The Project

The customer aimed to develop specialized AI-driven software for autonomous farming robots to automate essential plant cultivation and care processes. While these robots were highly efficient in navigating farm beds and fields, they initially lacked the ability to distinguish between crops and weeds, a critical function for precise fertilization and watering.

Data Collection & Processing

To address this challenge, our partner’s team implemented a comprehensive data acquisition strategy by equipping agricultural robots with video cameras to capture images of plants and weeds across the fields. Agricultural specialists then labeled this data to enable object detection and segmentation for further refinement.

Custom Neural Network Integration

They developed a custom neural network designed to classify plant species and categories based on visual data. This allowed the robots to make smart, data-driven decisions about plant care. The neural network was integrated into GPU-powered machines, ensuring real-time processing and accurate differentiation between plants and weeds. Additionally, a stem detection module was implemented to enhance laser-guided precision for plant-specific treatments.

Autonomous Decision-Making & Continuous Learning

The AI-powered software enables fully autonomous decision-making, allowing robots to operate in the field without an internet connection. This ensures uninterrupted performance in remote agricultural environments. When the robots return to their stations and reconnect to the network, the software updates the dataset with new insights, refining operational parameters for enhanced efficiency.

This cutting-edge AI solution empowers farmers to increase efficiency, optimize resource usage, and enhance precision in plant care, marking a significant advancement in autonomous farming technology.

Technologies

Python, PyTorch, OpenCV, TensorFlow, AWS (S3, Lambda, EC2, CloudWatch)

Team

2 ML specialists