Nutritional Monitoring of Rhodena Lettuce via Neural Networks and Point Cloud Analysis
Abstract
:1. Introduction
2. Related Works
3. Methods
- Data acquisition. In this step, we created two datasets of Rhodena lettuce seedling images labeled with macroelements and point clouds.
- Point cloud registration. We used the iterative closest point (ICP) algorithm to register the different point clouds of the crops.
- Training and evaluation. We trained and evaluated multiple machine learning models to detect the lack of nitrogen based on the seedlings morphology and we also trained multiple object detection architectures, including YOLOv8 [48], YOLOv9 [49], and YOLOv10 [50], to detect the lack of potassium based on the lettuces texture.
- Results comparison: In this final step, we compare the results obtained from laboratory tests with those obtained from our system.
3.1. Data Acquisition
3.2. Point Cloud Registration
- From the current variable , recover the rotation matrix and translation vector .
- Perform the ICP update .
- Compute the parameterization of to obtain .
- Compute the accelerated value with Anderson acceleration using , and .
3.3. Detection of Nitrogen Deficiencies
3.4. Detection of Potassium Deficiency
- Single step grid predictions. It divides the image I into grid cells, where each cell contains detection predictions.
- Bounding box regression. It determines the bounding boxes, which correspond to the rectangles that contain the objects in the image. The attributes of these bounding boxes are determined using a single regression module as shown in Equation (3).Here, Y is the vector representation of each bounding box, p is the probability score of each grid cell containing an object [0, 1], are the coordinates of the center of the bounding box concerning the grid cell, is the height and width of the bounding box to the cell, and c is the class for the n number of classes.
- Nonmaximum suppression (NMS). An object may have several overlapped detections. NMS keeps the predictions with the highest detection confidence.
3.4.1. YOLOv8
3.4.2. YOLOv9
3.4.3. YOLOv10
3.5. Evaluation Metrics
4. Results
4.1. Laboratory Tests
4.2. Detection of Nitrogen Deficiencies
4.3. Detection of Potassium Deficiency
5. Discussion and Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Test Laboratory Lettuce Rhodena | ||||
---|---|---|---|---|
Element | Result | Sufficiency | Unit | |
Healthy | Diseased | |||
Nitrogen | 4.91 | 2.05 | 4.50–5.50 | % |
Phosphorus | 0.47 | 0.32 | 0.35–0.40 | % |
Potassium | 6.17 | 2.75 | 6.00–7.00 | % |
Calcium | 1.89 | 0.78 | 1.00–2.00 | % |
Magnesium | 0.81 | 1.48 | 0.30–0.40 | % |
Sulfur | 0.51 | 0.35 | 0.20–0.60 | % |
Iron | 1429 | 3956 | 50.0–100 | ppm |
Zinc | 76.20 | 27.49 | 25.0–100 | ppm |
Manganese | 93.49 | 51.39 | 20.0–200 | ppm |
Copper | 6.24 | 11.25 | 5.00–10.0 | ppm |
Boron | 52.25 | 43.28 | 25.0–80.0 | ppm |
Classifier Crop Production % | ||||||
---|---|---|---|---|---|---|
Stage | NCA | SGD | DTC | Linear | RBF | Poly |
Third | 30.1 | 27.42 | 25.45 | 23.49 | 23.9 | 22.01 |
Second | 52.13 | 48.34 | 53.98 | 53.65 | 52.12 | 53.87 |
First | 1.87 | 2.18 | 5.81 | 2.02 | 5.95 | 5.36 |
Fails | 4.94 | 6.54 | 5.63 | 6.03 | 4.80 | 6.08 |
Accuracy | 89.04 | 84.48 | 90.87 | 85.19 | 86.77 | 87.32 |
15th day Prod. | 84.01 | 77.94 | 85.24 | 79.16 | 81.97 | 81.24 |
Detector | Model | Precision | Recall | mAP50 |
---|---|---|---|---|
YOLOv8 | n | 0.750 | 0.701 | 0.736 |
s | 0.633 | 0.782 | 0.747 | |
m | 0.788 | 0.658 | 0.729 | |
0.786 | 0.658 | 0.713 | ||
x | 0.627 | 0.765 | 0.737 | |
YOLOv9 | t | 0.670 | 0.792 | 0.772 |
s | 0.702 | 0.778 | 0.784 | |
m | 0.653 | 0.798 | 0.783 | |
c | 0.701 | 0.79 | 0.792 | |
e | 0.66 | 0.792 | 0.784 | |
YOLOv10 | n | 0.739 | 0.703 | 0.716 |
s | 0.731 | 0.730 | 0.740 | |
m | 0.800 | 0.639 | 0.740 | |
b | 0.775 | 0.657 | 0.715 | |
l | 0.679 | 0.752 | 0.750 | |
x | 0.764 | 0.687 | 0.726 |
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Ramírez-Pedraza, A.; Salazar-Colores, S.; Terven, J.; Romero-González, J.-A.; González-Barbosa, J.-J.; Córdova-Esparza, D.-M. Nutritional Monitoring of Rhodena Lettuce via Neural Networks and Point Cloud Analysis. AgriEngineering 2024, 6, 3474-3493. https://doi.org/10.3390/agriengineering6030198
Ramírez-Pedraza A, Salazar-Colores S, Terven J, Romero-González J-A, González-Barbosa J-J, Córdova-Esparza D-M. Nutritional Monitoring of Rhodena Lettuce via Neural Networks and Point Cloud Analysis. AgriEngineering. 2024; 6(3):3474-3493. https://doi.org/10.3390/agriengineering6030198
Chicago/Turabian StyleRamírez-Pedraza, Alfonso, Sebastián Salazar-Colores, Juan Terven, Julio-Alejandro Romero-González, José-Joel González-Barbosa, and Diana-Margarita Córdova-Esparza. 2024. "Nutritional Monitoring of Rhodena Lettuce via Neural Networks and Point Cloud Analysis" AgriEngineering 6, no. 3: 3474-3493. https://doi.org/10.3390/agriengineering6030198
APA StyleRamírez-Pedraza, A., Salazar-Colores, S., Terven, J., Romero-González, J. -A., González-Barbosa, J. -J., & Córdova-Esparza, D. -M. (2024). Nutritional Monitoring of Rhodena Lettuce via Neural Networks and Point Cloud Analysis. AgriEngineering, 6(3), 3474-3493. https://doi.org/10.3390/agriengineering6030198