Crop Growth Monitoring System in Vertical Farms Based on Region-of-Interest Prediction

• Published in: Agriculture (Basel) Vol. 12; no. 5; p. 656
• Main Authors: Hwang, Yujin, Lee, Seunghyeon, Kim, Taejoo, Baik, Kyeonghoon, Choi, Yukyung
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 30-04-2022
• Subjects: Agriculture; Artificial intelligence; Automation; Color imagery; Containers; Crop growth; crop growth monitoring; Crop yield; Crops; Datasets; Deep learning; Farms; Image processing; Labeling; Monitoring; Monitoring systems; Performance degradation; Predictions; pseudo label; region-of-interest (RoI) prediction; self-training; stance segmentation; Transfer learning; Vertical farming; vertical farms
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture12050656

Vertical farms are to be considered the future of agriculture given that they not only use space and resources efficiently but can also consistently produce large yields. Recently, artificial intelligence has been introduced for use in vertical farms to boost crop yields, and crop growth monitoring is an essential example of the type of automation necessary to manage a vertical farm system. Region of interest predictions are generally used to find crop regions from the color images captured by a camera for the monitoring of growth. However, most deep learning-based prediction approaches are associated with performance degradation issues in the event of high crop densities or when different types of crops are grown together. To address this problem, we introduce a novel method, termed pseudo crop mixing, a model training strategy that targets vertical farms. With a small amount of labeled crop data, the proposed method can achieve optimal performance. This is particularly advantageous for crops with a long growth period, and it also reduces the cost of constructing a dataset that must be frequently updated to support the various crops in existing systems. Additionally, the proposed method demonstrates robustness with new data that were not introduced during the learning process. This advantage can be used for vertical farms that can be efficiently installed and operated in a variety of environments, and because no transfer learning was required, the construction time for container-type vertical farms can be reduced. In experiments, we show that the proposed model achieved a performance of 76.9%, which is 12.5% better than the existing method with a dataset obtained from a container-type indoor vertical farm. Our codes and dataset will be available publicly.