Rice plays a vital role as a primary food source for over half of the world's population, and its production is critical for global food security. Nevertheless, rice cultivation is frequently affected by various diseases that can severely decrease yield and quality. Therefore, early and accurate detection of rice diseases is necessary to prevent their spread and minimize crop losses. In this research, we explore three mobile-compatible CNN architectures, namely ShuffleNet, MobileNetV2, and EfficientNet-B0, for rice leaf disease classification. These models are selected due to their compatibility with mobile devices, as they demand less computational power and memory compared to other CNN models. To enhance the performance of the three models, we added two fully connected layers separated by a dropout layer. We used early stop creation to prevent the model from being overfiting. The results of the study showed that the best performance was achieved by the EfficientNet-B0 model with an accuracy of 99.8%. Meanwhile, MobileNetV2 and ShuffleNet only achieved accuracies of 84.21% and 66.51%, respectively. This study shows that EfficientNet-B0 when combined with the proposed layer and early stop, can produce a high-accuracy model.
Keywords: rice leaf detection; green AI; smart agriculture; EfficientNet;
Charles Sturt University（查尔斯特大学）；Feni University（费尼大学）
A staple food in more than a hundred nations worldwide is rice (Oryza sativa). The cultivation of rice is vital to global economic growth. However, the main issue facing the agricultural industry is rice leaf disease. The quality and quantity of the crops have declined, and this is the main cause. As farmers in any country do not have much knowledge about rice leaf disease, they cannot diagnose rice leaf disease properly. That's why they cannot take proper care of rice leaves. As a result, the production is decreasing. From literature survey, it has seen that YOLOv5 exhibit the better result c…
Neural Network-based Study for Rice Leaf Disease Recognition and Classification: A Comparative Analysis Between Feature-based Model and Direct Imaging Model
Department of Computer Science & Engineering, Netrokona University（计算机科学与工程系，纳托克纳大学）；Department of Computer and Communication Engineering, International Islamic University Chittagong（计算机与通信工程系，国际伊斯兰大学钦德加角分校）；School of Computing Sciences and Computer Engineering, University of Southern Mississippi（计算科学与计算机工程学院，密西根州立大学）；Department of CSE, Daffodil International University（计算机科学与工程系，达福尔国际大学）
Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning
Begum Rokeya University, Rangpur（贝古姆·罗基亚大学，朗布尔）；Wilmington University（威尔明顿大学）；Washington University of Science and Technology（华盛顿科学与技术大学）；Central Michigan University（中密歇根大学）
National Electronics and Computer Technology Center (NECTEC)（国家电子与计算机技术中心（NECTEC））；Kasetsart University（泰国农业大学）
Fast, accurate and affordable rice disease detection method is required to assist rice farmers tackling equipment and expertise shortages problems. In this paper, we focused on the solution using computer vision technique to detect rice diseases from rice field photograph images. Dealing with images took in real-usage situation by general farmers is quite challenging due to various environmental factors, and rice leaf object size variation is one major factor caused performance gradation. To solve this problem, we presented a technique combining a CNN object detection with image tiling techniq…
Rice is the number one staple food in the country, as this serves as the primary livelihood for thousands of Filipino households. However, as the tradition continues, farmers are not familiar with the different types of rice leaf diseases that might compromise the entire rice crop. The need to address the common bacterial leaf blight in rice is a serious disease that can lead to reduced yields and even crop loss of up to 75%. This paper is a design and development of a rice leaf disease detection mobile application prototype using an algorithm used for image analysis. The researchers also used…
Rice is a staple food in the world's diet, and yet huge percentages of crop yields are lost each year to disease. To combat this problem, people have been searching for ways to automate disease diagnosis. Here, we extend on previous modelling work by analysing how disease-classification accuracy is sensitive to both model architecture and common computer vision techniques. In doing so, we maximise accuracy whilst working in the constraints of smaller model sizes, minimum GPUs and shorter training times. Whilst previous state-of-the-art models had 93% accuracy only predicting 5 diseases, we imp…
Empowering Agricultural Insights: RiceLeafBD -- A Novel Dataset and Optimal Model Selection for Rice Leaf Disease Diagnosis through Transfer Learning Technique
Leading University（领先大学）；Daffodil International University（水仙国际大学）；Queensborough Community College（皇后borough社区学院）
Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers
Nepal Engineering College（尼泊尔工程学院）；Pokhara University（博卡拉大学）；Tribhuvan University（特里布万大学）；Institute of Engineering（工程学院）
N-EIoU-YOLOv9: A Signal-Aware Bounding Box Regression Loss for Lightweight Mobile Detection of Rice Leaf Diseases
School of Electrical and Electronic Engineering, Hanoi University of Science and Technology（电子工程学院，河内科学技术大学）；Institute of Control Engineering and Automation, Hanoi University of Science and Technology（控制工程与自动化研究所，河内科学技术大学）
Convolutional neural networks (CNNs) have become popular especially in computer vision in the last few years because they achieved outstanding performance on different tasks, such as image classifications. We propose a nine-layer CNN for leaf identification using the famous Flavia and Foliage datasets. Usually the supervised learning of deep CNNs requires huge datasets for training. However, the used datasets contain only a few examples per plant species. Therefore, we apply data augmentation and transfer learning to prevent our network from overfitting. The trained CNNs achieve recognition ra…
Agriculture is an essential industry in the both society and economy of a country. However, the pests and diseases cause a great amount of reduction in agricultural production while there is not sufficient guidance for farmers to avoid this disaster. To address this problem, we apply CNNs to plant disease recognition by building a classification model. Within the dataset of 3,642 images of apple leaves, We use a pre-trained image classification model Restnet34 based on a Convolutional neural network (CNN) with the Fastai framework in order to save the training time. Overall, the accuracy of cl…
Although Convolutional neural networks (CNNs) are widely used for plant disease detection, they require a large number of training samples when dealing with wide variety of heterogeneous background. In this work, a CNN based dual phase method has been proposed which can work effectively on small rice grain disease dataset with heterogeneity. At the first phase, Faster RCNN method is applied for cropping out the significant portion (rice grain) from the image. This initial phase results in a secondary dataset of rice grains devoid of heterogeneous background. Disease classification is performed…
In this research, an attention-based depthwise separable neural network with Bayesian optimization (ADSNN-BO) is proposed to detect and classify rice disease from rice leaf images. Rice diseases frequently result in 20 to 40 \% corp production loss in yield and is highly related to the global economy. Rapid disease identification is critical to plan treatment promptly and reduce the corp losses. Rice disease diagnosis is still mainly performed manually. To achieve AI assisted rapid and accurate disease detection, we proposed the ADSNN-BO model based on MobileNet structure and augmented attenti…
United International University（联合国际大学）；Bangladesh Rice Research Institute（孟加拉国水稻研究所）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）
An accurate and timely detection of diseases and pests in rice plants can help farmers in applying timely treatment on the plants and thereby can reduce the economic losses substantially. Recent developments in deep learning based convolutional neural networks (CNN) have greatly improved the image classification accuracy. Being motivated by the success of CNNs in image classification, deep learning based approaches have been developed in this paper for detecting diseases and pests from rice plant images. The contribution of this paper is two fold: (i) State-of-the-art large scale architectures…
Bangladesh University of Business and Technology（孟加拉国商业技术大学）
A disease that limits a plant from its maximal capacity is defined as plant disease. From the perspective of agriculture, diagnosing plant disease is crucial, as diseases often limit plants' production capacity. However, manual approaches to recognize plant diseases are often temporal, challenging, and time-consuming. Therefore, computerized recognition of plant diseases is highly desired in the field of agricultural automation. Due to the recent improvement of computer vision, identifying diseases using leaf images of a particular plant has already been introduced. Nevertheless, the most intr…
TCLeaf-Net: a transformer-convolution framework with global-local attention for robust in-field lesion-level plant leaf disease detection
School of Optoelectronic Science and Engineering, South China Normal University（光电科学与工程学院，华南师范大学）；College of Engineering Physics, Shenzhen Technology University（工程物理学院，深圳科技大学）；Faculty of Intelligent Technology, Shanghai Institute of Technology（智能技术学院，上海理工大学）；Graduate School of Information Sciences, Tohoku University（信息科学研究生院，东北大学）
CT-CLIP: A Multi-modal Fusion Framework for Robust Apple Leaf Disease Recognition in Complex Environments
College of Information Science and Engineering, Shandong Agricultural University（山东农业大学信息科学与工程学院）；Shandong Provincial Smart Agriculture Characteristic Laboratory（山东省智慧农业特色实验室）；Centre for Vision Speech and Signal Processing (CVSSP), University of Surrey（萨里大学视觉语音与信号处理中心）；School of Mechanical and Electronic Engineering, Shandong Agricultural Engineering College（山东农业工程学院机械电子工程学院）；Australian Institute for Machine Learning, University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）
In this paper, a deep mixture of diverse experts algorithm is developed for seamlessly combining a set of base deep CNNs (convolutional neural networks) with diverse outputs (task spaces), e.g., such base deep CNNs are trained to recognize different subsets of tens of thousands of atomic object classes. First, a two-layer (category layer and object class layer) ontology is constructed to achieve more effective solution for task group generation, e.g., assigning the semantically-related atomic object classes at the sibling leaf nodes into the same task group because they may share similar learn…
Soybean Disease Detection via Interpretable Hybrid CNN-GNN: Integrating MobileNetV2 and GraphSAGE with Cross-Modal Attention
Biology Unit, Okinawa Institute of Science；Technology Graduate University, Okinawa 904-0412, Japan (e-mail: )；Thomas Lord Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90007, USA (e-mail: )；Department of Computer Science, American International University-Bangladesh, Dhaka 1229, Bangladesh (e-mail: , )；Department of Robotics；Automation, Multimedia University, Melaka, Malaysia (e-mail: )；Department of Computer Science \& Engineering, Techno International New Town, Kolkata 700156, India (e-mail: )
Penn State University（宾夕法尼亚州立大学）；International Institute of Tropical Agriculture（国际热带农业研究所）
Convolutional neural network models (CNNs) have made major advances in computer vision tasks in the last five years. Given the challenge in collecting real world datasets, most studies report performance metrics based on available research datasets. In scenarios where CNNs are to be deployed on images or videos from mobile devices, models are presented with new challenges due to lighting, angle, and camera specifications, which are not accounted for in research datasets. It is essential for assessment to also be conducted on real world datasets if such models are to be reliably integrated with…
