Assessment of Leaf Area and Biomass through AI-Enabled Deployment

• Published in: Eng (Basel, Switzerland) Switzerland), 2023-09, Vol.4 (3), p.2055-2074
• Main Authors: Shadrin, Dmitrii, Menshchikov, Alexander, Nikitin, Artem, Ovchinnikov, George, Volohina, Vera, Nesteruk, Sergey, Pukalchik, Mariia, Fedorov, Maxim, Somov, Andrey
• Format: Article
• Language: English
• Subjects: Agriculture; artificial intelligence; Artificial neural networks; Biomass; Cameras; deployment; environmental sensing; Greenhouses; image analysis; Image reconstruction; Image segmentation; Leaves; neural networks; Physical work; Plant monitoring; sensor network; Sensors
• ISSN: 2673-4117; 2673-4117
• DOI: 10.3390/eng4030116

Leaf area and biomass are important morphological parameters for in situ plant monitoring since a leaf is vital for perceiving and capturing the environmental light as well as represents the overall plant development. The traditional approach for leaf area and biomass measurements is destructive requiring manual labor and may cause damages for the plants. In this work, we report on the AI-based approach for assessing and predicting the leaf area and plant biomass. The proposed approach is able to estimate and predict the overall plants biomass at the early stage of growth in a non-destructive way. For this reason we equip an industrial greenhouse for cucumbers growing with the commercial off-the-shelf environmental sensors and video cameras. The data from sensors are used to monitor the environmental conditions in the greenhouse while the top-down images are used for training Fully Convolutional Neural Networks (FCNN). The FCNN performs the segmentation task for leaf area calculation resulting in 82% accuracy. Application of trained FCNNs to the sequences of camera images allowed the reconstruction of per-plant leaf area and their growth-dynamics. Then we established the dependency between the average leaf area and biomass using the direct measurements of the biomass. This in turn allowed for reconstruction and prediction of the dynamics of biomass growth in the greenhouse using the image data with 10% average relative error for the 12 days prediction horizon. The actual deployment showed the high potential of the proposed data-driven approaches for plant growth dynamics assessment and prediction. Moreover, it closes the gap towards constructing fully closed autonomous greenhouses for harvests and plants biological safety.