The fisherman’s crossing the borders and identification of locations in the sea is becoming a difficult task with existing equipment’s provide to the fisherman’s as a result they cross the borders. In our day-to-day life we hear about many Tamil Fishermen being caughtand put under Sri Lankan Naval custody. The sea border between thecountries is not easily identifiable, which is the main reason for this offence. Moreover, in cases of imminent natural disasters, failure or delayin notifying concerned personnel to evacuate results in loss of life on alarge scale. In this paper we have proposed a method which protects thefishermen by logging their entries and exits in the harbour using embeddedsystem, notifying the country’s sea border to them by using Global Positioning System (GPS), GeoFencing and Mobile Systems. We use GPSas a method to track the current location of the fishermen. The GPS’current latitude and longitude coordinates are sent to the database wherethe administrator utilizes it for continuous tracking and monitoring of theuser, if in distress using their credentials and last known location, currentlocation predictions can be made. Another benefit being the logging procedures help official authoritative agencies to identify fishermen and their activities for their own safety and security.
Agriculture is the backbone of human civilization since it is a requirement of
every living entity. Paddy agriculture is extremely important to humans, particularly in
Asia. Farmers are currently facing a deficit in agricultural yield owing to a variety of
factors, one of which is illness. The composition of paddy crop diseases is complicated,
and their presentation in various species is highly similar, making classification
challenging. These agricultural infections must be discovered and diagnosed as soon as
feasible to avoid disease transmission. The disease significantly impacts crop
productivity, and early detection of paddy infections is critical to avoiding these
consequences. These issues arise as a result of a lack of awareness regarding health.
Identifying the disease needs the best expertise or previous knowledge to regulate it.
This is both difficult and costly. To address the aforementioned problem, a Deep
Learning (DL) model was created utilizing a Convolutional Neural Network (CNN)
and the transfer learning approach. The model is trained using an image of a paddy
crop as input. By comparing metrics like accuracy and loss, the optimum technique is
identified.
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