2026· Journal of Agricultural Digitalization Research· 0 citations
TL;DR
Performance evaluation using accuracy, model size, time per image, and number of parameters showed that the proposed model achieved high accuracy and provided better discrimination between visually similar disease classes.
Abstract
Rice is one of the most popular and extensively grown crops. More than fifty percent of the global population consumes it as a staple food. However, a number of diseases affect the quality of crop which also affect the total production of the yield. Identification of such disorders is important for crop management. Traditional disease detection methods are time consuming and susceptible to human error because they rely on personal evaluation by professionals.
In our work, we have built a task specific system. This system uses a convolutional neural network based framework for detection and classification of rice leaf diseases by looking at leaf pictures. Two datasets are used in this work. The rice leaf disease dataset contains 16000 images which are distributed into four classes with 4000 images in each class and additionally 139 images of brown spot class from A dataset of rice leaf disease, to add variation in the dataset, is used. Duplicate images were cleaned and removed. Images were preprocessed. For fair evaluation, the dataset was divided into training set (70%), validation set (15%), and testing set (15%). With 25 epochs, by employing Adam optimizer, model was trained. It is designed to extract visual features using four convolutional layers, five batch normalization layers, four pooling layers and two fully connected layers.
To see how well trained model is performed, it is evaluated against some transfer learning models like MobileNetV2, ResNet50 and Vgg16 on the same dataset. Performance evaluation using accuracy, model size, time per image, and number of parameters showed that the proposed model achieved high accuracy and provided better discrimination between visually similar disease classes. Proposed model achieves 99.33% accuracy, has model size of 1.618 MB, 9.512 ms time per image, 424260 parameters. In contrast MobileNetV2 achieved 97.17% accuracy, has model size of 8.633 MB, 14.141 ms time per image and 2263108 parameters. ResNet50 achieved 44.93% accuracy, has model size of 90.01 MB, 64.661 ms time per image and 23595908 parameters. Vgg16 achieved 79.75% accuracy, has model size of 56.13 MB, 193.830 ms time per image and 14716740 parameters. Proposed model has performed well and doesn’t need numerous parameters, this shows that for practical implementation, it can be appropriate.
Agriculture is a key component in ensuring food security in the world and one of the most common staple foods in the world is rice. But there are a number of leaf diseases affecting rice, like bacterial leaf blight, brown spot, and rice blast, which cause lower production yield. In order to limit economic losses, it is necessary to be able to diagnose these diseases on time. This paper presents an intelligent prediction and classification of rice leaf disease prediction and classification using deep learning and transfer learning approaches. For that purpose, the proposed model uses two pre-trained transfer learning models, DenseNet201 and EfficientNetB3, and a conventional CNN, to classify healthy and diseased rice leaves from a set of image data. The performances of the three models are measured using the following performance measures: accuracy, precision, recall, and F1-score. From the experiments conducted, it can be noted that the best performing model among all three models is the EfficientNetB3, which shows an excellent classification accuracy of 95%. It performs better than both DenseNet201, whose accuracy is 92% and CNN model, whose accuracy is 83.5%. The reason behind the excellent performance of the EfficientNetB3 is that the architecture of the model is optimized in such a way that it is able to extract the most discriminative features with less computational complexity. The proposed framework can offer an efficient, reliable, and automated solution for the early detection of the diseases in the leaves of rice crops.
Vandana Samanthula, N. Valli, Sultanov Sirojiddin et al.· 2026 6th International Confe...· 0 citations
The clinical reliability of different deep learning models of various representational capacities has been tested while using ImageNet pre-trained parameters and the experimental results show that MobileNetV2 achieves the highest accuracy of 96.15%, outperforming deep CNN (76.92%) and medium CNN (61.54%).
Divya Singhal, Ankit Verma, Amit Kumar Gupta et al.· International Journal of Dru...· 0 citations
Agriculture remains at the core of human life, providing staple food and livelihood for millions worldwide. Among its different domains, food crops directly enter the human system, while cash crops are grown primarily for monetary gains. Maize, as one of the most extensively grown and consumed food crops, is of gigantic economic and nutritional value, particularly in West Africa. Unfortunately, maize plant diseases have adversely impacted farmer yields, resulting in decreased maize production. This research aims to create a system that can identify diseases in maize based on images of the leaves. Three deep convolutional neural network (DCNN) models, namely MobileNetV2, InceptionV3, and ResNet50, were selected to achieve this goal because of their prior ability. The transfer learning technique was adopted to develop new models for classifying maize disease using a hybrid maize leaf image dataset comprising 6,543 images from the University of Pretoria and Kaggle repositories. Furthermore, the dataset was split into 80% for training, 10% for validation, and 10% for testing and the three model were configured and trained. According to the evaluation results, MobileNetV2 was the best model for classifying maize leaf diseases, with a 95.29% classification accuracy. In comparison, InceptionV3 and ResNet-50 yielded accuracies of 92.18% and 74.48%, respectively. The MobileNetV2 was chosen for the dual deployment in both a web-based and a mobile application due to its exceptional performance metrics and its lightweight API. Evaluation of the deployed MobileNetV2 model on both the web and mobile applications showed that it achieved an average confidence rate of 91% on both platforms, with response times of 0.159 s and 0.163 s, and throughputs of 5.88 images/s and 6.28 images/s, respectively. This research offers a simple and intuitive tool for users and agricultural professionals to quickly detect maize leaf diseases and take necessary precautions to mitigate losses.
Kennedy O. Okokpujie, Osondu C. Ronald, Joshua S. Mommoh et al.· International Journal of Eng...· 0 citations
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, Geeta Tripathi· International Journal of Eng...· 0 citations
The general health condition of the crop is very essential for increasing the agricultural production and global food security. Fungal diseases in leaves may spread rapidly and result in yield reduction if not detected timely. To this end, an intelligent system is constructed based on deep convolutional neural network to classify the leaf diseases of plants using images. The CNN model can be further qualified by a diverse dataset coupled with preprocess and data augmentation for higher generalization capability. The CNN architecture drawn learns highly packed visual feature from the plant leaf image and can classify several types of disease with very high accuracy. Our experiment results verified the efficacy of the package with 92.23% classification accuracy which outperformed traditional image processing combined with classical machine learning approaches in the past. It can effectively be used for actual farming in the field with positive contribution to sustainable farming practices.
D.P. Rohitha, A.T.Vishaka, K. Subhashini· International Conference Com...· 0 citations
The results indicate that in the architecture field the application of deep learning methodologies is effective, suggesting that automated detection of diseases by using sophisticated image analysis manages crop diseases identification efficiently.
Rondik J. Hassan, Kazheen Ismael Taher· International journal of com...· 0 citations
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