Gearbox is one of the vital components in aircraft engines. If any small damage to gearbox, it can cause the breakdown of aircraft engine. Thus it is significant to study fault diagnosis in gearbox system. In this paper, two deep learning models (Long short term memory (LSTM) and Bi-directional long short term memory (BLSTM)) are proposed to classify the condition of gearbox into good or bad. These models are applied on aircraft gearbox vibration data in both time and frequency domain. A publicly available aircraft gearbox vibration dataset is used to evaluate the performance of proposed models. The results proved that accuracy achieved by LSTM and BLSTM are highly reliable and applicable in health monitoring of aircraft gearbox system in time domain as compared to frequency domain. Also, to show the superiority of proposed models for aircraft gearbox fault diagnosis, performance is compared with classical machine learning models.
Weed control is essential in agriculture since weeds reduce yields, increase production cost, impede harvesting, and degrade product quality. As a result, it is indeed critical to recognize weeds early in their vegetation cycle to evade negative impacts to crop growth. Earlier traditional methods used machine learning to determine crops along with weed species, but they had issues with weed detection efficiency at early growth stages. The current work proposes the implementation of a deep learning method that provides accurate results for precise weed recognition. Two different deep convolution neural networks have been used for our classification framework, namely Efficient Net B2 and Efficient Net B4. The plant seedlings dataset is utilized to investigate the proposed work. The evaluation metrics average accuracy, precision, recall, and F1-score were used. The findings demonstrate that the proposed approach is capable of differentiating between 12 species of a plant seedling dataset which contains 3 crops and 9 weeds. The average classification accuracy and F1 score are 99.00% for our Efficient Net B4 model and 97.00% for the Efficient Net B2. In addition, the proposed Efficient Net-B4 model performance is compared to the one of existing models on the plant seedlings dataset and the results showed that the proposed model Efficient Net B4 has superior performance. We intend to detect diseases in the identified plant species in our future research.
Farmers' primary concern is to reduce crop loss because of pests and diseases, which occur irrespective of the cultivation process used. Worldwide more than 40% of the agricultural output is lost due to plant pathogens, insects, and weed pests. Earlier farmers relied on agricultural experts to detect pests. Recently Deep learning methods have been utilized for insect pest detection to increase agricultural productivity. This paper presents two deep learning models based on Faster R-CNN Efficient Net B4 and Faster R-CNN Efficient Net B7 for accurate insect pest detection and classification. We validated our approach for 5, 10, and 15 class insect pests of the IP102 dataset. The findings illustrate that our proposed Faster R-CNN Efficient Net B7 model achieved an average classification accuracy of 99.00 %, 96.00 %, and 93.00 % for 5, 10, and 15 class insect pests outperforming other existing models. To detect insect pests less computation time is required for our proposed Faster-R-CNN method. The investigation reveals that our proposed Faster R-CNN model can be used to identify crop pests resulting in higher agricultural yield and crop protection.
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