AI-driven LoRaWAN Fertilizer Pollution Detector w/ WhatsApp

1 SenseCAP A1101 - LoRaWAN Vision AI Sensor
1 SenseCAP M2 Data-Only LoRaWAN Indoor Gateway
1 Arduino Nano
1 LattePanda 3 Delta 864
1 Elecrow 8.8″ (1920*480) IPS Screen
1 SH1106 OLED Display (128x64)
1 Anycubic Kobra 2
1 5mm Common Anode RGB LED
3 Button (6x6x12mm)
2 Half-sized Breadboard
1 Jumper Wires

To achieve a successful harvest season with prolific plants, farmers utilized a type of fertilizer to increase soil fertility, dating back to the earliest attempts to provide enough food in order to sustain larger populations. Until the industrial revolution, farmers mostly applied organic fertilizers and materials to supply adequate nutrients for plants, including naturally available mineral sources, manure, crop residues, etc. However, due to the evergrowing human population and declining fertile lands, agriculturalists started to utilize organic fertilizers in conjunction with chemical fertilizers to improve crop yield, even to the extent of causing soil contamination.


Chemical fertilizers are synthesized industrially out of estimated proportions of elements like nitrogen, phosphorus, and potassium[1], which provide necessary nutrients for plants to flourish vigorously. Due to intensive cultivation and the insufficient replenishment of nutrients, fertilizers mitigate precipitously declining soil fertility. In combination with organic fertilizers, chemical fertilizers can even revitalize arable lands. Although chemical fertilizers are indispensable to sustain soil fertility and avoid food shortages considering the current human population, they can also be noxious without painstaking attention to soil test reports. Since chemical fertilizers directly affect soil integrity and permeate through water bodies, they can contaminate the groundwater and the environment while contributing a remarkable gain in crop production. Also, chemical fertilizers infiltrate the soil and make plants vulnerable to various pathogens by hampering their roots[1].


When chemical fertilizers disperse throughout water bodies, they increase macronutrients in the environment, such as nitrogen, potassium, and phosphorus, resulting in contamination and eutrophication (nutrient pollution). These macronutrients can cause serious health problems due to overexposure. For instance, nitrogen can remain in water bodies for several years and cause nitrite (and nitrate) to accumulate exponentially. As a result of the high nitrite accumulation, nitrite-contaminated water can cause a blood disorder called methemoglobinemia (MetHb), also known as Blue Baby Syndrome. Furthermore, chemical reactions between nitrites heavily used in synthetic fertilizers instigate DNA damage, lipid peroxidation, and oxidative stress, which can all result in increased cellular degeneration. As a major health issue caused by the excessive use of chemical (synthetic) fertilizers, cellular degeneration can increase the risk of developing cancer. Forebodingly, a 2009 study by researchers at Rhode Island Hospital has found a substantial link between increased levels of nitrates in our environment and food with increased deaths from diseases, including Alzheimer's, diabetes mellitus, and Parkinson's[2].


According to earlier estimations, fertilizers provided approximately 70% of plant nutrients in 2020 at a global level[3]. Therefore, at this point, we cannot obviate the need for organic and chemical fertilizers to achieve sustainable crop production. Nevertheless, applying organic fertilizers in conjunction with chemical fertilizers can engender unexpected results and exacerbates the detrimental effects of chemical (synthetic) fertilizers. Since organic fertilizers behave differently depending on their manufacturing conditions, they change the degree of soil permeability of different soil types, such as loamy, peaty, silty, chalky, etc., not only unpredictably but also structurally. Hence, applying chemical fertilizers to the soil structurally altered by organic fertilizers may intensify the mentioned hazardous effects and lead to serious health conditions.


After scrutinizing the recent research papers on the effects of chemical and organic fertilizers, I noticed there are nearly no appliances focusing on detecting the excessive use of chemical fertilizers in the presence of organic fertilizers and providing real-time detection results for further inspection. Therefore, I decided to build a budget-friendly and easy-to-use proof-of-concept device to detect chemical fertilizer contamination levels with object recognition and inform the user of the model detection results simultaneously in the hope of averting the detrimental effects of fertilizer overuse by prewarning farmers.


To detect chemical fertilizer contamination levels accurately in relation to organic fertilizers, I needed to collect data from a controlled environment manifesting different soil conditions so as to train my object detection model with notable validity. Since utilizing manure as organic fertilizer affects soil acidification, integrity, and structure depending on the manure decomposition stages (fresh, active, mature, and old), I decided to produce my organic fertilizers by composting manure. Fortunately, I am raising quails on my balcony and have experience in utilizing quail manure as organic fertilizer. To change the soil integrity and structure in relation to the applied organic fertilizer, I collected quail manure in different decomposition stages:


Fresh (1 month) 


Active (3 months) 


Old (6 months) 


After producing organic fertilizers in different decomposition stages, I applied them to the soil in three separate flowerpots. Then, I added chemical fertilizers to each flowerpot in the same amount to examine the excessive use of chemical fertilizers depending on the soil integrity and structure. To demonstrate the fertilizer contamination effects on the environment, I sowed different types of tomato seedlings in each flowerpot.


Calcium Nitrate


Magnesium Sulphate 


Ammonium Sulphate 


Ammonium Phosphate 


Since Wi-Fi and Bluetooth transmissions may not be suitable options for a device operating in farms, I decided to utilize a SenseCAP A1101 vision AI sensor manufactured by Seeed Studio. SenseCAP A1101 provides a 400Mhz DSP Himax camera for image recognition and a Wio-E5 LoRaWAN module for LoRaWAN long-range transmission. Also, it is compatible with different types of LoRaWAN® gateways and networks, such as the Helium LongFi Network. As shown in the following steps, I explained how to activate a SenseCAP M2 data-only LoRaWAN indoor gateway (EU868) and connect SenseCAP A1101 to the Helium LongFi Network through the SenseCAP M2 data-only gateway. SenseCAP gateways are only required if the Helium network does not cover your surroundings. Since SenseCAP A1101 supports uploading TinyML object detection models as firmware, I was able to run my model without a single line of code. Nevertheless, SenseCAP A1101 does not give you the option to capture images with different labels out of the box. Therefore, I connected three control buttons and an SH1106 OLED screen to Arduino Nano in order to build a simple remote control. Then, I employed LattePanda 3 Delta to program SenseCAP A1101 to capture images according to labels transferred by the remote control via serial communication.


After completing my data set by taking pictures of fertilizer-exerted soils from my three separate flowerpots, I built my object detection model with Edge Impulse to detect chemical fertilizer contamination levels. I utilized Edge Impulse FOMO (Faster Objects, More Objects) algorithm to train my model, which is a novel machine learning algorithm that brings object detection to highly constrained devices. Since Edge Impulse is nearly compatible with all microcontrollers and development boards, I have not encountered any issues while uploading and running my model on SenseCAP A1101.


As labels, I utilized fertilizer contamination levels based on the soil integrity and structure altered by the applied organic fertilizer (manure) decomposition stage:








After training and testing my object detection (FOMO) model, I deployed and uploaded the model on SenseCAP A1101 as its compatible firmware (UF2). Therefore, the device is capable of detecting fertilizer contamination levels by running the model independently without any additional procedures or latency.


Since I focused on building a full-fledged AIoT appliance detecting fertilizer contamination levels despite utilizing the LoRaWAN network as the primary transmission method, I decided to develop a Python application from scratch informing the user of the recent model detection results via WhatsApp. Plausibly, all SenseCAP AI devices are capable of logging information to the SenseCAP Portal via the LoRaWAN network. Also, Seeed Studio provides the SenseCAP HTTP API to obtain registered data records from the SenseCAP Portal via HTTP GET requests. Therefore, firstly, I utilized the application to get the recent model detection results from the given SenseCAP Portal account.


Then, this complementing application employs Twilio's WhatsApp API to send the latest model detection results to the verified phone number, which SenseCAP A1101 registered to the SenseCAP Portal via the Helium LongFi Network.


Since I decided to capture images with SenseCAP A1101 and run my Python application on LattePanda 3 Delta, I wanted to build a mobile and compact apparatus to access LattePanda 3 Delta in the field without requiring an additional procedure. To improve the user experience, I utilized a high-quality 8.8" IPS monitor from Elecrow. As explained in the following steps, I designed a two-part case (3D printable) in which I placed the Elecrow IPS monitor.


Lastly, to make the device as robust and sturdy as possible while operating outdoors, I designed a plant-themed case providing screw holes to attach the SenseCAP A1101 bracket, a sliding front cover, and a separate section for the remote control compatible with a diagonal top cover with snap-fit joints (3D printable).


So, this is my project in a nutshell 😃


Click here to inspect code files, STL files, the object detection model, and instructions.


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