In this article, we share our experience in the scope of controlled-environment agriculture automation in the Antarctic station greenhouse facility called EDEN ISS. For remote plant monitoring, control, and maintenance, we solve the problem of plant classification. Due to the inherent communication limitations between Antarctica and Europe, we first propose the image compression mechanism for the data collection. We show that we can compress the images, on average, 7.2 times for efficient transmission over the weak channel. Moreover, we prove that decompressed images can be further used for computer vision applications. Upon decompressing images, we apply machine learning for the classification task. We achieve 92.6% accuracy on an 18-classes unbalanced dataset. The proposed approach is promising for a number of agriculture related applications, including the plant classification, identification of plant diseases, and deviation of plant phenology.

Usage of multispectral satellite imaging data opens vast possibilities for monitoring and quantitatively assessing properties or objects of interest on a global scale. Machine learning and computer vision (CV) approaches show themselves as promising tools for automatizing satellite image analysis. However, there are limitations in using CV for satellite data. Mainly, the crucial one is the amount of data available for model training. This paper presents a novel image augmentation approach called MixChannel that helps to address this limitation and improve the accuracy of solving segmentation and classification tasks with multispectral satellite images. The core idea is to utilize the fact that there is usually more than one image for each location in remote sensing tasks, and this extra data can be mixed to achieve the more robust performance of the trained models. The proposed approach substitutes some channels of the original training image with channels from other images of the exact location to mix auxiliary data. This augmentation technique preserves the spatial features of the original image and adds natural color variability with some probability. We also show an efficient algorithm to tune channel substitution probabilities. We report that the MixChannel image augmentation method provides a noticeable increase in performance of all the considered models in the studied forest types classification problem.

Deep convolutional neural networks are highly efficient for computer vision tasks using plenty of training data. However, there remains a problem of small training datasets. For addressing this problem the training pipeline which handles rare object types and an overall lack of training data to build well-performing models that provide stable predictions is required. This article reports on the comprehensive framework XtremeAugment which provides an easy, reliable, and scalable way to collect image datasets and to efficiently label and augment collected data. The presented framework consists of two augmentation techniques that can be used independently and complement each other when applied together. These are Hardware Dataset Augmentation (HDA) and Object-Based Augmentation (OBA). HDA allows the users to collect more data and spend less time on manual data labeling. OBA significantly increases the training data variability and remains the distribution of the augmented images being close to the original dataset. We assess the proposed approach for the apple spoil segmentation scenario. Our results demonstrate a substantial increase in the model accuracy reaching 0.91 F1-score and outperforming the baseline model for up to 0.62 F1-score for a few-shot learning case in the wild data. The highest benefit of applying XtremeAugment is achieved for the cases where we collect images in the controlled indoor environment, but have to use the model in the wild.

Computer vision methods have been recently integrated into industrial facilities performing the automatic household waste sorting. In this research, we report on an approach based on hyperspectral cameras and consecutive data analysis in recycling plants for improve waste sorting processes in terms of waste detection and classification. The majority of existing methods have been conducted on static conveyor belt, and their findings may not be applicable to recycling systems. The study focuses on developing a real-time hyperspectral object classification system for sorting machines, addressing the challenge of differentiating objects that are unidentifiable to RGB cameras. The linear hyperspectral camera operates within the wavelength range of 900 nm to 1800 nm. The research includes the creation of a dataset with physically separated training and test data, automated pseudo-labeling of hyperspectral data. This allows for rapid expansion to new classes without waste of manual annotation. The dataset contains 18 classes of objects, including various types of plastics, films, textiles, tetrapack, and cellulose products. To improve the accuracy and inference speed, we experiment with normalization, dimensionality reduction, and model calibration. Experimental results show a promising F1-score of 71% on a moving conveyor belt.

Precision agriculture is a research domain aimed at securing the food production for the growing Earth population and urbanization. In this paper, we report on a monitoring system that provides robust prediction of plant growth dynamics. Based on the knowledge about plants growth, we propose a solution that consists of two data analysis stages. The first consists of image preprocessing using filters and time series pruning applying statistical methods. On the second stage we make predictions with superficial machine learning algorithms. The proposed solution can run on embedded devices locally. The computation on low-power devices allows us to reduce the data transmission and ensure ‘distributed intelligence’. As for the dataset, we collected the top-down view sequences of plant images. We achieved 5% relative error on four days predictions. The experimental results and modelling demonstrate the high potential of the proposed solution for the precision agriculture industry, being a crucial part for the development of a plant growth control system.