Disrupticulture

Inspiration

Global warming and climate change have become a harsh reality, haunting every aspect of human life, including various industries. As a primary victim and contributor to global warming, agriculture must be more productive and sustainable to meet the needs of a growing population. Market fluctuations can impact the profitability and viability of farming operations, leading to reduced investment and innovation. These economic pressures and challenges faced by farmers are leading to a growing number of farmer suicides. Hence, we strive to address this challenge by developing a mechanism that supports farmers and promotes sustainable agriculture while balancing ecological and economic factors.

Vision

To ensure that our farmers thrive through equitable compensation and have enhanced financial access while reducing the carbon footprint and ensuring food security. Thus, work towards a brighter future where farmers are empowered, our planet is protected, and our communities thrive.

Financial Aspect

The potential market size of AgriTech technology is significant, as the agriculture industry is a vital part of the global economy. According to a report by Grand View Research, the global market size of AgriTech was valued at $13.5 billion in 2020 and is expected to grow at a compound annual growth rate (CAGR) of 12.2% from 2021 to 2028. This growth is driven by factors such as increasing demand for food due to population growth, the need for sustainable agricultural practices, and advancements in technologies like artificial intelligence, the Internet of Things (IoT), and robotics. With the increasing adoption of technology in agriculture, the potential market size of AgriTech is expected to continue to grow in the coming years.

Design and Construction

For our hardware component, we chose the DJI Tello RoboMaster TT programmable drone, primarily due to the wide availability of documentation for programming the drone. The drone is managed using a backend server based on the sensor data collected from the ground. We are also using an Arduino controller on the ground, which is connected to all the sensors and transfers the data to the cloud through Wi-Fi. Arduino was chosen as our IoT controller as it is easily customizable to suit our needs.

To facilitate smart farming, the drone will be used for monitoring, aerial sensing, tracking, data collection, communication, and security. The drone will be equipped with diverse sensors to gather information from the monitoring area and transfer it to different edge computing nodes. These nodes will carry out a preliminary analysis of the data and forward the outcomes to a cloud server. The server will accumulate and summarize the results to anticipate the overall health of crops and approximate the total yield of the harvest. Additionally, the cloud will provide a platform for hosting a blockchain database that tracks the farmers' non-fungible tokens (NFTs) and related transactions.

Challenges Faced

The primary challenge we encountered while using a LIDAR (Light Detection and Ranging) sensor to generate a point cloud was data processing. LIDAR sensors generate a large amount of data that requires advanced computing resources to process efficiently. The LIDAR sensor provided us with point distance measurements that could not be directly used to create a point cloud for 3-D mapping. Our task was to create an algorithm capable of translating the distance measurements obtained from the LIDAR sensor into a practical 3D point cloud. However, the absence of a standardized procedure for constructing point clouds from LIDAR readings posed a significant challenge.

How we overcame these challenges To overcome this challenge, we conducted a literature review on available research papers and algorithms related to the generation of point clouds from LIDAR readings. After extensive research on this topic, we combined it with our own efforts to devise an algorithm capable of generating 3D point clouds in real time using LIDAR readings. Despite all the technical complexities involved, we successfully incorporated LIDAR data into our solution by developing an algorithm that can overcome this challenge of LIDAR data.

Accomplishments

We successfully incorporated the Verbwire APIs into our system, which enabled us to generate NFTs with relative ease. In addition, we successfully collected real-time data from the drone, which has applications in sustainable agriculture.

Future for Disrupticulture

To broaden our reach, we must first identify our target audience, and then create our product marketplace. There are around 2.5M farmers in the USA right now who are our potential audience. In future, we hope to provide customizable solutions tailored to our user's unique needs and preferences, such as crop types, water usage, and soil types, by establishing an open communication channel with them.