Our client is a company producing autonomous agricultural robots to automate and accelerate farm work within the European region.
Detailed information about the client cannot be disclosed under the provisions of the NDA.
The utilization of ML farm systems and robots in the agricultural sector is becoming increasingly crucial due to the significant hurdles posed by manual plant care, which demands extensive human effort, time, and expenses. These advanced technologies can address various challenges, including labor scarcity and resource efficiency. This results in a more comprehensive and efficient solution to modern agriculture issues.
Our client produces autonomous robots and devices that are supposed to automate the process of cultivating and nurturing plants. Although the robots could move around the beds and fields, they lacked the ability to differentiate between plants and weeds for selective fertilization and watering purposes.
Our experts were faced with a significant challenge of integrating specialized software into the robots that could precisely distinguish and segregate thinned-out plants. The subsequent objective for the program was to eliminate specific weeds using lasers with optimal accuracy. Furthermore, ML farm systems needed to determine the type of plants and supply them with a sufficient amount of suitable fertilizer, depending on their class and condition metrics.
In summary, the scope of work included:
Our team conducted an initial meeting with the client to gather requirements and understand their specific needs for the autonomous robots. Based on these requirements, we created a comprehensive design plan for developing the software system, which consisted of two main stages: data collection and labeling using an integrated video camera and the implementation of a supervised machine learning model.
To manage the project effectively, we followed the Agile methodology and held daily meetings to track progress and discuss any issues or concerns. We also utilized communication tools like Google Chat and project management software like Jira and Confluence to assign tasks and monitor performance.
After a month and a half of development, we were able to create the MVP version of the neural network, which was capable of making effective decisions without additional control. This approach allowed us to develop a flexible and scalable system that could be adapted to different agricultural settings and use cases, providing farmers with a cost-effective and efficient solution for managing their operations.
The implementation of machine learning in agriculture through the use of agricultural robots equipped with computer vision and AI-based engines provides numerous benefits for the industry. It promotes cost-effectiveness by reducing the use of unnecessary fertilizers and chemicals and improving agricultural productivity through selective treatment of each plant. Moreover, it offers detailed field monitoring and mapping without human intervention, providing farmers with vital information on their fields’ condition.
The result of implementing this technology for the client is a reduction in overall resources used, leading to economic benefits through continuous automatic crop care, high yields, and perfect plant health. Additionally, laser-based, chemical-free weed elimination protects agricultural ecosystems, minimizing the negative environmental impact of traditional farming practices. The system’s ability to continuously learn and adapt allows farmers to update the data set regularly and adapt to new types of plants and agricultural work.
Overall, the integration of AI technology in agriculture has enormous potential to bring benefits to the industry, the environment, and nature. ML robots can increase crop quality and fertility, reduce costs, preserve natural resources, and eliminate potential harm to humans by completing complex tasks automatically.
Having received and processed your request, we will get back to you shortly to detail your project needs and sign an NDA to ensure the confidentiality of information.
After examining requirements, our analysts and developers devise a project proposal with the scope of works, team size, time, and cost estimates.
We arrange a meeting with you to discuss the offer and come to an agreement.
We sign a contract and start working on your project as quickly as possible.