Agriculture, as of today is highly dependent on weather conditions and the framer`s experience in taking decisions. Often, the level of knowledge or insight is insufficient as it is not possible to monitor crops in real time. This leads to decreased yield, excess fertiliser or water disposal, insecurity against weather conditions, insecurity against weed or pest attacks, lack of knowledge of market conditions...the list is endless. This is a huge problem as at the rate the population is increasing very soon many countries are going to run into food and water shortages. Also, a decreased yield inhibits the purchasing power in the rural sector which affects the economic growth of the nation. In a country like India, where more than 50% of the workforce comprises of farmers, it is very essential to empower them so that they contribute to economic growth and are able to meet surging demands. Further, to control weeds and pests there is a prejudicial and unmindful usage of chemical fertilisers which not only deteriorate quality of the soil but also pose risks to public health. There is no way for the consumer to know whether the food he buys is safe from these fertilisers or not. There exists no single solution to all these problems.
To help monitor crops, soil quality, weather, fertiliser use, water content and market conditions I plan to deploy a mesh network of nodes at the edges(the farmland) which would connect to AWS IoT cloud through a central monitoring station placed on the farm. The monitoring station would receive soil quality data including mineral and water content from the mesh of nodes, deployed on the farm atop poles at strategic locations, which would communicate amongst each other and with the station via BLE. The monitoring station would have a weather station to predict disturbances beforehand.
Once, the data from each node is received it is processed extensively using Machine learning techniques on AWS IoT Greengrass and only those nodes where abnormalities are found are noted along with the type of error present and published to the AWS cloud in a coded format, when the need arises. It must be noted that minor issues like water or mineral deficit would immediately be resolved by opening valves at the node where the error is present. This way, the irrigation is completely autonomous.
The central station essentially is a quadcopter sitting atop a charging pad. Once, in every two or three days, it flies over the farm and captures images of crops of only those regions where mineral stress or any unnatural growth is expected and after analysis flies over to the farmer along with the data collected over the course of the past few days wirelessly over BLE or WiFi. The imagery data and the analysed crop health parameters can be received by the farmer on his smartphone or desktop through an app that is based AWS Iot ThingsGraph and IoT core.
This data can then be analysed for taking important decisions like harvesting or tweaking the irrigation plan. The data is also automatically uploaded to the AWS DynamoDB for sharing with health-conscious consumers or for agricultural and meteorological research.
On the software side would be a web service written on NodeRed that would fetch data published onto the AWS cloud from the central station and send notifications to the farmer alongwith geotagged abnormal behaviour. The farmer would be able to override autonomous regulations by the central station like change the irrigation plan or the imagery data acquisition.
The webservice would also fetch agri-market data from online feeds and inform the farmer about selling prospects. The farmer would have the option to publish collected soil quality data to the web service which consumers may subscribe to, to ensure that what they get on the table is safe to consume.
The nodes would be based on the NRF52832 module sitting atop a wooden pole on the farm. Mounted on...