PERTANIKA JOURNAL OF SOCIAL SCIENCES AND HUMANITIES

The problematic and undesirable effects of weeds lead to degradation in the quality and productivity of yields. These unacceptable weeds are close competitors of crops as they constantly devour water, air, nutrients, and sunlight which are helpful for the maturation of crops. For better cultivation and good quality production of crops, weed detection at the appropriate time is an essential stride. In recent years, various state-of-the-art (SOTA) architectures were proposed to detect weeds among crop yields, but they lacked computational cost. This paper mainly focuses on proposing a customized state-of-the-art (SOTA) architecture and comparative study with transfer learning models for detecting and classifying weeds among soybean crops by concentrating on the low computational cost. The selected SoTA is beneficial for detecting weeds on a large scale with very low computational costs. In terms of selection, Maximum Validation Accuracy (MVA), Least Validation Cross-Entropy Loss (LVCEL), and Training Time (TT) were considered for proposing an objective function value system. In total, 15 proposed CNNs with 18 Transfer learning models were analyzed with the help of objective function value and various metric evaluations for finding the best and optimal architecture for weed classification. Experimentation and analysis resulted in C13 being robust and optimal architecture which outperformed every CNNs and Transfer learning model by achieving the highest accuracy of 0.9458 with an objective function value of 5.9335 and ROC-AUC of 0.9927 for the classification of weeds from soybean crops.

• Ahmad, A., Saraswat, D., Aggarwal, V., Etienne, A., & Hancock, B. (2021). Performance of deep learning models for classifying and detecting common weeds in corn and soybean production systems. Computers and Electronics in Agriculture, 184, Article 106081. https://doi.org/10.1016/j.compag.2021.106081

• Alfaras, M., Soriano, M. C., & Ortín, S. (2019). A fast machine learning model for ECG-based heartbeat classification and arrhythmia detection. Frontiers in Physics, 7, Article 103. https://doi.org/10.3389/fphy.2019.00103

• Al-Timemy, A. H., Khushaba, R. N., Mosa, Z. M., & Escudero, J. (2021). An efficient mixture of deep and machine learning models for covid-19 and tuberculosis detection using x-ray images in resource-limited settings. In D. Oliva, S. A. Hassan & A. Mohamed (Eds.) Artificial Intelligence for COVID-19 (Vol. 358, pp. 77-100). Springer. https://doi.org/10.1007/978-3-030-69744-0_6

• Aravind, K. R., & Raja, P. (2020). Automated disease classification in (Selected) agricultural crops using transfer learning. Automatika, 61(2), 260-272. https://doi.org/10.1080/00051144.2020.1728911

• Asad, M. H., & Bais, A. (2020). Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network. Information Processing in Agriculture, 7(4), 535-545. https://doi.org/10.1016/j.inpa.2019.12.002

• Badage, A. (2018). Crop disease detection using machine learning: Indian agriculture. International Research Journal of Engineering and Technology (IRJET), 5(9), 866-869.

• Chandra, S., Gourisaria, M. K., GM, H., Konar, D., Gao, X., Wang, T., & Xu, M. (2022). Prolificacy assessment of spermatozoan via state-of-the-art deep learning frameworks. IEEE Access, 10, 13715-13727. https://10.1109/ACCESS.2022.3146334

• Chen, L., & Yuan, Y. (2018). Agricultural disease image dataset for disease identification based on machine learning. In J. Li, X. Meng, Y. Zhang, W. Cui & Du, Z. (Eds.), International Conference on Big Scientific Data Management (Vol. 11473, pp. 263-274). Springer. https://doi.org/10.1007/978-3-030-28061-1_26

• Ferreira, A. D. S., Freitas, D. M., da Silva, G. G., Pistori, H., & Folhes, M. T. (2017). Weed detection in soybean crops using ConvNets. Computers and Electronics in Agriculture, 143, 314-324. https://doi.org/10.1016/j.compag.2017.10.027

• Etienne, A., & Saraswat, D. (2019). Machine learning approaches to automate weed detection by UAV-based sensors. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping IV, International Society for Optics and Photonics (Vol. 11008, Article 110080R). SPIE Digital Library. https://doi.org/10.1117/12.2520536

• Gourisaria, M. K., Harshvardhan, G. M., Agrawal, R., Patra, S. S., Rautaray, S. S., & Pandey, M. (2021). Arrhythmia detection using deep belief network extracted features from ECG signals. International Journal of E-Health and Medical Communications (IJEHMC), 12(6), 1-24. https://doi.org/10.4018/ijehmc.20211101.oa9

• Harshvardhan, G. M., Sahu, A., Gourisaria, M. K., Singh, P. K., Hong, W. C., Singh, V., & Balabantaray, B. K. (2022). On the dynamics and feasibility of transferred inference for diagnosis of invasive ductal carcinoma: A perspective. IEEE Access, 10, 30870-30889. https://doi.org/10.1109/ACCESS.2022.3159700

• Khalajzadeh, H., Mansouri, M., & Teshnehlab, M. (2014). Face recognition using a convolutional neural network and simple logistic classifier. In V. Snášel, P. Krömer, M. Köppen & G. Schaefer (Eds.), Soft Computing in Industrial Applications (Vol. 223, pp. 197-207). Springer. https://doi.org/10.1007/978-3-319-00930-8_18

• Rajagopalan, N., Narasimhan, V., Vinjimoor, S. K., & Aiyer, J. (2021). Retracted article: Deep CNN framework for retinal disease diagnosis using optical coherence tomography images. Journal of Ambient Intelligence and Humanized Computing, 12, 7569-7580. https://doi.org/10.1007/s12652-020-02460-7

• Sannigrahi, A., Singh, V., Gourisaria, M. K., & Srivastava, R. (2021). Diagnosis of skin cancer using feature engineering techniques. In 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) (pp. 405-411). IEEE Publishing. https://doi.org/10.1109/ICAC3N53548.2021.9725420

• Sarah, S., Singh, V., Gourisaria, M. K., & Singh, P. K. (2021). Retinal disease detection using CNN through optical coherence tomography images. In 2021 5th International Conference on Information Systems and Computer Networks (ISCON) (pp. 1-7). IEEE Publishing. https://doi.org/10.1109/ISCON52037.2021.9702480

• Singh, V., Gourisaria, M. K., GM, H., Rautaray, S. S., Pandey, M., Sahni, M., Leon-Castro, & Espinoza-Audelo, L. F. (2022a). Diagnosis of intracranial tumors via the selective CNN data modeling technique. Applied Sciences, 12(6), Article 2900. https://doi.org/10.3390/app12062900

• Singh, V., Gourisaria, M. K., GM, H., & Singh, V. (2022b). Mycobacterium tuberculosis detection using CNN ranking approach. In T. K. Gandhi, D. Konar, B. Sen & Sharma, K. (Eds.), Advanced Computational Paradigms and Hybrid Intelligent Computing, (Vol. 1373, pp. 583-596). Springer. https://doi.org/10.1007/978-981-16-4369-9_56

• Soystats. (2020). International: World soybean production. The American Soybean Association. http://soystats.com/international-world-soybean-production/

• Tang, J., Wang, D., Zhang, Z., He, L., Xin, J., & Xu, Y., (2017). Weed identification based on K-means feature learning combined with the convolutional neural network. Computers and Electronics in Agriculture, 135, 63-70. https://doi.org/10.1016/j.compag.2017.01.001

• Sivakumar, A. N. V., Li, J., Scott, S., Psota, E., Jhala, A. J., Luck, J. D., & Shi, Y. (2020). Comparison of object detection and patch-based classification deep learning models on mid-to late-season weed detection in UAV imagery. Remote Sensing, 12(13), Article 2136. https://doi.org/10.3390/rs12132136

• Yu, J., Sharpe, S. M., Schumann, A. W., & Boyd, N. S. (2019). Deep learning for image-based weed detection in turfgrass. European Journal of Agronomy, 104, 78-84. https://doi.org/10.1016/j.eja.2019.01.004