Aug 2026· Engineering, Technology & Applied Science Research· 0 citations· 31 references
TL;DR
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.
Abstract
Corn is a staple crop of global significance; however, foliar diseases may lead to 30–60% yield loss if not detected at an early stage. Conventional visual inspection is time-consuming, subjective, and difficult to scale for smallholder farmers worldwide. To overcome these issues, we present Corn Transfer Learning Network (CTL-Net), an end-to-end hybrid deep learning model for corn leaf disease identification. CTL-Net, which combines Inception-ResNet-v2 as the backbone and MobileNetV3 as a feature extractor in parallel convolutional streams, simultaneously learns diverse scales of texture information from low-level textures, mid-level structural patterns, and high-level disease semantics of RGB leaf images. Adaptive feature fusion is formulated through learnable weighting coefficients and bi-directional spatial–channel attention mechanisms, which further enhance feature discriminability and robustness. The proposed approach is tested on an extensive dataset of 12,456 images from 10 corn diseases, including Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Cercospora Leaf Spot, acquired under controlled and real-field conditions. CTL-Net attains the highest classification accuracy of 99.42%, outperforming DenseNet121 (97.92%), EfficientNetB3 (97.35%), and general stacking models (97.89%). Robustness experiments demonstrate the effectiveness of the proposed method against illumination variations, additive noise, and partial occlusions. CTL-Net enables real-time inference with a latency of 42 ms on an NVIDIA RTX 3090 GPU. Gradient-weighted Class Activation Mapping++ (Grad-CAM++)-based interpretability analysis results in a mean Intersection over Union (IoU) of 87.6% with expert-annotated disease regions. Five-fold cross-validation, ablation studies, and statistical significance testing (p < 0.001) demonstrated that the contribution of each architectural component is significant. To summarize, CTL-Net allows accurate and interpretable predictions in a computationally efficient manner, making it an excellent candidate for mobile and resource-limited applications in precision agriculture.
A deep hybrid Convolutional Neural Network –Transformer architecture is introduced by combining ConvNeXt Large (ConvNeXt-L) and Swin Transformer architecture by combining ConvNeXt Large (ConvNeXt-L) and Swin Transformer (as local feature extractor) and Swin Transformer (as global context encoder) to predict tomato leaf diseases.
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.
Ammar Kamil Al Abror, Melika Debiyana Putri, Yunanda Rizki Sitompul et al.· bit-Tech· 0 citations
Agricultural production is exposed to losses when plants became infected with diseases from the environment, which in turn threatens food security; therefore, having the ability to detect plant diseases early on gives farmers the opportunity to minimize the loss they incur. Sunflower (Helianthus annuus) is Ranking among other oilseed crops worldwide for total agricultural production, and it has a high risk of being affected by diseases that cause reduced crop yield and quality, e.g., Downy mildew, grey mould, and leaf scars. Disease detection using conventional methods usually involves visual inspections by trained personnel; this process, however, is very time consuming, subjective, and could produce numerous errors. To counteract these issues, this paper proposes a hybrid learning framework for plant disease detection that combines deep feature-driven image analysis with a combination of two classifiers used in an ensemble mode to create a single final classification. The use of MobileNetV2 will act as the feature extraction process for the hybrid learning framework, while the classifier portion of the hybrid learning framework will consist of two K-Nearest Neighbour classifiers. Classification using SVM and RF classifiers will be completed using a combination of ensemble voting. Grad-CAM (gradient-weighted class activation maps) will be used to improve the interpretability of the disease classification results by identifying affected areas on images. Test results for the hybrid learning framework reveal an overall accuracy of 98.8%, compared to the accuracy of the two classifiers used separately: 95.8% for CNN, 93% for SVM, and 96.0% for RF. The confusion matrix for the two classifiers shows a significant number of accurate classifications with minimal misclassifications. The proposed hybrid learning framework for plant disease detection provides an effective method for detecting plant diseases in real time, interpretable results, and potential for scalability.
Kshirsagar Soumya, Mr. G Sekhar Reddy, D. G. L. A. Babu et al.· International Conference Com...· 0 citations
This study delineates five advanced convolutional neural network designs that employ deep learning for the classification of wheat weeds. The dataset consists of greyscale photos taken in agricultural fields, enhanced with RGB lighting and cropped to 256×256×3 pixels. Normalization, batch-wise augmentation, contrast stretching, or histogram equalization were all part of the preprocessing that improved picture quality and model learning efficiency. These enhancements developed feature extraction by increasing picture contrast and homogeneity. A VGG16-based model with further Conv2D layers and spatial attention is one of the five models recommended. An additional design, inspired by ResNet50 that applies residual blocks and worldwide average pooling. A hybrid RNN integrated ICNA-CNN or LSTM for spatial-temporal content learning is another. Lastly, InceptionResNetV2 is enhanced with CNN layers and a query-key attention mechanism. We used the Adam optimizer and categorical cross-entropy to estimate the loss throughout the training for the round. At the end, all the models had ReLU activation, batch normalization, MaxPooling2D, and a dropout charge of 0.5 to keep them from overfitting. Upon evaluating their performance based on accuracy, recall, reliability, and training loss, VGG16 emerged as the standout model, achieving an impressive 99.07% precision and a remarkably low training loss of just 0.0079. The study determined that VGG16 is the optimal choice for precise wheat–weed categorization because to its superior generalizability and accuracy.
A. Bodhale, S. Verma, Aishwary Bodhale· International Journal of Inf...· 0 citations
A novel hybrid deep learning framework integrating Convolutional Neural Networks, Transformer-based attention mechanisms, and Long Short-Term Memory networks for spatio-temporal cotton leaf disease detection and classification is proposed, suitable for intelligent precision agriculture systems and real-time disease monitoring applications.
Prajakta Sunil Gupta, A. V. Zade· International journal of com...· 0 citations
Maize leaf diseases are also major causes of loss in the productivity of the crop, and they are also visually similar to each other, which frequently causes a misclassification under manual inspection. The paper fills this gap of the necessity of a practical and automated solution by coming up with a hybrid of deep learning frameworks with the ability to not only classify diseases with high accuracy but also localize region of interest (ROI) with high precision. The aim is to strengthen the early detection with an integrated system that is comprised of both the advanced models of transfer learning and the YOLO-based detection. The dataset is processed systematically to enhance normalization, preprocessing, and augmentation of the dataset to enhance its strength in a variety of imaging conditions. Several deep learning models VGG16, ResNet50, InceptionV3 and MobileNetV2 and lightweight and ensemble versions were tested. The results indicate that Ensemble Model 1 (VGG16 + MobileNetV2) performs the best, with the accuracy of 96% and the precision, recall, and F1-score of 95%, 95%, and 95%, respectively, being higher than individual classifiers and Ensemble Model 2 do. The ability to mix complementary feature extraction tasks of deep and lightweight networks is seen to be powerful in this superior performance. YOLO component is highly effective in detecting diseased regions on the leaf with high confidence identified and produces clear and understandable bound-box results. Results provide validation that combining classification and object detection has benefits on the diagnostic reliability and decision-making capability which can be used for actionable decisions in agriculture. The area of their activity is also the real-time deployment, applicability to cross crops, and integration with mobile or IoT-based smart farming systems, which allows extending the disease management solutions to scale and efficiency.
T. Varma, Sanjeev Sharma, Bhupendra Singh· Journal of Intelligent Decis...· 0 citations