The primary contribution of this work lies in the empirical demonstration that MobileNetV2, without architectural modification, can serve as a practical and accessible diagnostic tool when integrated into a web-based deployment pipeline, offering a favorable trade-off between accuracy and computational cost compared to heavier architectures.
Abstract
This study investigates the hypothesis that a lightweight Convolutional Neural Network (CNN), specifically MobileNetV2, can achieve competitive classification accuracy for corn leaf disease detection while maintaining computational efficiency suitable for real-time web-based deployment. Corn is a critical agricultural commodity, and plant diseases significantly threaten productivity, causing economic losses and reduced yields. Traditional identification methods depend on manual observation, which is time-consuming, subjective, and error-prone at scale. While recent deep learning approaches such as Vision Transformers and hybrid architectures have demonstrated high accuracy, their computational demands limit practical deployment in resource-constrained agricultural environments. To address this gap, this research employs MobileNetV2 with transfer learning from ImageNet, leveraging depthwise separable convolutions for efficient feature extraction. The dataset comprises 3,852 corn leaf images categorized into four classes: Healthy, Common Rust, Gray Leaf Spot, and Northern Leaf Blight. Preprocessing includes image resizing to 224×224 pixels, normalization, and 80:20 train-validation splitting. The model was trained using TensorFlow and deployed via a Streamlit-based web interface for real-time inference. Experimental results demonstrate a training accuracy of 96.76% and a validation accuracy of 92.86%, with inference confidence reaching 99.80% during testing. The primary contribution of this work lies in the empirical demonstration that MobileNetV2, without architectural modification, can serve as a practical and accessible diagnostic tool when integrated into a web-based deployment pipeline, offering a favorable trade-off between accuracy and computational cost compared to heavier architectures.
A scalable image-based framework for automatic rice leaf disease detection and monitoring using deep learning and cloud-enabled analytics and enables deployment on smartphones, drones, and edge devices for smart agriculture applications.
B Venkata Ramulu, Dr. Geeta Tripathi· International Journal of Eng...· 0 citations
This study introduces a lightweight convolutional neural network architecture specifically designed for accurately and efficiently detecting grapevine leaf diseases—including Black Rot, ESCA, and Leaf Blight—based on image classification.
Ashraf Mustafa, Araz Rajab Abrahim· Science Journal of Universit...· 0 citations
Agriculture is significant in world food production and global economic stability, but leaf disease and pest infection can cause a threat to crop quantity and quality. Thus, it became crucial to have timely and accurate identification of plant leaf disease to prevent loss in agriculture on a large scale and to have sustainable crop management. This paper introduces LeafLiteX, a lightweight mobile-based deep learning application for real-time detection and classification of leaf diseases. This application uses U-Net segmentation to precisely find leaf regions and MobileNetV3-Large to quickly classify diseases with less computation on the computer. The application performs end-to-end processing, from image acquisition to segmentation and disease prediction on mobile devices. An experiment was performed on publicly available crop disease datasets containing various leaf images having different disease types. The model obtained an accuracy of 98.85% showing improved generalization with minimal latency. The design of the model was such that it was suitable for inference on-device while still being robust enough despite changes in lighting conditions, background noise, and camera resolution. LeafLiteX is a low-cost, easy to use, offline-capable, and in-the-moment decision-making supportive diagnostic application that supports farmers and agrarians who require early detection. This paper demonstrates the capabilities that can be achieved using edge-optimized machine learning and computer vision to support the development of smart agriculture technologies. While traditional methods rely solely on classification, this research focuses more on practical implementation by incorporating segmentation, lightweight classification, and explainability to develop a mobile-friendly model.
P. Verma, Divya Midhun, Hemalatha et al.· Scientific Reports· 0 citations
Corn Transfer Learning Network (CTL-Net), an end-to-end hybrid deep learning model for corn leaf disease identification, allows accurate and interpretable predictions in a computationally efficient manner, making it an excellent candidate for mobile and resource-limited applications in precision agriculture.
K. P. Kumar, Y. Kuma· Engineering, Technology &...· 0 citations
Background: Groundnut farming is affected by several leaf diseases that reduce crop yield. Farmers often rely on visual inspection, which can be inaccurate and time-consuming. Early and precise identification of leaf diseases is essential for effective crop management. This study presents a deep learning-based solution to automate disease detection. Methods: A modified EfficientNetB0 architecture is proposed for classifying five types of groundnut leaf conditions: healthy, leaf spot (early and late), alternaria leaf spot, rust and rosette. The dataset is sourced from the Mendeley database that was collected from Ramchandrapur village in West Bengal, India, under natural lighting. A total of 1,720 images were captured using a DSLR camera. After verification and cleaning, the dataset was split into 1,204 training and 516 testing images. All images were resized, normalized and label-encoded. Data augmentation techniques such as rotation, flipping and zoom were used to improve generalization. Regularization was applied to reduce overfitting. The model was trained for 100 epochs using the RMSprop optimizer and early stopping. Result: The model achieved a test accuracy of 99.22%. Evaluation metrics confirm high performance across all classes. The model outperformed existing methods such as ResNet50 (82.3%), CNN with progressive resizing (96.12%) and LeafNet (97.23%). It also maintained low training and validation loss throughout training. These results highlight the model’s robustness, accuracy and potential for real-time field applications. The approach is lightweight and suitable for mobile-based disease detection tools.
Jie-Shin Lin, Y. Tai, Suh-Chen Hsiao et al.· Legume Research An Internati...· 0 citations
Plant leaf diseases significantly reduce agricultural productivity and crop yield worldwide, making early and accurate detection essential to prevent large-scale crop damage. Traditional disease identification methods rely on manual inspection by experts, which is time-consuming, costly, and often inaccessible to farmers in rural areas. This paper proposes an AI-based leaf disease detection system using deep learning and transfer learning, in which EfficientNetB5 serves as a pretrained feature extractor to classify 38 plant disease categories spanning 14 crop species. Preprocessing includes HSV-based leaf segmentation, resizing to 456×456 pixels, and EfficientNet-specific normalization. A compact two-layer dense classifier is trained on the 2,048-dimensional feature vectors produced by the frozen backbone. The system achieves an overall validation accuracy of 96.49%, macro-average precision of 0.97, recall of 0.96, and F1-score of 0.96 on 2,280 held-out images. Beyond classification, the system provides actionable cure and precautionary recommendations for every detected disease, making it directly useful to smallholder farmers. Comparative analysis with ResNet50, VGG16, and MobileNetV2 confirms that EfficientNetB5 achieves the highest accuracy with a favorable parameter-to-performance ratio. Multi-class ROC evaluation further demonstrates strong discriminative capability across all disease categories.
Kuppala Ajay Kumar, Yella Sai Krishna, R. Kumar et al.· 2026 6th International Confe...· 0 citations
