Farming accounts for 70% of the world’s water consumption, with many over-watering their crops, while one-fifth of the world's population lacks safe drinking water. This issue primarily stems from a lack of understanding of what individual plants require in terms of water, fertilizer, sunlight and pesticides.

berrySmart is a device that gives farmers a way to better understand their farm. A variety of sensors are strategically placed across the farm in a mesh network, which we use to create a dashboard for farmers. With overwatering and overfertilization, berrySmart allows farmers to be more environmentally friendly while also better caring for their crops.

Our target market is small-scale family or community owned farms. We focus on a variety of berry crops as they are one of the largest agricultural exports for the Northeastern area. The berrySmart device fulfills the need for agricultural efficiency and data analysis for middle-income, low technology farms.

Berry farming has a variety environmental requirements. Blueberries require acidic soil (pH range from 4.8 to 5.2) that must be monitored. Berries are high water and high sunlight crops, requiring an abundant supply of high-quality water delivered at the right rate, time and location. Strawberries are particularly sensitive to overcrowding of soil beds and demand consistent pruning and weeding. The use of extensive data collection and analysis in the berrySmart device will enable farmers to better understand and grow their crops.

The berrySmart system consists of subsystems including sensor nodes, an edge node, database, dashboard and the farmer's personal computer, which is used to view the dashboard. Nodes are connected through a mesh network.

We designed a mesh network overlayed with WiFi via an Espressif ESP32 chip coded from the Arduino IDE. We will make our hardware and mesh network software implementation open-source, to allow farmers to cheaply build their own sensors - this will also allow anyone to make contributions and changes to their personal implementation. In order to achieve the mesh network overlayed with WiFi, we instantiate WiFi signals which connect between adjacent nodes. These WiFi connections are instantiated every time the device wakes up and is ready to receive data, and it is deinitialized once the device has received the data packet. We store the data in a character array, where subsequent data packets from different ESP devices are separated in the character array by a hashtag (#).

Sensor nodes are made up of an ESP, battery and sensors that measure soil moisture, temperature, humidity and ambient light. They are placed throughout the farm and collect sensor values. Each sensor node is not connected to the Internet directly; rather, it communicates with other sensor nodes in a mesh network. An edge node communicates all information to the central server and database. Sensor nodes collect information once every two hours, and otherwise put in deep sleep to conserve power. The sensor nodes are designed to last long periods of times without being connected to a power source.

As mentioned, we will make berrySmart’s hardware and mesh networks components open source. By providing links to the necessary parts, farmers only need to pay roughly $30 per sensor node (when ordering in bulk, this figure could be even lower). Of course, if farmers do not require certain sensors such as humidity or temperature, the cost would also decrease. By having our system open source, we greatly increase flexibility.

These sensor nodes communicate with our server, which consists of a database and dashboard. Here, we visually present sensor information overlaid on a map, provide recommendations based off sensor values (such as watering certain crops more as a result of the sensor information, weather patterns and crop type) and alerts to farmers. We also send daily emails that update the farmer on the status of the farm.

We believe we are the only platform that utilizes a sensor mesh network to alert and make recommendations to farmers based off of resource allocations for crops. The main novelty of our project lies firstly with applying sensor mesh networks for this application; secondly, our analytics dashboard innovatively and intuitively incorporates sensor information to make suggestions to the farmer.

In the future, we plan to utilize drone aerial imagery as another form of sensor information, further test our design with farmers to get more feedback and further improve on our analytics and recommendations through more sophisticated algorithms.

Farming is universal, and with so many resources being devoted to cultivating crops, berrySmart aims to make this process as efficient as possible. By collecting sensor information in a mesh network, farmers can cheaply understand the status of their farm. By displaying this information visually and providing specific recommendations to ameliorate any issues, berrySmart intuitively allows farmers to conserve water, use less pesticides and do their part in saving our environment.

Code can be found here:
Our website/dashboard can be found here:


We all first met by taking a class, 6.08 Introduction to EECS via Interconnected Embedded Systems, but really connected over our shared interest in helping the environment. All coming from technical backgrounds, from Mechanical Engineering to Computer Science to Machine Learning, we all decided to harness what we had learned to create an IOT project that could really have an impact on our Earth, even if we were just college students. As a result, we formed a team and started ideating, eventually landing on the idea of improving resource allocation in farms through a sensor mesh network. After prototyping and demoing our idea, we were recommended to apply to the IoT Innovation Challenge, which seems to be an amazing opportunity to receive feedback and showcase our work! Ultimately, we are inspired by the power that we have just as students - coming into this project, we did not expect or even believe we would develop a platform that farmers would find useful. However, as we iterated, failed and explored, we were able to develop a tool that we are really proud of, and in the process, learned so much about interconnected systems. We would like to especially thank Joe Steinmeyer, who has provided invaluable mentorship, from connecting us to experts and giving advice when we were stuck. Thank you Joe!


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