Deep Convolutional Neural Network Architecture for Plant Seedling Classification
Received: 23 August 2022 | Revised: 4 September 2022 | Accepted: 6 September 2022 | Online: 15 September 2022
Corresponding author: N. C. Kundur
Abstract
Weed control is essential in agriculture since weeds reduce yields, increase production cost, impede harvesting, and degrade product quality. As a result, it is indeed critical to recognize weeds early in their vegetation cycle to evade negative impacts to crop growth. Earlier traditional methods used machine learning to determine crops along with weed species, but they had issues with weed detection efficiency at early growth stages. The current work proposes the implementation of a deep learning method that provides accurate results for precise weed recognition. Two different deep convolution neural networks have been used for our classification framework, namely Efficient Net B2 and Efficient Net B4. The plant seedlings dataset is utilized to investigate the proposed work. The evaluation metrics average accuracy, precision, recall, and F1-score were used. The findings demonstrate that the proposed approach is capable of differentiating between 12 species of a plant seedling dataset which contains 3 crops and 9 weeds. The average classification accuracy and F1 score are 99.00% for our Efficient Net B4 model and 97.00% for the Efficient Net B2. In addition, the proposed Efficient Net-B4 model performance is compared to the one of existing models on the plant seedlings dataset and the results showed that the proposed model Efficient Net B4 has superior performance. We intend to detect diseases in the identified plant species in our future research.
Keywords:
deep learning, efficient net, machine learning, weed recognition, plant seedling classificationDownloads
References
T. M. Giselsson, R. N. Jorgensen, P. K. Jensen, M. Dyrmann, and H. S. Midtiby, "A Public Image Database for Benchmark of Plant Seedling Classification Algorithms." arXiv, Nov. 15, 2017.
S. Murawwat, A. Qureshi, S. Ahmad, and Y. Shahid, "Weed Detection Using SVMs," Engineering, Technology & Applied Science Research, vol. 8, no. 1, pp. 2412–2416, Feb. 2018. DOI: https://doi.org/10.48084/etasr.1647
N. C. Eli-Chukwu, "Applications of Artificial Intelligence in Agriculture: A Review," Engineering, Technology & Applied Science Research, vol. 9, no. 4, pp. 4377–4383, Aug. 2019. DOI: https://doi.org/10.48084/etasr.2756
S. L. Sanga, D. Machuve, and K. Jomanga, "Mobile-based Deep Learning Models for Banana Disease Detection," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5674–5677, Jun. 2020. DOI: https://doi.org/10.48084/etasr.3452
S. Alqethami, B. Almtanni, W. Alzhrani, and M. Alghamdi, "Disease Detection in Apple Leaves Using Image Processing Techniques," Engineering, Technology & Applied Science Research, vol. 12, no. 2, pp. 8335–8341, Apr. 2022. DOI: https://doi.org/10.48084/etasr.4721
C.-C. Andrea, B. B. Mauricio Daniel, and J. B. Jose Misael, "Precise weed and maize classification through convolutional neuronal networks," in Second Ecuador Technical Chapters Meeting, Salinas, Ecuador, Oct. 2017, pp. 1–6. DOI: https://doi.org/10.1109/ETCM.2017.8247469
G. Sehgal, B. Gupta, K. Paneri, K. Singh, G. Sharma, and G. Shroff, "Crop Planning using Stochastic Visual Optimization," in IEEE Visualization in Data Science, Phoenix, AZ, USA, Oct. 2017, pp. 47–51. DOI: https://doi.org/10.1109/VDS.2017.8573443
S. T. Hang and M. Aono, "Open world plant image identification based on convolutional neural network," in Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, Jeju, Korea (South), Dec. 2016, pp. 1–4. DOI: https://doi.org/10.1109/APSIPA.2016.7820676
A. Lee, Comparing Deep Neural Networks and Traditional Vision Algorithms in Mobile Robotics. Swarthmore, PA, USA: Swarthmore College, 2017.
A. Kamilaris, A. Kartakoullis, and F. X. Prenafeta-Boldu, "A review on the practice of big data analysis in agriculture," Computers and Electronics in Agriculture, vol. 143, pp. 23–37, Dec. 2017. DOI: https://doi.org/10.1016/j.compag.2017.09.037
M. Dyrmann, P. Christiansen, and H. S. Midtiby, "Estimation of plant species by classifying plants and leaves in combination," Journal of Field Robotics, vol. 35, no. 2, pp. 202–212, 2018. DOI: https://doi.org/10.1002/rob.21734
W. M. A. H. B. W. Zefri and S. Nordin, "Plant Recognition System Using Convolutional Neural Network," IOP Conference Series: Earth and Environmental Science, vol. 1019, no. 1, Dec. 2022, Art. no. 012031. DOI: https://doi.org/10.1088/1755-1315/1019/1/012031
A. Milioto, P. Lottes, and C. Stachniss, "Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs," in IEEE International Conference on Robotics and Automation, Brisbane, QLD, Australia, Dec. 2018, pp. 2229–2235. DOI: https://doi.org/10.1109/ICRA.2018.8460962
W.-S. Jeon and and S.-Y. Rhee, "Plant Leaf Recognition Using a Convolution Neural Network," International Journal of Fuzzy Logic and Intelligent Systems, vol. 17, no. 1, pp. 26–34, Mar. 2017. DOI: https://doi.org/10.5391/IJFIS.2017.17.1.26
J. Champ, A. Mora-Fallas, H. Goeau, E. Mata-Montero, P. Bonnet, and A. Joly, "Instance segmentation for the fine detection of crop and weed plants by precision agricultural robots," Applications in Plant Sciences, vol. 8, no. 7, 2020, Art. no. e11373. DOI: https://doi.org/10.1002/aps3.11373
H. A. Elnemr, "Convolutional Neural Network Architecture for Plant Seedling Classification," International Journal of Advanced Computer Science and Applications, vol. 10, no. 8, pp. 319–325, 2019. DOI: https://doi.org/10.14569/IJACSA.2019.0100841
K. Gupta, R. Rani, and N. K. Bahia, "Plant-Seedling Classification Using Transfer Learning-Based Deep Convolutional Neural Networks," International Journal of Agricultural and Environmental Information Systems, vol. 11, no. 4, pp. 25–40, Oct. 2020. DOI: https://doi.org/10.4018/IJAEIS.2020100102
A.-A. Binguitcha-Fare and P. Sharma, "Crops and weeds classification using Convolutional Neural Networks via optimization of transfer learning parameters," International Journal of Engineering and Advanced Technology, vol. 8, no. 5, pp. 2284–2294, 2019.
N. Makanapura, C. Sujatha, P. R. Patil, and P. Desai, "Classification of plant seedlings using deep convolutional neural network architectures," Journal of Physics: Conference Series, vol. 2161, no. 1, Jan. 2022, Art. no. 012006. DOI: https://doi.org/10.1088/1742-6596/2161/1/012006
J. G. A. Barbedo, "A review on the main challenges in automatic plant disease identification based on visible range images," Biosystems Engineering, vol. 144, pp. 52–60, Apr. 2016. DOI: https://doi.org/10.1016/j.biosystemseng.2016.01.017
N. Teimouri, M. Dyrmann, P. R. Nielsen, S. K. Mathiassen, G. J. Somerville, and R. N. Jorgensen, "Weed Growth Stage Estimator Using Deep Convolutional Neural Networks," Sensors, vol. 18, no. 5, May 2018, Art. no. 1580. DOI: https://doi.org/10.3390/s18051580
M. Dyrmann and R. N. Jorgensen, "RoboWeedSupport: weed recognition for reduction of herbicide consumption," in Precision agriculture ’15, Wageningen, Netherlands: Wageningen Academic Publishers, 2015, pp. 571–578. DOI: https://doi.org/10.3920/978-90-8686-814-8_71
S. Hochreiter, "The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 6, no. 2, pp. 107–116, Apr. 1998. DOI: https://doi.org/10.1142/S0218488598000094
M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, "Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network," IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1207–1216, Feb. 2016. DOI: https://doi.org/10.1109/TMI.2016.2535865
N. R. Rahman, Md. A. M. Hasan, and J. Shin, "Performance Comparison of Different Convolutional Neural Network Architectures for Plant Seedling Classification," in 2nd International Conference on Advanced Information and Communication Technology, Dhaka, Bangladesh, Nov. 2020, pp. 146–150. DOI: https://doi.org/10.1109/ICAICT51780.2020.9333468
S. Umamaheswari and A. V. Jain, "Encoder–Decoder Architecture for Crop-Weed Classification Using Pixel-Wise Labelling," in International Conference on Artificial Intelligence and Signal Processing, Amaravati, India, Jan. 2020, pp. 1–6. DOI: https://doi.org/10.1109/AISP48273.2020.9072992
M. Tan and Q. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks," in 36th International Conference on Machine Learning, Long Beach, CA, USA, Jun. 2019, pp. 6105–6114.
C. R. Alimboyong and A. A. Hernandez, "An Improved Deep Neural Network for Classification of Plant Seedling Images," in 15th International Colloquium on Signal Processing & Its Applications, Penang, Malaysia, Mar. 2019, pp. 217–222. DOI: https://doi.org/10.1109/CSPA.2019.8696009
S. Malliga, S. V. Kogilavani, D. Jaivignesh, and S. Jeevananth, "Classification of Plant Seedlings Using Deep Learning Architectures," International Journal of Advanced Science and Technology, vol. 29, no. 3s, pp. 1024-1030, Mar. 2020.
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