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Objectives: To make a quantitative study of Indigenous Technical Knowledge (ITK) practices pertinent to crops grown in Kolli hills tribes in the Namakkal district of Tamil Nadu, India. Methods: The attempt was made to study the adoption level of the interpreted ITKs among Kolli hills tribes. The Kolli hill was picked purposively for its uniqueness in continuing their traditions age old practices in farming practices by the tribes. The present study has been carried out in the Namakkal district of Tamil Nadu state. The Namakkal district was administratively divided into seven taluks and fifteen blocks. Among these seven taluks and fifteen blocks, higher populations of tribes were seen at Kolli hills. For the selection of the respondents based on the proportionate random sampling technique and the data were collected from each respondent through personal interview method. The study was carried during 2019 with 150 respondents in highly populated villages of Kolli hills. The Adoption index was used to analyse the adoption level of the ITK’s by the tribes. Findings: More than half of the respondents (55.56 per cent) had a medium level of knowledge followed by high (25.10 per cent) and low (19.34 per cent) levels of knowledge on indigenous cultivation practices. Conclusion: Indigenous technical knowledge has a substantial heritage in agrarian civilization. The findings highlight the effectiveness of indigenous technical knowledge above its modern equivalent. Thus, extension workers should identify and incorporate them in the technology transfer action in order to ensure long-term or sustainable agricultural development.

Internet of Things (IoTs) is the emerging Technology as the third wave in the development of the internet. IOT is accepted to have massive impact sensors, surveillance devices; wearable's and body area networks with advanced interface technique. This leads to the need for a hardware-software co-design approach. We present a highly parameterized hardware design allowing configurability and the ability to evaluate different design choices in a short amount of time. This paper aims to analyze the use of Deep learning algorithm for building such reconfigurable low power devices. We develop the hardware architecture for such a system and provide experimental results obtained for the case study of Posture detection for body worn cameras used for law enforcement. Our proposed system, implemented on a Raspberry picamera platform uses minimal power consumption with an accuracy of more than 90% for the selected case study of Posture Detection. 