Jul 2026· International Journal for Research in Applied Science and Engineering Technology· 0 citations
TL;DR
This work introduces a deployable artificial intelligence system for automatic mango leaf disease recognition using deep transfer learning integrated with an interactive analytics dashboard and indicates that transfer learning can be trained efficiently and has a good predictive power.
Abstract
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.
Early diagnosis of tree leaf diseases is crucial for ensuring ecological stability, conserving biodiversity and sustainable agriculture productivity. Manual inspection and traditional image processing methods are often subjective, time-consuming, and susceptible to environmental changes like illumination variations, complex background and orientation of leaves in the images, which makes them difficult to work with. In response to the above drawbacks, the present work aims at proposing a novel, intelligent deep learning-based framework for automated detection of tree leaf diseases and supporting tree Agri-knowledge, namely PHYTOASSIST. The proposed approach is based on YOLO (You Only Look Once) convolutional neural network (CNN) for real-time object recognition of disease region on diseased leaf using high resolution image. To ensure robustness and generalisation capabilities to field scenarios, image pre-processing is used such as resize, normalisation and augmentation. The framework also includes a fertilizer and treatment recommendation module as well as an interactive agricultural chatbot that offers contextually relevant information for agricultural practice and disease prevention. Experimental results have proven the robustness of the accuracy (96.2%), precision (95.4%), recall (94.8%), and F1 score (95.1%) with an inference speed of less than 10 frames per second. The outcomes demonstrate the effectiveness of the design of the framework supporting the scalable agricultural disease monitoring and intelligent decision support in sustainable agricultural environments.
G. S, R. S, Sanjay A K et al.· 2026 4th International Confe...· 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
In order to ensure sustainable agricultural productivity, a reliable diagnostic framework to identify mango leaf diseases through interpretable visual symptoms is necessary. Deep learning models have high classification accuracy, but the “black box” nature of the deep learning models often makes it difficult to understand the underlying rationale of a prediction. To overcome this limitation, an explainable artificial intelligence (XAI) agent is computationally developed based on a two-stage diagnostic strategy. The proposed framework first employs hybrid vision transformer architecture for leaf-level classification, and employs local interpretability methods to determine the specific image patches that influence the decision. In the second stage, a feature detection model scans the identified regions to link the classification to visible pathological indicators such as necrotic regions, holes, and discoloration. By bridging the gap between global predictions and local geometric causes, this dual-model approach mimics the selective attention of a human specialist. The result analysis demonstrates that this strategy effectively transforms opaque diagnostic processes into a transparent and human-understandable format, thereby enhancing the reliability of automated systems for early crop management in hazardous or large-scale agricultural environments. The empirical results reveal that the suggested framework is capable of accurately classifying leaves while simultaneously localizing symptoms in an interpretable way, which can further be applied to diagnose diseases in different plants.
R. Anand, R. Mishra, Rijwan Khan et al.· Discover Internet of Things· 0 citations
The escalating global food crisis, exacerbated by climate change-induced yield losses and the increasing impact of pests and plant diseases, has reached a critical level. The early and accurate detection of plant diseases is of strategic importance not only for ensuring sustainable agricultural production but also for reducing economic dependency and safeguarding food security. Although deep learning-based approaches proposed in the existing literature often achieve high classification accuracy, their decision-making processes largely remain opaque, thereby limiting model reliability and practical adoption. In this study, a MobileNetV2-based deep learning architecture was employed for plant leaf disease classification, and the Convolutional Block Attention Module (CBAM) was integrated to enhance model performance. By emphasizing salient regions within leaf images, CBAM improved classification accuracy while simultaneously reducing computational overhead associated with processing irrelevant features. Furthermore, to enhance model interpretability, the Grad-CAM technique was applied to visualize the specific features and image regions that influenced the model's predictions. The experimental results not only demonstrate the contribution of the attention mechanism to classification performance but also address a significant gap in transparency and reliability within deep learning-based agricultural decision support systems.
Ömer Furkan Kaplan, Dilber Çetintaş· Signal Processing and Commun...· 0 citations
AgriFusionNet is discussed, a context-aware multimodal deep learning framework that integrates leaf images, textual symptom descriptions, and environmental data for robust plant disease classification and facilitates the co-learning of visual, semantic, and contextual environmental representations.
V. C., Nischith N Shetty, M. Ramaiah et al.· Frontiers in Fungal Biology· 0 citations
Agricultural productivity and food security are heavily impacted by plant diseases, and thus there is a high demand for accurate and automated plant disease detection that can be achieved by applying deep learning techniques. This research proposes a Multi-Model Ensemble Method Based on Deep Learning for multi-plant disease detection using ResNet50 to improve classification performance across multiple crop varieties. The proposed framework takes five important categories of plants into consideration including tomato, potato, grape, apple and maize, and 10 classes of healthy and diseased plants are generated from the PlantSeg dataset. The Anaconda platform was used along with Python to create a development environment that allows data preprocessing, augmentation, training and testing to be implemented efficiently. The proposed ensemble framework combines the feature extraction power of ResNet50 with several deep learning classifiers so as to obtain a good identification performance at different resolutions and environments. The proposed model performance is tested with the following metrics Accuracy, Precision, Inference Time, and Resolution quality and compared with MobileNetV2, YOLOv8 and the baseline CNN models. Experimental results show that the proposed ensemble ResNet50 framework achieves an accuracy of 98.7% and precision of 98.3%, which is about 6.4%, 4.8%, and 9.2% higher than that of MobileNetV2, YOLOv8, and CNN respectively. Moreover, the proposed method achieves high resolution disease localization capability with an inference time improvement of almost 18% compared with YOLOv8. The proposed system greatly improves the detection accuracy of the early stage and the calculation speed of the system, which is very suitable for smart agriculture applications and real-time monitoring of the health status of crops.
