Jul 2026· International Journal For Multidisciplinary Research· 0 citations· 30 references
Abstract
India is one of the biggest producers and exporters of mangoes in the world, yet its cultivation is persistently threatened diseases that reduce yield, fruit quality, and orchard longevity. Traditional disease diagnosis is based on agronomists' hand visual inspection, which is a laborious, subjective, and challenging technique to scale across vast plantations. This research provides a hybrid deep learning system that incorporates AlexNet and ResNet-50 for the automated classification of five commercially relevant mango leaf diseases: Bacterial Canker, Anthracnose, Powdery Mildew, Sooty Mould and Healthy foliage. Through a fused, jointly trained classification head, the suggested architecture combines the deep, residual feature hierarchies of ResNet-50 with the shallow, texture-sensitive representations learned by AlexNet, enabling the network to take advantage of complementary visual cues that neither backbone fully captures on its own. The hybrid model was implemented and trained using MATLAB. The trained model achieved a validation accuracy of 99.47%. Comparative analysis against standalone AlexNet, standalone ResNet-50, and other architectures reported in the recent mango plant-disease literature indicates that the hybrid fusion strategy offers a favourable balance of accuracy and convergence stability.
Plant disease detection is essential to ensure the quality of agricultural production.
However, manual identification of disease symptoms is often tedious and susceptible to
human error. Deep learning techniques have been widely integrated into agricultural
systems, providing innovative solutions thanks to their robustness. Most of these studies are
essentially based on Convolutional Neural Networks (CNNs) architectures. This paper
presents a lightweight and accurate model for the classification of 29 plant leaf classes using
the PlantVillage dataset. We propose a hybrid learning model called Agriculture Network
(AgriNet). AgriNet consists of only six layers, and integrates the strengths of three CNN
models : Residual Neural Networks-50 (ResNet-50), Visual Geometry Group-16 (VGG-16)
and AlexNet. ResNet-50 is a very deep and powerful network, whereas VGG-16, despite
having a large number of parameters, achieves good accuracy using (3×3) filters. In contrast,
AlexNet presents a relatively simple architecture compared to the other two deep models.
An extensive set of experiments was conducted to evaluate the performance of the proposed
model for plant disease detection. The analysis, based on assessment measures, including
accuracy, precision, recall, and F1-score, demonstrates that our model achieves highly
reliable results, with an accuracy of 97.13%. Furthermore, AgriNet maintains low
complexity due to its lightweight hybrid architecture and reduced parameters count.
Hayat Meddeber, M. Meddeber· ITEGAM- Journal of Engineeri...· 0 citations
The infection of mango leaves is a major yield and fruit quality loss problem, and the necessity of having a precise and early diagnostic system for sustainable agriculture. Five convolutional neural network (CNN) architectures–namely, VGG16, VGG19, ResNet50, DenseNet121, and a custom-built AlexNet variant–were evaluated for their performance in classifying eight categories of mango leaf diseases. All models were pre-trained using a well-defined dataset of healthy and infected leaves and fine-tuned using transfer learning. VGG16 had the highest test accuracy of 99%, which was better than ResNet50 (91.5%), DenseNet121 (90.5%), VGG19 (87%), and AlexNet (86.5%). Further evaluation of these models using confusion matrices and F1-scores was conducted to ensure the strength of these models for various diseases including powdery mildew, anthracnose, and bacterial canker. Overall, the results underscore that advanced CNN models—particularly VGG16— can deliver near-expert precision in automated mango leaf disease identification. The leaf disease of mango is a major issue confronting farmers in Southeast Asia, particularly in India. Mango leaf disease is one of the biggest challenges faced by the farmers of south-east Asian countries, especially India.
P. G. K., Kiran Kumar H R· 2026 International Conferenc...· 0 citations
Timely detection of plant diseases is critical for sustaining agricultural production and reducing economic loss. Mango
crops are prone to several leaf infections that are difficult to diagnose without expert supervision. This work introduces a
deployable artificial intelligence system for automatic mango leaf disease recognition using deep transfer learning integrated
with an interactive analytics dashboard. A pretrained ResNet50 convolutional neural network is used as a feature extractor,
followed by customized fully connected layers for multi-class classification. The trained model is integrated into a web-based
interface using Streamlit, so users canupload images, obtainpredictions immediately, and view confidence-based analysis. The
proposed framework supports 8 disease categories. The proposed framework outputs interpretable results such as severity
estimation and probability distribution visualization. The experiment results indicate that transfer learning can be trained
efficiently and has a good predictive power. It provides a practical way to integrate deep learning solutions to precision
agriculture environments.
Dr. Bhavana R Maale, Shivadarshini R· International Journal for Re...· 0 citations
Phytopathological threats to mango (Mangifera indica L.) cultivation cause severe global agricultural yield
losses, necessitating rapid and accurate diagnostic frameworks. This study presents a highly optimized, end-to-end deep
learning pipeline leveraging transfer learning via the EfficientNet-B0 architecture for multi-class mango leaf disease
classification. The model was trained and rigorously evaluated on the MangoLeafBD dataset, comprising 4,000 balanced
images across eight categorical states (one healthy control and seven distinct fungal and bacterial pathologies) captured
under heterogeneous orchard conditions. Utilizing a stratified 70-15-15 data split, the network achieved exceptional
convergence within 16 epochs. Empirical evaluation on an isolated test set of 600 images yielded an absolute classification
accuracy of 100 percent, with precision, recall, and F1-scores of 1.0 across all classes and zero off-diagonal
misclassifications. Furthermore, the pipeline demonstrated near-zero latency inference on standard edge-computing
hardware. These findings validate the deployment viability of lightweight convolutional neural networks in resourceconstrained agricultural environments, establishing a robust and computationally efficient baseline for automated
precision pathology.
Jodell R. Bulaclac, J. D. Carmen· International Journal of Inn...· 0 citations
Background: Mango (Mangifera indica) is a commercially important fruit crop, but its yield and quality are often threatened by diseases and insect pests. Among the most common are fungal diseases such as sooty mould and powdery mildew and insect pests like gall midge. If not detected early, these cause substantial economic losses to farmers. Manual identification is time-consuming and error-prone, creating the need for automated solutions. Deep learning, particularly Convolutional Neural Networks (CNNs), has shown strong potential in plant disease and pest recognition.
Methods: In this study, a sequential CNN-based framework was developed for the automated identification and classification of mango leaf images into four classes: Sooty mould, powdery mildew, gall midge and healthy. The architecture consisted of five convolutional layers with max-pooling for feature extraction, followed by fully connected layers for classification. Model performance was assessed using accuracy, precision, recall, F1-score and confusion matrix analysis.
Result: The model achieved an overall accuracy of 96.0%, with high weighted average precision, recall and F1-scores, indicating reliable performance despite class imbalance. The confusion matrix confirmed the model’s capability to distinguish between disease, pest and healthy conditions with minimal misclassification.
Prashant Chaudhary, T. Rathore, Durgaprasad Navulla et al.· Agricultural Science Digest...· 0 citations
An intelligent deep learning framework is proposed for the automated prediction of diseases in cotton plants using leaf images, which has significant potential for real-time deployment in precision agriculture sys-tems, enabling farmers to take timely dis-ease management actions and improve crop productivity.
N. Jha, Vaishnav Badki, Tushar Sawarkar· Best Journal of Innovation i...· 0 citations