Aug 2026· EAI Endorsed Transactions on AI and Robotics· 0 citations· 33 references
Abstract
Artificial intelligence has become an effective tool for improving agricultural productivity through automated crop disease diagnosis. Brinjal (eggplant) cultivation suffers substantial yield losses from diseases such as Shoot and Fruit Borer, Wet Rot, Fruit Cracking, and Phomopsis Blight, yet reliable field-based diagnostic systems remain limited. To address this challenge, we introduce \textit{BrinjalFruitX}, a real-world dataset comprising 1,823 annotated images collected under natural farming conditions in Bangladesh across four disease classes and one healthy class. We propose an interpretable hybrid feature-enhancement deep ensemble framework that integrates image preprocessing, transfer learning, traditional machine learning, class imbalance mitigation, and explainable artificial intelligence. Three preprocessing techniques, Gaussian, Laplacian, and Unsharp Masking, are systematically evaluated, while deep features extracted using pre-trained VGG and ResNet models are classified by Random Forest, K-Nearest Neighbors, and a classifier-level ensemble. The optimal Unsharp--ResNet--Random Forest configuration achieved 80.0\% accuracy with an F1-score of 87.0\%. ADASYN applied in the deep feature space improved minority-class sensitivity, while Grad-CAM and Grad-CAM++ enhanced model interpretability. The proposed framework provides an effective and transparent solution for practical field-level brinjal disease detection and precision agriculture.
To correctly distinguish leaf diseases in eggplant (Solanum melongena), it is very important to improve agricultural production systems for precision. This paper presents a comparative analysis of five pre-trained convolutional neural network models, namely ResNet50, MobileNetV2, GoogLeNet, Xception, and VGG16, for multi-class classification of eggplant leaf diseases. In this work, a composite image dataset was developed by combining images from publicly available Kaggle , Mendeley repositories and local dataset (by collection from fields) to improve data diversity and generalization capability. A structurally optimized VGG16 model was developed to process 128 × 128 pixel images, which aimed to reduce computational complexity with preserved classification accuracy. Under the same training environment, the proposed modification achieved a classification accuracy of 96.5%. In addition to quantitative analysis, interpretability of the model was incorporated using Local Interpretable Model-agnostic Explanations (LIME) to generate localized feature attribution maps, thereby addressing the transparency problem associated with deep neural networks. The experimental results demonstrated that, although ResNet50 achieved the highest overall classification accuracy, the modified VGG16 architecture demonstrated a more balanced performance in terms of computational complexity, latency, and interpretability. Thus, the modified VGG16 is a reasonable candidate for use in real-time resource-constrained agricultural disease diagnostic system applications.
Honey Vachharajani, R. Gupta, Samir Patel· 2026 International Conferenc...· 0 citations
Pomegranate is a commercially valuable horticulture crop whose productivity is significantly affected by leaf diseases, particularly Bacterial Blight caused by Xanthomonas axonopodis pv. punicae. Early detection is crucial for preventing widespread infection and minimizing economic losses. Conventional diagnostic methods rely on visual examination by experts, which is subjective, labour-intensive and prone to delays. This paper presents a Convolutional Neural Network (CNN)–based deep learning approach for automated detection of pomegranate leaf diseases from image datasets. The proposed architecture processes raw leaf images, performs feature extraction and classifies them into healthy or diseased categories. Data augmentation, normalization and regularization techniques are applied to enhance model robustness and reduce overfitting. Experimental results demonstrate high classification accuracy and improved precision compared to traditional machine learning models. The trained model is further integrated into a web-based interface for real-time prediction, enabling farmers and agricultural practitioners to upload leaf images using low-cost devices. The system supports rapid decision-making and contributes to precision agriculture by providing a scalable, reliable and automated disease identification solution.
Ashwini S. Patil, S. R. Patil, S.R. Kumbhar· International Journal of Adv...· 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
An enhanced hybrid deep-learning method by combining graph neural networks (GNNs) and multi-layer perceptrons (MLPs) for effective strawberry disease detection in real environments of fields offers an accurate and explainable solution that has a computationally efficient commitment for real-time monitoring of disease in smart agriculture settings, particularly on low-cost hardware assets.
V. Bhosale, Chin-Shiuh Shieh· International Journal of Inf...· 0 citations
The prompt identification and precise categorization of chili plant diseases are crucial for promoting sustainable agriculture and reducing crop losses due to pests and pathogens. This research presents an automated detection method using the Single Shot Detector (SSD) framework, with ResNet-50 as the backbone and a Feature Pyramid Network (FPN) to manage multi-scale feature representations. Dataset including 14,248 images of chili plants, categorized into six classes like Leaf Spot, Rotten Fruit, Healthy Fruit, Healthy Leaf, Mosaic Curl, and Yellowing, underwent preprocessing involving segmentation, resizing, and augmentation, followed by a division into 90% training data and 10% testing data. Transfer learning was implemented using a COCO-pretrained SSD ResNet-50 FPN model, enhanced with cosine-decay learning-rate scheduling and momentum optimization. The assessment results indicated an overall accuracy of 92.7%, with the highest F1-scores achieved for Healthy Fruit (0.965) and Rotten Fruit (0.967). Under the COCO evaluation protocol, the model achieved an mAP@0.5 of 91.5% and mAP@[0.5:0.95] of 65.2%. Model ran at approximately 30 FPS on an NVIDIA T4 GPU. Reduced precision values were noted for Leaf Spot (0.866) and Mosaic Curl (0.850), suggesting a propensity for misclassification due to visual similarities among disease symptoms. Nonetheless, all classes attained F1-scores exceeding 0.86, illustrating the robustness of the proposed model. Importantly, the SSD-ResNet-50 approach offers both efficiency and accuracy within a single pipeline, enabling rapid inference practical for real-world applications. These findings emphasize the potential of deep learning-based solutions to strengthen plant disease monitoring systems. In conclusion, SSD with ResNet-50 FPN provides an effective and scalable methodology for the automated identification of chili plant diseases, contributing directly to sustainable agriculture and improved crop management practices.
Kusworo Adi, A. Setiadi, C. E. Widodo et al.· JOIV: International Journal...· 0 citations
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.
Ranu Solanki, D. Yadav· International Journal For Mu...· 0 citations