A novel CNN gap layer for growth prediction of palm tree plantlings.

Monitoring palm tree seedlings and plantlings presents a formidable challenge because of the microscopic size of these organisms and the absence of distinguishing morphological characteristics. There is a demand for technical approaches that can provide restoration specialists with palm tree seedling monitoring systems that are high-resolution, quick, and environmentally friendly. It is possible that counting plantlings and identifying them down to the genus level will be an extremely time-consuming and challenging task. It has been demonstrated that convolutional neural networks, or CNNs, are effective in many aspects of image recognition; however, the performance of CNNs differs depending on the application. The performance of the existing CNN-based models for monitoring and predicting plantlings growth could be further improved. To achieve this, a novel Gap Layer modified CNN architecture (GL-CNN) has been proposed with an IoT effective monitoring system and UAV technology. The UAV is employed for capturing plantlings images and the IoT model is utilized for obtaining the ground truth information of the plantlings health. The proposed model is trained to predict the successful and poor seedling growth for a given set of palm tree plantling images. The proposed GL-CNN architecture is novel in terms of defined convolution layers and the gap layer designed for output classification. There are two 64×3 conv layers, two 128×3 conv layers, two 256×3 conv layers and one 512×3 conv layer for processing of input image. The output obtained from the gap layer is modulated using the ReLU classifier for determining the seedling classification. To evaluate the proposed system, a new dataset of palm tree plantlings was collected in real time using UAV technology. This dataset consists of images of palm tree plantlings. The evaluation results showed that the proposed GL-CNN model performed better than the existing CNN architectures with an average accuracy of 95.96%.

Molecular Characterization of Geographically Different Banana bunchy top virus Isolates in India.

Banana bunchy top disease (BBTD) caused by Banana bunchy top virus (BBTV) is one of the most devastating diseases of banana and poses a serious threat for cultivars like Hill Banana (Syn: Virupakshi) and Grand Naine in India. In this study, we have cloned and sequenced the complete genome comprised of six DNA components of BBTV infecting Hill Banana grown in lower Pulney hills, Tamil Nadu State, India. The complete genome sequence of this hill banana isolate showed high degree of similarity with the corresponding sequences of BBTV isolates originating from Lucknow, Uttar Pradesh State, India, and from Fiji, Egypt, Pakistan, and Australia. In addition, sixteen coat protein (CP) and thirteen replicase genes (Rep) sequences of BBTV isolates collected from different banana growing states of India were cloned and sequenced. The replicase sequences of 13 isolates showed high degree of similarity with that of South Pacific group of BBTV isolates. However, the CP gene of BBTV isolates from Shervroy and Kodaikanal hills of Tamil Nadu showed higher amino acid sequence variability compared to other isolates. Another hill banana isolate from Meghalaya state had 23 nucleotide substitutions in the CP gene but the amino acid sequence was conserved. This is the first report of the characterization of a complete genome of BBTV occurring in the high altitudes of India. Our study revealed that the Indian BBTV isolates with distinct geographical origins belongs to the South Pacific group, except Shervroy and Kodaikanal hill isolates which neither belong to the South Pacific nor the Asian group.

Sustainable leprosy related disability care within integrated general health services: findings from Salem District, India.

The main focus of leprosy control has been case detection and treatment delivery with relative neglect of prevention of disability. Absence of reliable data and lack of research have added to the problem. This raised concerns about the capacity of the general health system to address the needs of people living with leprosy-related disabilities. In this prospective study appropriate services for people living with leprosy-related disabilities were introduced in the form of self-care training, guidance and monitoring by the general health staff facilitated by a non-governmental organisation leprosy centre in a district in south India with a population of 3.1 million (estimated in 2005). The staff identified 1232 people with leprosy-related disabilities and trained them in self-care. Follow-up assessments indicated that 86% were found to be practising self-care regularly and all the 239 general health workers were found to be actively involved. The most heartening outcome was the healing of plantar ulcers in 70% of people at the 1-year follow up. This intervention is sustainable because of the simplicity of the procedures and the involvement of all health staff including supervisors.

Pollution and its control in asbestos milling processes in India.

An intervention study has been conducted in chrysotile asbestos milling processes with an interval of one year. The 'Membrane Filter Method' adopted in the former study for fiber concentration estimation has been followed in the present study, to estimate the environmental samples under the same experimental conditions. Though very high fiber levels compared to the prescribed permissible limit in India, have also been obtained in this study, a general reduction trend in the values of mills common to the both studies are observed. A further suitable control measure has been recommended in the paper to reduce fiber levels in the milling processes.