A Robust Illumination-Invariant Camera System for Agricultural Applications
This work provides a pragmatic solution for agricultural computer vision applications by reducing the data requirements for training deep learning models, which is a significant problem for researchers and practitioners in the field.
This paper addresses the challenge of inconsistent image quality in agricultural computer vision due to changing outdoor lighting conditions. They developed an active lighting-based camera system that generates consistent images, enabling deep neural networks for object detection to achieve similar accuracy with nearly four times less training data.
Object detection and semantic segmentation are two of the most widely adopted deep learning algorithms in agricultural applications. One of the major sources of variability in image quality acquired in the outdoors for such tasks is changing lighting condition that can alter the appearance of the objects or the contents of the entire image. While transfer learning and data augmentation to some extent reduce the need for large amount of data to train deep neural networks, the large variety of cultivars and the lack of shared datasets in agriculture makes wide-scale field deployments difficult. In this paper, we present a high throughput robust active lighting-based camera system that generates consistent images in all lighting conditions. We detail experiments that show the consistency in images quality leading to relatively fewer images to train deep neural networks for the task of object detection. We further present results from field experiment under extreme lighting conditions where images without active lighting significantly lack to provide consistent results. The experimental results show that on average, deep nets for object detection trained on consistent data required nearly four times less data to achieve similar level of accuracy. This proposed work could potentially provide pragmatic solutions to computer vision needs in agriculture.