Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset Acquisition
Compilation of the AgroInsect Dataset
2.2. Training Methods
2.3. Conversion Methods to ONNX and TFLite
2.4. Models’ Parameters and Evaluation Metrics
3. Results
3.1. Evaluation of the Detectron2 Model in Insect Detection
3.2. Evaluation of the YOLO Model for Insect Detection
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Dataset | Classes | |||||
---|---|---|---|---|---|---|
Diabrotica speciosa | Dalbulus maidis | Diceraeus ssp. | Spodoptera frugiperda | Total | ||
AgroInsect | Image | 591 | 177 | 248 | 334 | 1350 |
Annotation | 599 | 280 | 257 | 358 | 1496 | |
Reduced | Image | 100 | 100 | 100 | 100 | 400 |
Annotation | 100 | 156 | 104 | 102 | 462 | |
Validation | Image | 15 | 15 | 15 | 15 | 60 |
Annotation | 15 | 15 | 15 | 15 | 60 | |
Test | Image | 25 | 25 | 25 | 25 | 100 |
Annotation | 25 | 25 | 25 | 25 | 100 |
Model | Detectron2 | YOLOv5n | YOLOv7 | YOLOv8 | YOLOv9-c | YOLOV9-gelan |
---|---|---|---|---|---|---|
Layers | 50 | 168 | 106 | 25 | 70 | 42 |
Activation Function | ReLu 1 | ReLu | ReLu | Leaky R 4 | Leaky R | ReLu |
Loss Function | Cross-entropy | BCE 3 | Cross-entropy | Combined loss | DFL | DFL 5 |
Optimizer | SGD 2 | Adam | SGD | Adam | Adam | SGD |
Learning Rate | 0.00005 | 0.01 | 0.01 | 0.001 | 0.01 | 0.01 |
Batch Size | 2 | 16 | 16 | 16 | 16 | 8 |
Epochs | 10.000 | 300 | 300 | 300 | 300 | 300 |
Regularization | L2 | L2 | L2 | L2 | L2 | L2 |
Dataset | Classes | Precision | Recall | F1-Score | Accuracy |
---|---|---|---|---|---|
AgroInsect | Diabrotica speciosa | 92.31 | 96 | 94.12 | 97.12 |
Dalbulus | 96.15 | 100 | 98.04 | 99.04 | |
Diceraeus | 100 | 96 | 97.96 | 99.04 | |
Spodoptera frugiperda | 96.15 | 100 | 98.04 | 99.04 | |
General | 94.23 | 94.23 | 94.23 | 97.69 | |
Reduced | Diabrotica speciosa | 92.31 | 96 | 94.1176 | 97.0874 |
Dalbulus | 96.15 | 100 | 98.0392 | 99.0291 | |
Diceraeus | 100 | 92 | 95.83 | 98.06 | |
Spodoptera frugiperda | 100 | 100 | 100 | 100 | |
General | 94.17 | 94.17 | 94.17 | 97.67 |
YOLO Version | Model | Dataset | Original Size (MB) | Training Size (MB) |
---|---|---|---|---|
YOLOv5 | YOLOv5n | Reduced | 3.9 | 3.7 |
AgroInsect | 13.8 | |||
YOLOv7 | YOLOv7 | Reduced | 72.1 | 284.7 |
AgroInsect | 284.7 | |||
YOLOv8 | YOLOv8n | Reduced | 6.2 | 6 |
AgroInsect | 35 | |||
YOLOv9 | YOLOv9-c | Reduced | 98.4 | 98 |
AgroInsect | 98 | |||
YOLOv9-gelan | Reduced | 49.1 | 195.2 | |
AgroInsect | 195.2 |
YOLO Version | Dataset | Precision % | Recall % | F1-Score % | Accuracy % |
---|---|---|---|---|---|
YOLOv5 | Reduced | 85.58 | 85.58 | 85.58 | 94.23 |
AgroInsect | 98.04 | 98.04 | 98.04 | 99.22 | |
YOLOv7 | Reduced | 86.67 | 86.67 | 86.67 | 94.67 |
AgroInsect | 96.12 | 96.12 | 96.12 | 98.45 | |
YOLOv8 | Reduced | 86.54 | 86.54 | 86.54 | 94.62 |
AgroInsect | 96.08 | 96.08 | 96.08 | 98.43 | |
YOLOv9-c | Reduced | 97.03 | 97.03 | 97.03 | 98.81 |
AgroInsect | 97.06 | 97.06 | 97.06 | 98.82 | |
YOLOv9-gelan | Reduced | 96.08 | 96.08 | 96.08 | 98.43 |
AgroInsect | 98.04 | 98.04 | 98.04 | 99.22 |
Model | Dataset | Conversion | Precision % | Recall % | F1-Score % | Accuracy % |
---|---|---|---|---|---|---|
YOLOv5 | Reduced | ONNX | 94.23 | 94.23 | 94.23 | 97.69 |
TFLite | 94.23 | 94.23 | 94.23 | 97.69 | ||
AgroInsect | ONNX | 96.15 | 96.15 | 96.15 | 98.46 | |
TFLite | 96.15 | 96.15 | 96.15 | 98.46 | ||
YOLOv7 | Reduced | ONNX | 94.23 | 94.23 | 94.23 | 97.69 |
TFLite | 94.23 | 94.23 | 94.23 | 97.69 | ||
AgroInsect | ONNX | 96.15 | 96.15 | 96.15 | 98.46 | |
TFLite | 96.15 | 96.15 | 96.15 | 98.46 | ||
YOLOv8 | Reduced | ONNX | 86.54 | 86.54 | 86.54 | 94.62 |
TFLite | 86.54 | 86.54 | 86.54 | 94.62 | ||
AgroInsect | ONNX | 96.12 | 96.12 | 96.12 | 98.45 | |
TFLite | 96.12 | 96.12 | 96.12 | 98.45 | ||
YOLOv9-c | Reduced | ONNX | 96.97 | 96.00 | 96.48 | 98.25 |
AgroInsect | ONNX | 97.09 | 97.09 | 97.09 | 98.84 | |
YOLOv9-gelan | Reduced | ONNX | 97.03 | 97.03 | 97.03 | 98.81 |
AgroInsect | ONNX | 98.04 | 98.04 | 98.04 | 99.22 |
Model | Classes | Dataset | Precision | Recall | F1-Score | Accuracy |
---|---|---|---|---|---|---|
YOLOv5s | 4 | AgroInsect | 98.04% | 98.04% | 98.04% | 99.22% |
YOLOv5s | 4 | Reduced | 85.58% | 85.58% | 85.58% | 94.23% |
YOLOv7 | 4 | Reduced | 86.67% | 86.67% | 86.67% | 94.67% |
YOLOv8 | 4 | Reduced | 86.54% | 86.54% | 86.54% | 94.62% |
YOLOv9-gelan | 4 | AgroInsect | 98.04% | 98.04% | 98.04% | 99.22% |
Detectron2 | 4 | AgroInsect | 94.23% | 94.23% | 94.23% | 97.69% |
YOLOv5s [49] | 5 | - | 93.20% | 99.60% | 96% | - |
YOLOv4s [49] | 5 | - | 99% | 93% | 96% | - |
YOLOv7-Adam [97] | 3 | - | 99.95% | - | - | - |
Maize-YOLO [98] | 13 | - | 73.30% | 77.30% | 75.10% | - |
New Version-5x [99] | 7 | - | 86.80% | 88.60% | 87.80% | - |
YOLOv3/5 [100] | 6 | - | 92.70% | 93.90% | 93.20% | - |
EfficientNet-Br [50] | 36 | - | 93.51% | 97.14% | 94.68% | - |
YOLOv3 [67] | 12 | - | 95.15% | 75.79% | 84.35% | 72.96% |
YOLO-MPNET—OSW [101] | 3 | - | 94.14% | 91.99% | 93.05% | - |
YOLOv5-Modificado [102] | 2 | - | 86.84% | 84.58% | 85.69% | - |
CNN [51] | 5 | - | 97.00% | - | - | - |
InceptionV3 [51] | 5 | - | 97.00% | - | - | - |
YOLOv5 [51] | 5 | - | 98.75% | - | - | - |
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de Almeida, G.P.S.; dos Santos, L.N.S.; da Silva Souza, L.R.; da Costa Gontijo, P.; de Oliveira, R.; Teixeira, M.C.; De Oliveira, M.; Teixeira, M.B.; do Carmo França, H.F. Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset. Agronomy 2024, 14, 2194. https://doi.org/10.3390/agronomy14102194
de Almeida GPS, dos Santos LNS, da Silva Souza LR, da Costa Gontijo P, de Oliveira R, Teixeira MC, De Oliveira M, Teixeira MB, do Carmo França HF. Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset. Agronomy. 2024; 14(10):2194. https://doi.org/10.3390/agronomy14102194
Chicago/Turabian Stylede Almeida, Guilherme Pires Silva, Leonardo Nazário Silva dos Santos, Leandro Rodrigues da Silva Souza, Pablo da Costa Gontijo, Ruy de Oliveira, Matheus Cândido Teixeira, Mario De Oliveira, Marconi Batista Teixeira, and Heyde Francielle do Carmo França. 2024. "Performance Analysis of YOLO and Detectron2 Models for Detecting Corn and Soybean Pests Employing Customized Dataset" Agronomy 14, no. 10: 2194. https://doi.org/10.3390/agronomy14102194