Environment monitoring device for prediction of climate.

Climate change can disrupt food availability, reduce access to food, and affect food quality. This happen due to the increases in temperatures, changes in precipitation patterns, changes in extreme weather events, and reductions in water availability may all result in reduced agricultural productivity. A number of countries already face semi-arid conditions that make agriculture challenging, and climate change will be likely to reduce the length of growing season as well as force large regions of marginal agriculture out of production. Studies reveals that projected reductions in yield in some countries could be as much as 50% by 2020, and crop net revenues could fall by as much as 90% by 2100, with small-scale farmers being the most affected. Agriculture is extremely vulnerable to climate change. Higher temperatures eventually reduce yields of desirable crops while encouraging weed and pest proliferation. The four most important factors that influence crop yield are soil fertility, availability of water, climate, and diseases or pests. These factors can pose a significant risk to farms when they are not monitored and managed correctly.

I would like to design a smart monitoring device on climate change for agriculture because it is very important to predict the conditions earlier and suggest the farmers about the upcoming risks so that they can plan accordingly to eradicate the issues. The hardware kit QuickFeather has salient features, the system is having on-board mCube MC3635 accelerometer, Infineon DPS310 pressure sensor, Infineon IM69D130 PDM digital microphone sensors which are suitable for the current project. GPIO pins to include additional sensors with SPI/I2C. SensiML AI Software Bundle has SensiML Data Capture Lab (DCL) is used to capture data and transfer/store to the remote devices. In the analytic studio the data can be trained for AI processing. The hardware and software drawn my attention to focus on the present projects with ease of working and creating a useful AI predication to help farmers. The existing devices are not best like this, it take time to search for the suitable software for the selected hardware and their may be some technical issues in configuring them. QuickFeather can be used to create a smart, low-power design with EOS S3 MCU + FPGA SoC and analytics can be performed on the SensiML AI tool kit.I will be good opportunity for me to design the device and study about the climate changes and give suitable suggestion for the farmers to avoid risk in their crop planing.

The monitoring and predicting devices are necessary for the farmer to alert them for the upcoming risk in their agriculture in prior of facing them. The hardware and software bundle drawn my attention to design "Environment monitoring device for prediction of climate changes in agriculture". mCube MC3635 accelerometer is used to monitor the wind gusts and based on the movement we can train the device to alert. Sometime wind blow is good but in some cases it is curse. Infineon DPS310 pressure sensor consists of pressure and temperature sensor to monitor the environment conditions in agriculture based on the data, necessary steps like water motor switch ON/OFF can be done. Infineon IM69D130 PDM digital microphone sensors used to identify the animal and birds chirping. So that strobe lights in night are blinked to avoid grazing animals on crop. SensiML Data Capture Lab (DCL) the data is captured in remote location for AI processing and to detect the abnormal characteristics in air pressure, heat waves, wind gusts, etc.