Rice Diseases Detection and Classification Using Attention Based Neural Network and Bayesian Optimization (2201.00893v1)

Abstract: 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 attention mechanism. Moreover, Bayesian optimization method is applied to tune hyper-parameters of the model. Cross-validated classification experiments are conducted based on a public rice disease dataset with four categories in total. The experimental results demonstrate that our mobile compatible ADSNN-BO model achieves a test accuracy of 94.65\%, which outperforms all of the state-of-the-art models tested. To check the interpretability of our proposed model, feature analysis including activation map and filters visualization approach are also conducted. Results show that our proposed attention-based mechanism can more effectively guide the ADSNN-BO model to learn informative features. The outcome of this research will promote the implementation of artificial intelligence for fast plant disease diagnosis and control in the agricultural field.

• The paper introduces an Attention-Based Depthwise Separable Neural Network optimized via Bayesian techniques, achieving a detection accuracy of 94.65% for rice diseases.
• It leverages a mobile-compatible architecture by enhancing MobileNet with attention mechanisms to facilitate on-device, efficient disease diagnosis using digital imagery.
• Comparative evaluations against models like VGG16 and ResNet demonstrate its superior performance and significant potential for improving agricultural disease management.

Analysis of an Attention-Based Neural Network for Rice Disease Detection

The paper "Rice Diseases Detection and Classification Using Attention Based Neural Network and Bayesian Optimization" presents a meticulous investigation centered around employing deep learning methodologies to refine the detection and classification of rice diseases using digital imagery. Rice diseases present significant challenges, contributing substantially to global economic losses with quantitative implications where crops may face a production dip of 20 to 40%. Thus, efficient disease identification is paramount. The research at hand suggests an artificial intelligence-driven approach to expedite and enhance diagnostic processes, focusing on an Attention-Based Depthwise Separable Neural Network (ADSNN) optimized through Bayesian techniques.

The research recognizes mobile-compatible identification mechanisms as a critical need, particularly due to the ease of access to imaging technology on portable devices. Leveraging the architecture of MobileNet and enhancing it with attention mechanisms, the paper aims to produce a model that is both efficient and reliable in a mobile setting. This would represent a significant advance over traditional identification processes, which are largely manual and prone to error and delay.

The crux of the methodology involves deploying the ADSNN model, integrated with a Bayesian optimization strategy for hyper-parameter tuning to refine the model's performance. The paper discusses the meticulous application of attention layers to improve feature extraction capabilities, and the depthwise separable convolutions contribute to reducing computational demands. Notably, this proposed model is pitted against well-established architectures like VGG16, ResNet, DenseNet, InceptionV3, and Xception, demonstrating its superior predictive efficacy with a commendable accuracy of 94.65%.

Some specific features that are distinctive in this paper include the detailed comparative analysis of the ADSNN-BO against existing architectures, where it exhibits significant advantages in various performance metrics. The filter visualization approach accentuates the capacity of the model to focus more effectively on informative features critical to distinguishing between different types of rice diseases—brown spot, rice hispa damage, and leaf blast.

The implications of the model are vast, particularly in promoting precisely-timed, AI-assisted diagnosis and treatment plans that could alleviate the financial burden linked to rice crop diseases. On a theoretical level, this reinforces the application of deep learning in agricultural contexts, aligning with ongoing discussions about the promises of technology in enhancing food security and sustainable agricultural practices.

Looking ahead, there are avenues for further exploration, notably in broadening the model's applicability across different datasets and contexts, integrating additional variables such as soil and weather data for a more holistic approach to disease detection. There may also be potential to optimize the hyper-parameters further using other advanced techniques that could offer even more efficient model performance.

Conclusively, while the model is an improvement over existing methodologies, the paper provides a basis for continued refinement and exploration of AI technologies in agriculture, emphasizing computational efficiency and accuracy, crucial for real-world implementation.