Step 9: Developing a Python application to transfer the model detection results via WhatsApp.Step 8.1: Activating SenseCAP M2 data-only LoRaWAN gateway (EU868.Step 8: Transferring the detection results to the SenseCAP Portal via the Helium LongFi Network.Step 7: Connecting SenseCAP A1101 to SenseCAP Mate App and setting up the Edge Impulse FOMO model.

Step 6.3: Evaluating the model accuracy and deploying the model.

Step 6.2: Training the FOMO model on the fertilizer-exerted soil images.

Step 6.1: Uploading images (samples) to Edge Impulse and labeling objects.

Step 6: Building an object detection (FOMO) model with Edge Impulse.Step 5.1: Saving the captured images as samples on LattePanda 3 Delta.Step 5: Capturing fertilizer-exerted soil images w/ SenseCAP A1101 and communicating with Arduino Nano via serial communication.Step 5.0: Setting up SenseCAP A1101 on LattePanda 3 Delta.Step 4.1: Displaying images on the SH1106 OLED screen.Step 4: Utilizing Arduino Nano as a remote control to send commands via serial communication.Step 3.1: Adding chemical fertilizers and sowing tomato seedlings.Step 3: Producing organic fertilizers from quail manure in different decomposition stages.Step 2: Creating an account to utilize Twilio's WhatsApp API.Step 1.2: Creating a LattePanda Deck to display the video stream.Step 1.1: Assembling the case and making connections & adjustments.Step 1: Designing and printing a plant-themed case.

Arduino wires library exxplained