CROPCOPTER

About CropCopter

Countries strive for UN Sustainable Development Goals to combat global starvation and epidemics. Our 2030 vision prioritizes conservation and export growth. Our project utilizes robots and drones for disease detection, fire prevention, land assessment, and resource optimization. These technologies integrate into a website for product exchange and productivity awareness. With our drone-based solution, we revolutionize agriculture, improving land monitoring, product quality, and irrigation. Leveraging advanced tech like LI-Fi and AI, we drive agricultural innovation. Our software platforms educate and support agriculture related groups.

Web Application Aims

More important than modern agricultural techniques is the awareness of their proper usage. Farmers should not only focus on crop production but also on effective selling and marketing strategies to maximize profits. An integrated environment should be created to facilitate buying and selling, with reliable delivery services for online transactions. To ensure secure and private financial transactions, a virtual currency has been introduced on the website, encouraging investments. The website offers various account types: trader, delivery, and user. Traders have a control panel to manage their products, while users can browse and interact with the available items. The delivery account handles product transportation Between farmers, traders, and users. Users can earn points by uploading highly-rated articles, which can be used on the website or converted into real currency. The website also features a section of our main product which is the drone. In the future, a section detailing agricultural land specifics, including strengths and weaknesses, will be added.

Embedded Systems Usage

Farmers face challenges that demand new technologies and techniques for increased productivity. CropCopter is a remote-controlled drone with machine learning capabilities, ntegrated with web and mobile apps. CropCopter's hardware captures farm segment images, enabling land monitoring. It offers two control options: a long-range remote controller and a limited-range mobile app. The drone scans diverse green lands, using specific points or GPS to determine land shape. It provides live streaming and collects weather data (temperature, humidity, smoke, raindrops) for fire detection. Furthermore, CropCopter controls equipment like pumps. It consists of two units: one collects, processes, and transmits data, while the other receives data and operates equipment. The transmitting unit gathers temperature, humidity, and soil moisture data, processes it, and transmits control signals via light. The receiving unit operates actuators and collects data from the transmitting unit or drone. Both units use solar cells, light sources, and detectors for data transmission and reception.

Machine Learning Function

Plant disease detection combines EfficientNet B3 and YOLOv8 architectures. A dataset of plant images, with and without diseases contains 52 type of disease, is collected and divided for training, validation, and testing. Preprocessing ensures uniformity in size, brightness, and contrast. This combination effectively identifies plant diseases, enabling early intervention for farmers with model accuracy 97%. For fire detection, the model employs YOLOv8 trained on a large dataset of fire images. Bounding boxes are labeled for training, enabling real-time detection in videos or images. By recognizing flame and smoke characteristics, the model accurately locates fires. Using Google Earth with NDVI factor facilitates vegetation health monitoring and land use tracking. NDVI data identifies healthy and stressed vegetation. Farmers create disease management maps using this information. High NDVI values indicate healthy vegetation, aiding in monitoring crop growth and assessing forest health.

Li-Fi

Li-Fi based irrigation system mainly consists of two parts, they are transmitter part and receiver part. The sensors in the transmitting part collect the information of soil moisture and temperature of different crops in different period and then an algorithm was developed with the threshold values of temperature and soil moisture that was programmed into a micro-controller based gateway to control water quantity. This contrast describes a complete wireless networking system and uses bi-directional communication system. The input signal at the transmitter section can be modulated with a specific time period then send the data using LED bulbs in 0’s and 1’s forms. At the receiver end, a photo-diode is used to receive the LED flashes strengthens the signal and gives the output. The input of transmitter can be any kind of data like text, voice, etc. The speed of this system is very high. Irrigation system that consists of a distributed wireless network of soil moisture and temperature sensors deployed in plant root zones. Each sensor node involved a soil-moisture probe, a temperature probe, a micro-controller for data acquisition.

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About CropCopter

Web Application Aims

Embedded Systems Usage

Machine Learning Function

Li-Fi