In a recent engagement, Strong Analytics had the opportunity to partner with a leading firm in the agricultural sector. Our partner company is at the forefront of agricultural innovation, focusing on the development of autonomous robots to mechanize the process of weeding fields. The objective of these robots is to mitigate the need for expensive manual labor and the use of potentially harmful pesticides, which can negatively impact the environment and human health.
The Problem
In our collaborative efforts with our partner, we aimed to enhance their crop object detection model, a critical component of their robotic system. The vision system in the robots is designed to distinguish between crops and weeds accurately. The mechanical component of this robot is programmed to selectively eliminate weeds while leaving the desired crops undisturbed, thereby ensuring efficiency and successful harvest with optimal crop yield.
Our partner’s initial model faced challenges in meeting requirements on low powered edge hardware, and the team at Strong was tasked with the responsibility of improving the overall accuracy and speed of the existing model.
Our Solution
The fundamental challenge at the beginning of this project was to balance the traditional trade-off between speed and accuracy. This required us to go beyond conventional methods to leverage advanced techniques and approaches in machine learning to retain accuracy with a substantially smaller model meant to run on edge.
By integrating several state-of-the-art approaches, we succeeded in developing a more efficient object detection model that significantly outperformed the original model in terms of speed and maintained (and exceeded) the original accuracy. With the new model, we managed to process images faster than real-time and exceed the original accuracy, which was a substantial improvement over the previous model.
Performance Impact
Our proposed model has resulted in substantial improvements in performance and speed and has been integrated into the partner’s robotic platform. Since the launch of our initial model, we’ve broadened the scope of work to encompass a variety of additional impactful research areas, including the following:
Multi-crop detection models. Transitioning from a single-crop detection approach to a multi-crop detection methodology presented several challenges, but ultimately unlocked significant advantages. This shift greatly simplified the workflow for end users as well as the deployment process. Now, a single model can be employed across different fields, irrespective of the crop type, enabling indiscriminate weeding. This means that robots can be deployed to weed any field at any time, significantly simplifying operations.
Unannotated image search. As part of our goal to improve model performance in an efficient manner, we introduced an unannotated image search feature. This feature enables automatic exploration of extensive unannotated image datasets to identify complex crop and weed combinations.
Generative image training. Adapting models to variable field conditions, such as different camera settings, lighting conditions, and soil characteristics, was a major challenge for our partner and for agricultural applications of ML in general. To address these challenges, we turned to the power of generative AI and implemented a solution utilizing a state-of-the-art algorithm in image translation. The ability to simulate these varying conditions in a controlled manner, without the need to collect new data or perform additional costly annotations has been critical to the model’s adaptability.
Conclusion
Through partnering with this leading agricultural company, we were able to enhance their crop object detection model, which is an important component of their robotic system. Through the integration of advanced machine learning techniques, we were able to develop a more efficient model that significantly outperformed the original model in terms of speed and accuracy. The proposed model has resulted in substantial improvements in performance and speed and has been integrated into the partner’s robotic platform.
Overall, this work showcases the power of machine learning in addressing real-world challenges and improving efficiency and sustainability in agriculture. Interested in learning more about how artificial intelligence and machine learning can help you streamline operations and push the boundaries of innovation in your own business? Reach out to our team of PhD-trained researchers and data scientists to learn how strategically investing in technology development can help you take your business to the next level.
