Abstract
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.
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Claims 4
Claim · model-written summary
FCNN leaf area segmentation achieved 82% accuracy.
- Taxon
- cucumbers
Cucumis sativuse1 - Outcome
- accuracye0
- Value
- 82%e0
- Setting
- industrial greenhousee1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The FCNN performs the segmentation task for leaf area calculation resulting in 82% accuracy.”
e1“industrial greenhouse for cucumbers growing”
Claim · model-written summary
Applying trained FCNNs reconstructed per plant leaf area and growth dynamics.
- Taxon
- cucumbersCucumis sativuse1
- Outcome
- reconstruction of per-plant leaf area and their growth-dynamicse0
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Application of trained FCNNs to the sequences of camera images allowed the reconstruction of per-plant leaf area and their growth-dynamics.”
e1“industrial greenhouse for cucumbers growing”
Claim · model-written summary
Average leaf area depended on directly measured biomass.
- Taxon
- cucumbersCucumis sativuse1
- Organ / stage
- leafe0
- Outcome
- biomasse0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“we established the dependency between the average leaf area and biomass using the direct measurements of the biomass”
e1“industrial greenhouse for cucumbers growing”
Claim · model-written summary
Image data predicted biomass growth over 12 days with 10% average relative error.
- Taxon
- cucumbersCucumis sativuse1
- Outcome
- average relative errore0
- Value
- 10%e0
- Qualifier
- 12 days prediction horizone0
- Setting
- greenhousee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“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”
e1“industrial greenhouse for cucumbers growing”