High-Performance Plant Pest and Disease Detection Based on Model Ensemble with Inception Module and Cluster Algorithm

A deep learning approach for electric motor fault diagnosis based on modified InceptionV3

Electric motors are essential equipment widely employed in various sectors. However, factors such as prolonged operation, environmental conditions, and inadequate maintenance make electric motors prone to various failures. In this study, we propose a thermography-based motor fault detection method based on InceptionV3 model. To enhance the detection accuracy, we apply Contrast Limited Adaptive Histogram Equalization (CLAHE) to the input images. Furthermore, we improved the performance of the InceptionV3 by integrating a Squeeze-and-Excitation (SE) channel attention mechanism. The proposed model was tested using a dataset containing 369 thermal images of an electric motor with 11 types of faults. Image augmentation was employed to increase the data size and the evaluation was conducted using fivefold cross validation. Experimental results indicate that the proposed model can achieve accuracy, precision, recall, and F1 score of 98.82%, 98.93%, 98.82%, and 98.87%, respectively. Additionally, by freezing the fully connected layers of the InceptionV3 model for feature extraction and training a Support Vector Machines (SVM) to perform classification, it is able to achieve 100% detection rate across all four evaluation metrics. This research contributes to the field of industrial motor fault diagnosis. By incorporating deep learning techniques based on InceptionV3 and SE channel attention mechanism with a traditional classifier, the proposed method can accurately classify different motor faults.

Recent advances of microneedles biosensors for plants

As the lack of plants can affect the energy operation of the entire ecosystem, monitoring and improving the health status of plants is crucial. However, ordinary biosensing platforms lack accuracy and timeliness in monitoring plant growth status. In addition, the prevention and control of plant diseases often involve spraying and administering drugs, which is inefficient and prone to pollution. Microneedles have unique dimensions and shapes, and they have significant advantages as biosensors in the fields of sensing, detection, and drug delivery. Recent evidence suggests that microneedle biosensors can become effective tools for plant diagnosis and treatment. In this review, the comprehensive development of the application of microneedle biosensors in the field of plants is introduced, as well as their manufacturing processes and sensing and detection functions. Furthermore, the application of microneedle biosensors in this field is discussed, and future development directions are proposed.

High-Precision Tomato Disease Detection Using NanoSegmenter Based on Transformer and Lightweighting

With the rapid development of artificial intelligence and deep learning technologies, their applications in the field of agriculture, particularly in plant disease detection, have become increasingly extensive. This study focuses on the high-precision detection of tomato diseases, which is of paramount importance for agricultural economic benefits and food safety. To achieve this aim, a tomato disease image dataset was first constructed, and a NanoSegmenter model based on the Transformer structure was proposed. Additionally, lightweight technologies, such as the inverted bottleneck technique, quantization, and sparse attention mechanism, were introduced to optimize the model's performance and computational efficiency. The experimental results demonstrated excellent performance of the model in tomato disease detection tasks, achieving a precision of 0.98, a recall of 0.97, and an mIoU of 0.95, while the computational efficiency reached an inference speed of 37 FPS. In summary, this study provides an effective solution for high-precision detection of tomato diseases and offers insights and references for future research.

A High-Precision Plant Disease Detection Method Based on a Dynamic Pruning Gate Friendly to Low-Computing Platforms

Timely and accurate detection of plant diseases is a crucial research topic. A dynamic-pruning-based method for automatic detection of plant diseases in low-computing situations is proposed. The main contributions of this research work include the following: (1) the collection of datasets for four crops with a total of 12 diseases over a three-year history; (2) the proposition of a re-parameterization method to improve the boosting accuracy of convolutional neural networks; (3) the introduction of a dynamic pruning gate to dynamically control the network structure, enabling operation on hardware platforms with widely varying computational power; (4) the implementation of the theoretical model based on this paper and the development of the associated application. Experimental results demonstrate that the model can run on various computing platforms, including high-performance GPU platforms and low-power mobile terminal platforms, with an inference speed of 58 FPS, outperforming other mainstream models. In terms of model accuracy, subclasses with a low detection accuracy are enhanced through data augmentation and validated by ablation experiments. The model ultimately achieves an accuracy of 0.94.