Study of seasonal incidence and impact of abiotic factors on sucking pests of brinjal

The present investigation was undertaken to find the impact of abiotic factors on seasonal incidence and sucking pest complex of brinjal under field conditions during kharif 2015-2016. The incidence of leaf hopper population (2.80 Lh/L i.e., Leaf hopper mean population/leaf) was noticed during 34th standard week and reached peak by 40th standard week (5.00 Lh/L) (October) whereas the aphid population was noticed during the 34th standard week (3.00 Lh/L) and peak population observed during the 40th standard week (4.60 Lh/L) (October). Correlation studies showed that among the various abiotic factors, maximum temperature showed highly significant positive correlation (r= 0.77) and sunshine hours (r = 0.61) showed significant positive correlation with the leaf hopper population. In case of aphid population, maximum temperature showed significant positive correlation (r = 0.70), rainfall showed highly significant negative correlation (r = -0.74) and relative humidity evening (r = -0.59) showed significant negative correlation with aphid population. The present investigations will give a brief idea about how the abiotic factors influencing the sucking pests of brinjal

Edge Intelligence with Light Weight CNN Model for Surface Defect Detection in Manufacturing Industry

Surface defect identification is essential for maintaining and improving the quality of industrial products. However, numerous environmental factors, including reflection, radiance, light, and material, affect the defect detection process, considerably increasing the difficulty of detecting surface defects. Deep Learning, a part of Artificial intelligence, can detect surface defects in the industrial sector. However, conventional deep learning techniques are heavy in terms of expensive GPU requirements to support massive computations during the defect detection process.CondenseNetV2, a Lightweight CNN-based model, which performs well on microscopic defect inspection, and can be operated on low-frequency edge devices, was proposed in this research. It provides sufficient feature extractions with little computational overhead by reusing a set of the existing Sparse Feature Reactivation module. The training data are subjected to data augmentation techniques, and the hyper-parameters of the proposed model are fine-tuned with transfer learning. The model was tested extensively with two real datasets while running on an edge device (NVIDIA Jetson Xavier Nx SOM). The experiment results confirm that the projected model can efficiently detect the faults in the real-world environment while reliably and robustly diagnosing them

Accuracy of preoperative ultrasonography in measuring tumor thickness and predicting the incidence of cervical lymph node metastasis in oral cancer

Context: A major determinant of the prognosis of oral cancer is the risk of cervical lymph node metastasis. Several factors have been assessed preoperatively to predict the risk of lymph node metastasis; among them, tumor thickness is proved to be a significant predictor of lymph node metastasis. Ultrasonography (US) is a noninvasive, rapid, easily repeatable, and economical examination to measure tumor thickness. This study is undertaken for evaluating the usefulness of US to predict neck metastasis. Aim: To measure tumor thickness in oral cancer with preoperative US and to predict occult cervical lymph node metastasis. Materials and Methods: In all, 43 patients with biopsy-proven squamous cell carcinoma of tongue/buccal mucosa underwent preoperative US to measure tumor thickness. Statistical Analysis: Tumor thickness from histolopathology and US was analyzed using Pearson's product moment correlation. Fisher's exact test was used to assess the relationship between tumor thickness and the risk of cervical lymph node metastasis. Results: There was a significant correlation between preoperative US and histological measures of tumor thickness (correlation coefficient 0.961, P 0.05. Conclusion: Preoperative US is an accurate measure of maximal tumor thickness. Tumor thickness ≥5 mm can be considered as a risk factor for cervical lymph nodal metastasis

Edge Intelligence with Light Weight CNN Model for Surface Defect Detection in Manufacturing Industry

178-184Surface defect identification is essential for maintaining and improving the quality of industrial products. However, numerous environmental factors, including reflection, radiance, light, and material, affect the defect detection process, considerably increasing the difficulty of detecting surface defects. Deep Learning, a part of Artificial intelligence, can detect surface defects in the industrial sector. However, conventional deep learning techniques are heavy in terms of expensive GPU requirements to support massive computations during the defect detection process.CondenseNetV2, a Lightweight CNN-based model, which performs well on microscopic defect inspection, and can be operated on lowfrequency edge devices, was proposed in this research. It provides sufficient feature extractions with little computational overhead by reusing a set of the existing Sparse Feature Reactivation module. The training data are subjected to data augmentation techniques, and the hyper-parameters of the proposed model are fine-tuned with transfer learning. The model was tested extensively with two real datasets while running on an edge device (NVIDIA Jetson Xavier Nx SOM). The experiment results confirm that the projected model can efficiently detect the faults in the real-world environment while reliably and robustly diagnosing them

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