2025.104080 · Cited by 11
A Review of Mango Leaf Diseases Classification, Causes and Management Strategies
Kumar, S., Gupta, B., & Garg, L. (2023) · doi:10.1109/icacctech61146.2023.00077 · Cited by 11
Identifying The Coconut Leaf Disease Using Deep Transfer Learning Approach
Tuhin, H. H., Kayum, M. A., Sarker, M. R. K. R., Siddiquee, S. M. T., & Pandey, B. (2024) · IEEE International Conference on Smart Information Systems and Technologies (SIST) · doi:10.1109/sist61555.2024.10629552 · Cited by 11
“This study by Pham, T. N., et al. [6] says that early disease detection of mango leaves using feed-forward neural networks is an effective way to determine if a plant is infected or not.”
BackgroundPatil, M. A., & Manohar, M. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-9225-4_36 · Cited by 11
Sahu, K., & Minz, S. (2023) · Geocarto International · doi:10.1080/10106049.2023.2178520 · Cited by 11
“(Pham et al. 2020) have proposed a wrapper-based feature selection approach using an adaptive particle-grey wolf hybrid meta-heuristic algorithm.”
MethodRecognition of Bean Leaf Diseases Using Neural Network and Machine Learning Techniques
Rahunathan, L., Sivabalaselvamani, D., Elakkiya, E. S., Madhumitha, M., & Kumaresh, K. (2023) · International Conference on Smart Data Intelligence (ICSMDI) · doi:10.1109/icsmdi57622.2023.00098 · Cited by 11
“Tan nhat pham, et al., [8] proposed since so many fungal and bacterium infections directly impact the fruits and grains quality and are causing a reduction in crop production, they pose a serious threat to world food security.”
BackgroundMachine Learning based Classification of Diseased Mango Leaves
Selvakumar, A., & Balasundaram, A. (2022) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v10i7.5563 · Cited by 11
LeafAI: Interpretable plant disease detection for edge computing
Abdullah-Al-Kafi, M., Banshal, S. K., Moni, R., Aziz, A. L., Aljuaid, M. M., & Bansal, R. (2026) · PLoS ONE · doi:10.1371/journal.pone.0335956 · Cited by 10
“For example, early-stage identification of diseases in mango leaves, such as Anthracnose, Gall Midge, and Powdery Mildew, showed promising results using an Artificial Neural Network (ANN), which outperformed popular CNN models like ResNet-50, VGG16, and AlexNet [11].”
Ramadan, S. T. Y., Sakib, T., Rahat, M. A., Mosharrof, S., Rakin, F. I., & Jahangir, R. (2023) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit60459.2023.10441374 · Cited by 10
“TAN NHAT PHAM et al. [12] presented a new approach for the early identification of plant leaf diseases, notably on Mango leaves.”
BackgroundGarg, R., Sandhu, A. K., Kaur, B., Goyal, B., & Dogra, A. (2024) · Biomedical & Pharmacology Journal · doi:10.13005/bpj/2861 · Cited by 10
Nagpal, J., Goel, L., & Shekhawat, P. S. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00578-x · Cited by 10
“Bio-inspired algorithms, like genetic algorithm, ant colony optimization (ACO), and particle swarm optimization (PSO), are increasingly used in engineering optimization techniques [60].”
Elevating Mango Leaf Disease Classification Utilizing Dense ViT
Nazera, F., Nadim, A. N. A., Dey, S. K., Tanvir, K., & Kabir, M. S. (2024) · International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) · doi:10.1109/accai61061.2024.10602314 · Cited by 10
“Scientifically known as Procontariniamatteiana, this midge causes galls to develop on tree compo-nents, particularly leaves [18].”
BackgroundAdvancing mango leaf variant identification with a robust multi-layer perceptron model
Fahim-Ul-Islam, M., Chakrabarty, A., Rahman, R., Moon, H., & Piran, M. J. (2024) · Scientific Reports · doi:10.1038/s41598-024-74612-0 · Cited by 9
Metaheuristic-Integrated DensoMobileForestNet for Apple Leaf Disease Identification
Palei, S., & Mohapatra, P. (2025) · Applied Fruit Science · doi:10.1007/s10341-025-01384-w · Cited by 9
“Pham et al. (2020) developed an ANN-based method with hybrid metaheuristics for early disease detection, outperforming standard CNNs. El Akhal et al. (2023) used hybrid CNN-classifier models for olive leaf classifi-cation, improving diagnostic AC.”
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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