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Deep learning significantly supports key tasks in science, engineering, and precision agriculture. In this study, we propose a method for automatically determining maize developmental stages on the BBCH scale (phases 10-19) using RGB and multispectral images, deep neural networks, and a voting classifier. The method was evaluated using RGB images and multispectral data from the MicaSense RedEdge MX-Dual camera, with training conducted on HTC_r50, HTC_r101, HTC_x101, and Mask2Former architectures. The models were trained on RGB images and separately on individual spectral channels from the multispectral camera, and their effectiveness was evaluated based on classification performance. For multispectral images, a voting classifier was employed because the varying perspectives of individual spectral channels made it impossible to align and merge them into a single coherent image. Results indicate that HTC_r50, HTC_r101, and HTC_x101 trained on spectral channels with a voting classifier outperformed their RGB-trained counterparts in precision, recall, and F1-score, while Mask2Former demonstrated higher precision with a voting classifier but achieved better accuracy, recall, and F1-score when trained on RGB images. Mask2Former trained on RGB images yielded the highest accuracy, whereas HTC_r50 trained on spectral channels with a voting classifier achieved superior precision, recall, and F1-score. This approach facilitates automated monitoring of maize growth stages and supports result aggregation for precision agriculture applications. It offers a scalable framework that can be adapted for other crops with appropriate labeled datasets, highlighting the potential of deep learning for crop condition assessment in precision agriculture and beyond.
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S. Bansal, M. Singh, S. Barda, N. Goel, and M. Saini. PA-RDFKNet: Unifying Plant Age Estimation through RGB-Depth Fusion and Knowledge Distillation. IEEE Transactions on AgriFood Electronics, 2(2):226-235, 2024. https://doi.org/10.1109/TAFE.2024.3418818. (Crossref)
A. Bera, D. Bhattacharjee, and O. Krejcar. An attention-based deep network for plant disease classification. Machine Graphics and Vision, 33(1):47-67, 2024. https://doi.org/10.22630/MGV.2024.33.1.3. (Crossref)
A. Bera, D. Bhattacharjee, and O. Krejcar. PND-Net: Plant Nutrition Deficiency and Disease Classification Using Graph Convolutional Network. Scientific Reports, 14(1):15537, 2024. https://doi.org/10.1038/s41598-024-66543-7. (Crossref)
A. Bera, O. Krejcar, and D. Bhattacharjee. RAFA-Net: Region Attention Network for Food Items and Agricultural Stress Recognition. IEEE Transactions on AgriFood Electronics, pp. 1-13, 2024. https://doi.org/10.1109/TAFE.2024.3466561. (Crossref)
L. Bompani et al. Accelerating Image-based Pest Detection on a Heterogeneous Multicore Microcontroller. IEEE Transactions on AgriFood Electronics, 2(2):170-180, 2024. https://doi.org/10.1109/TAFE.2024.3451888. (Crossref)
L. Bottou. Stochastic Gradient Descent Tricks. In: G. Montavon, G. B. Orr, and K.-R. Müller, eds., Neural Networks: Tricks of the Trade, vol. 7700 of Lecture Notes in Computer Science, pp. 421-436. Springer, second edition edn., 2012. https://doi.org/10.1007/978-3-642-35289-8_25. (Crossref)
K. Chen, J. Pang, J. Wang, Y. Xiong, X. Li, et al. Hybrid Task Cascade for Instance Segmentation. In: Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4974-4983. Long Beach, CA, USA, June 16-20 2019. https://doi.org/10.1109/CVPR.2019.00511. (Crossref)
K. Chen, J. Wang, J. Pang, Y. Cao, Y. Xiong, et al. MMDetection: Open MMLab Detection Toolbox and Benchmark. arXiv, 2019. ArXiv:1906.07155. https://doi.org/10.48550/arXiv.1906.07155.
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. In: Proc. European Conference on Computer Vision (ECCV), pp. 801-818. Springer, Munich, Germany, September 8-14 2018. https://doi.org/10.1007/978-3-030-01234-2_49. (Crossref)
B. Cheng, A. Schwing, and A. Kirillov. Masked-attention Mask Transformer for Universal Image Segmentation. In: Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1280-1289. New Orleans, LA, USA, 2022. https://doi.org/10.1109/CVPR52688.2022.01280. (Crossref)
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, et al. The Cityscapes Dataset for Semantic Urban Scene Understanding. In: Proc. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3213-3223. IEEE, Las Vegas, NV, USA, June 27-30 2016. https://doi.org/10.1109/CVPR.2016.350. (Crossref)
S. Figiel. Development of Artificial Intelligence and Potential Impact of Its Applications in Agriculture on Labor Use and Productivity. Zagadnienia Ekonomiki Rolnej / Problems of Agricultural Economics, 373(4):5-21, 2022. https://doi.org/10.30858/zer/153583. (Crossref)
R. Girshick. Fast R-CNN. In: IEEE International Conference on Computer Vision (ICCV), pp. 1440-1448. Santiago, Chile, December 7-13 2015. https://doi.org/10.1109/ICCV.2015.169. (Crossref)
K. He, G. Gkioxari, P. Dollár, and R. Girshick. Mask R-CNN. In: IEEE International Conference on Computer Vision (ICCV), pp. 2980-2988. Venice, Italy, October 22-29 2017. https://doi.org/10.1109/ICCV.2017.322. (Crossref)
K. He, X. Zhang, S. Ren, and J. Sun. Deep Residual Learning for Image Recognition. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778. IEEE, Las Vegas, NV, USA, 2016. https://doi.org/10.1109/CVPR.2016.90. (Crossref)
A. Kamilaris and F. X. Prenafeta-Boldú. Deep Learning in Agriculture: A Survey. Computers and Electronics in Agriculture, 147:70-90, 2018. https://doi.org/10.1016/j.compag.2018.02.016. (Crossref)
P. D. Lancashire, H. Bleiholder, T. van den Boom, P. Langelüddeke, R. Stauss, et al. A Uniform Decimal Code for Growth Stages of Crops and Weeds: BBCH Monograph. Annals of Applied Biology, 119(3):561-601, 1991. https://doi.org/10.1111/j.1744-7348.1991.tb04895.x. (Crossref)
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, et al. Feature Pyramid Networks for Object Detection. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 936-944. Honolulu, HI, USA, July 21-26 2017. https://doi.org/10.1109/CVPR.2017.106. (Crossref)
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár. Focal Loss for Dense Object Detection. In: Proc. 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980-2988. IEEE, Venice, Italy, October 22-29 2017. https://doi.org/10.1109/ICCV.2017.324. (Crossref)
M. Liu, W.-H. Su, and X.-Q. Wang. Quantitative Evaluation of Maize Emergence Using UAV Imagery and Deep Learning. Remote Sensing, 15(8):1979, 2023. https://doi.org/10.3390/rs15081979. (Crossref)
U. Meier, H. Bleiholder, L. Buhr, C. Feller, H. Hack, et al. The BBCH system for coding the phenological growth stages of plants - history and publications. Journal für Kulturpflanzen, 61(2):41–52, 2009. https://doi.org/10.5073/JfK.2009.02.01.
E. F. I. Raj, M. Appadurai, and K. Athiappan. Precision farming in modern agriculture. In: Smart Agriculture Automation Using Advanced Technologies: Data Analytics and Machine Learning, Cloud Architecture, Automation and IoT, pp. 61-87. Springer Singapore, Singapore, 2022. https://doi.org/10.1007/978-981-16-6124-2_4. (Crossref)
S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 91-99. Montreal, Canada, December 7-12 2015. https://doi.org/10.1109/TPAMI.2016.2577031. (Crossref)
T. A. Shaikh, T. Rasool, and F. R. Lone. Towards Leveraging the Role of Machine Learning and Artificial Intelligence in Precision Agriculture and Smart Farming. Computers and Electronics in Agriculture, 198:107119, 2022. https://doi.org/10.1016/j.compag.2022.107119. (Crossref)
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15(56):1929-1958, June 2014. https://doi.org/10.5555/2627435.2670313.
J. Stypułkowska. bbch-maize-learning-curves. https://github.com/JustynaStypulkowska/bbch-maize-learning-curves, 2024. GitHub repository.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, et al. Attention Is All You Need. In: Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 6000-6010. Curran Associates, Inc., Long Beach, CA, USA, December 4-9 2017. https://doi.org/10.5555/3295222.3295349.
J. Wu, V. Abolghasemi, M. H. Anisi, U. Dar, A. Ivanov, et al. Strawberry Disease Detection Through an Advanced Squeeze-and-Excitation Deep Learning Model. IEEE Transactions on AgriFood Electronics, 2(2):259-267, 2024. https://doi.org/10.1109/TAFE.2024.3412285. (Crossref)
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He. Aggregated Residual Transformations for Deep Neural Networks. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5987-5995. IEEE, Honolulu, HI, USA, July 21-26 2017. https://doi.org/10.1109/CVPR.2017.634. (Crossref)
X. Xu, L. Wang, M. Shu, X. Liang, A. Ghafoor, et al. Detection and Counting of Maize Leaves Based on Two-Stage Deep Learning with UAV-Based RGB Image. Remote Sensing, 14(21):5388, 2022. https://doi.org/10.3390/rs14215388. (Crossref)
Y. Yao, J. Yue, Y. Liu, H. Yang, H. Feng, et al. Classification of Maize Growth Stages Based on Phenotypic Traits and UAV Remote Sensing. Agriculture, 14(7):1175, 2024. https://doi.org/10.3390/agriculture14071175. (Crossref)
D. Yu, Y. Zha, Z. Sun, et al. Deep Convolutional Neural Networks for Estimating Maize Above-Ground Biomass Using Multi-Source UAV Images: A Comparison with Traditional Machine Learning Algorithms. Precision Agriculture, 24(1):92-113, 2023. https://doi.org/10.1007/s11119-022-09932-0. (Crossref)
X. Zan, X. Zhang, Z. Xing, W. Liu, X. Zhang, et al. Automatic Detection of Maize Tassels from UAV Images by Combining Random Forest Classifier and VGG16. Remote Sensing, 12(18):3049, 2020. https://doi.org/10.3390/rs12183049. (Crossref)
H. Zhang, Y. Liu, K. Ma, H. Su, S. Li, et al. Transformers for Image Segmentation. In: Proc. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1251-1260. IEEE, Long Beach, CA, USA, June 16-20 2019. https://doi.org/10.1109/CVPR.2019.00130. (Crossref)
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia. Pyramid Scene Parsing Network. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2881-2890. Honolulu, HI, USA, July 21-26 2017. https://doi.org/10.1109/CVPR.2017.660. (Crossref)
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, et al. Scene Parsing through ADE20K Dataset. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5122-5130. IEEE, Honolulu, HI, USA, July 21-26 2017. https://doi.org/10.1109/CVPR.2017.544. (Crossref)
X. Zhu, Z. Zhang, Z. Li, X. Wang, J. Sun, et al. Mask2Former: A Transformer Architecture for Universal Image Segmentation. In: Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7823-7833. IEEE, New Orleans, LA, USA, June 19-24 2022. https://doi.org/10.1109/CVPR52688.2022.00767. (Crossref)
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