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.
Abstract
Early and accurate identification of plant diseases is essential for improving crop productivity and ensuring food security. Many existing deep learning-based plant disease classification methods rely solely on leaf images collected from a controlled environment, which limits their applicability in real-world agricultural conditions where symptoms may be visually unclear and influenced by environmental factors. To address these challenges, this study discusses AgriFusionNet, a context-aware multimodal deep learning framework that integrates leaf images, textual symptom descriptions, and environmental data for robust plant disease classification. The proposed architecture employs EfficientNet-B0 for visual feature extraction, BERT for semantic representation of symptom descriptions, and a lightweight multilayer perceptron for modeling environmental factors such as temperature, humidity, rainfall, and soil moisture. Features from all three modalities are fused into a unified representation to train the CNN model. The model is trained and tested upon the Context-Aware Multimodal Augmented PlantVillage dataset covering 38 plant diseases and healthy classes. Experimental results show that AgriFusionNet gives an overall accuracy of 98.94% on the dataset Context-Aware Multimodal Augmented PlantVillage, with competitive precision and recall and F1-score. The multimodal framework facilitates the co-learning of visual, semantic, and contextual environmental representations and the analyses of the confusion matrix and feature interactions give insights into cross-modal relationships. The proposed approach aims to explore context-aware multimodal representation learning for agricultural AI applications, with emphasis on integrating complementary visual, semantic, and contextual information.
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
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.
Dr. Bhavana R Maale, Shivadarshini R· International Journal for Re...· 0 citations
A hybrid framework integrating a Convolutional Neural Network with a Large Language Model to perform image-based plant leaf disease classification accompanied by interpretable diagnostic explanations demonstrated that combining high-performance visual recognition with language-based reasoning enhanced both diagnostic reliability and interpretability in digital agriculture applications.
Frenky Riski Gilang Pratama, S. Surono, A. Thobirin· International Journal of Adv...· 0 citations
Plant diseases have long been considered a major threat to global food production systems. Therefore, early diagnosis is vital to mitigate the risk of these diseases. This task can be challenging, as the number of harmful diseases is substantial. One technology that has gained widespread interest is artificial intelligence, specifically deep learning, which is used to identify plant diseases using leaf patterns. This paper presents two deep learning models, a custom CNN model and a transfer learning model based on the DenseNet-121 architecture. Experiments were carried out using the PlantCity dataset, which consists of twelve subsets of diverse crop species representing fruits, vegetables, and grains with variations among the subsets, including the number of classes, the subset sizes, class distribution, and visual complexity of disease symptoms. The two models were evaluated using multiple metrics, including accuracy, loss, precision, recall, and F1-score. Explainable AI using the LIME technique was deployed to better interpret the acquired results. Results showed that the developed transfer learning model based on DenseNet-121 had superior performance over the CNN model, with accuracies ranging from 86% to 99% across eleven experimented crops. In order to perform an independent experimental validation for the developed model, future work will focus on constructing a local crop dataset captured from Iraqi fields to evaluate the developed models based on the local environment.
H. Jeiad, S. Samaan, Omar Janeh et al.· Automation· 0 citations
Accurate detection of plant leaf diseases is essential for enhancing crop productivity and supporting global food security. In addition to disease classification, understanding how environmental and soil conditions affect model performance is important for developing robust real-world agricultural monitoring systems. Although deep learning (DL) models achieve high accuracy on benchmark datasets, their performance in real-world settings is often limited by variations in illumination, background complexity, and environmental conditions. This study proposes a smart DL framework for detecting and classifying multiple leaf diseases in tomato, potato, and pepper plants. The framework combines U2-Net-based leaf segmentation with a Convolutional Neural Network–Bidirectional Gated Recurrent Unit (CNN–Bi-GRU) architecture. MobileNetV2 is employed as the feature extraction backbone to capture spatial characteristics, while Bi-GRU layers model sequential feature dependencies, forming a spatio-temporal network whose architectural design prioritizes parameter efficiency through depthwise separable convolutions and reduced gating complexity. The model was trained and validated using the PlantVillage benchmark dataset and achieved a classification accuracy of 99.8% with a macro-averaged F1-score of 94%, outperforming several state-of-the-art architectures. To assess robustness under real-world conditions, the trained model was further tested on leaf images collected from open-field environments near Eluru, South India. The field evaluation revealed a reduction in classification accuracy to 61.97%, indicating the impact of domain shift and environmental variability. To investigate potential contributing factors, soil parameters, including pH, temperature, moisture, and NPK levels, were monitored using an IoT-based Arduino sensing system over ten consecutive days. Rather than serving as direct inputs to the disease classification model, these environmental measurements were analyzed to assess their potential influence on disease symptom expression and the observed reduction in model performance under field conditions. The results suggest that environmental conditions may influence disease symptom expression and model transferability. This study highlights the importance of integrating DL-based disease recognition with environmental monitoring for reliable field-level agricultural applications. Nevertheless, computational complexity metrics, including inference latency and memory footprint, were not evaluated in the present work and are identified as a priority for future edge deployment studies.
J. Thirupathi, N. Malarvizhi, P. S. Brahmanandam· Sustainability· 0 citations
Plant-leaf diseases cause a significant damage to the agriculture yield if they are not diagnosed and treated early. It is also crucial in preserving global food security and promoting sustainable farming practices. These diseases can be detected through manual inspection but it is laborious to do by hand and the outcome is entirely dependent on the examiner. It has been observed that manual assessment prone to errors, particularly when there are irregularities in the illumination, abnormalities in the leaves, and small variations in disease symptoms. So, there is a need for a model that can successfully classify data by extracting features using computer vision and deep learning. This paper introduces a hybrid image classification deep-learning model using Convolutional Neural Network EfficientNetV2B3 combined with Transformer block made of Multi-head Attention and Multilayer Perceptron (Feedforward layers). EfficientNetV2B3 known for its scaling efficiency is used as a backbone for initial feature extraction, while the multi-head attention lets the model to learn relationships between distant regions by focusing on multiple areas of the image and the feedforward layers help model to figure complex features and then classified through a softmax output layer. The study tells that this model with less parameters, speed and high accuracy than existing image classification models like Resnet, VGG, Inception etc. generalizes better and performs well in detecting plant diseases with a validation accuracy of 99.70%.
Mohammad Farukh Hashmi, P. Meghana, Thota Bhagath· International Conference on...· 0 citations