AgriSuccess

AgriSuccess

Is an Agricultural information sharing platform with a device that makes soil analysis to recommend a particular crop that will thrive.

Android, Artificial Intelligence, Internet of Things

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Description

Agrisuccess is a hardware(running on Intel genuino 101), web-based, and mobile system designed to help farmers perform real time soil fertility analysis, which generates reports on: soil pH, soil Nitrogen, Phosphorus, Potassium (NPK) contents, soil temperature and moisture. The purpose of which is to identify pre or post-harvest nutrients in the soil by using Agrisuccess’ hardware, which connects to a phone via Bluetooth or Wifi, and prepares for the farmer a list of crops that can be produced successfully, given their current soil conditions. Before planting, if other crops are desired, Agrisuccess can report to the farmer what additional nutrients need to be added to the ground to ensure new crop success. The system also assists them in making the right decisions on which crops can thrive better on their soils and hence, in turn, increase their incomes. The beauty with the soil analysis device is that the soil analysis is done in the field rather than collecting soil samples to labs as it is done in the current manual systems and this eliminates a lot of errors. And also results are sent to the user in real time automatically wherever they are. Agrisuccess helps farmers connect with near buy buyers of crop produce. It also provides the farmer with weather forecasts. The system eliminates tedious paper work by helping the farmer keep records on irrigation, pest control and current fertilizer status. Agrisuccess allows farmers and farming professionals the opportunity to discuss their unique issues: possibly recommend and connect the farmer to financial institutions for loans, with the supporting documentation needed about their farm which will assist the farmer in obtaining both a loan and quality crop yields. The ultimate goal is to increase agricultural sector contribution to the national GDP.

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Rima M. updated status

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Rima Modak

I am currently working to develop a prototype of an device based on IoT. This device will be used to detect any kind of material. It will be used to detect both living and non-living things, and after detecting, it will display varies other things related to the detected object according to the tags provided by the user/customer.

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Aravindhan N. created project Automatic attendance management system using face detection

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Automatic attendance management system using face detection

Automatic attendance management system will replace the manual method, which takes a lot of time and is difficult to maintain.There are many bio metric processes ,in that face recognition is the best method. In our campus staff attendance is taken with the help of Gesture recognition /attendance sheet .We can take this to next level by implementing Artificial Intelligence based Face Recognition using Convolution Neural Network(CNN). We have to train our neural net using COCO (large Image dataset designed for object detection) and Staff Dataset (Several images of individual staffs). Since we don't have the photos of the staffs,we have trained our neural net using our own photos.Our Neural net consists of 20 neurons in the hidden layer which help us to diagnose the pixels of the image and compares the result with the trained dataset .By using our advanced system the staffs can use their own mobile/laptop [camera] for registering their presence in their own place which is possible only if they are connected to our college Network (WiFi).

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