Abstract
Nowadays, people are more concerned about the requirement of good quality food and healthier food. Hence, the food manufacturing and processing industries showed their interest in Artificial Intelligence (AI) and Machine Learning (ML) technologies for improving the food quality in major crops and plants. AI and ML are applied in agriculture sectors to protect the field crops from pest attacks. The Fall Armyworm (FAW) is one of the most devastating insects that cause a severe infestation in important field crops to reduce the crop yield. The manual inspection of FAW insects is critical when dealing with similar insects such as corn borer, armyworm, and corn earworm. In this work, FAW insect detection system based on Mask Region Convolutional Neural Network (Mask R-CNN) was proposed to detect the FAW insects and ensure crop quality and safety. The FAW insect dataset is pre-processed, augmented, and annotated to train the Mask R-CNN model. The outcome of the Mask R-CNN detection model was compared with Region CNN (R-CNN), Faster R-CNN, RetinaNet, and Single Shot Multi-box Detector (SSD). The FAW insect detection results showed that the mean average precision of 94.21% was observed in the Mask R-CNN model with the Resnet-101 backbone which proves the effectiveness of the model for instance segmentation of FAW insects in the field crops.
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References
Amara, J., Bouaziz, B., Algergawy, A.: A deep learning-based approach for banana leaf diseases classification. BTW 2017, Datenbanksysteme für Business, Technologie und Web, Stuttgart, Deutschland, pp. 79–88 (2017)
Cheng, X., Youhua, Z., Yiqiong, C., Yunzhi, W., Yi, Y.: Pest identification via deep residual learning in complex background. Comput Electron Agric. 141, 351–356 (2017)
Chiwamba, S.H., Phiri, J., Nkunika, P.O., Nyirenda, M., Kabemba, M.M.: An application of machine learning algorithms in automated Identification and capturing of fall armyworm (FAW) moths in the field. ICICT2018, In: 2nd IEEE International Conference in Information and Communication Technologies, Lusaka, Zambai (2018).
Chulu, F., Phiri, J., Nyirenda, M., Kabemba, M.M., Nkunika, P., Chiwamba, S.: Developing an automatic identification and early warning and monitoring web based system of fall army worm based on machine learning in developing countries. Zambia ICT J. 3, 13–20 (2019)
Dutta, A., Zisserman, A.: The VIA annotation software for images, audio and video. In: Proceedings of the 27th ACM International Conference on Multimedia. pp. 2276–2279 (2019)
Hailu, G., Niassy, S., Zeyaur, K.R., Ochatum, N., Subramanian, S.: Maize-legume intercropping and push-pull for management of fall armyworm, stemborers, and striga in Uganda. Agron J. 110, 2513–2522 (2018)
He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask R-CNN. arXiv:1703.06870 (2017)
Ip, R.H., Ang, L.M., Seng, K.P., Broster, J.C., Pratley, J.E.: Big data and machine learning for crop protection. Comput Electron Agric. 151, 376–383 (2018)
Jin, S., Ma, X., Han, Z., Wu, Y., Yang, W., Liu, W., Qian, C., Ouyang, W.: Towards multi-person pose tracking: Bottom-up and top-down methods. In ICCV PoseTrack Workshop 2, 7 (2017)
Karar, M.E., Alsunaydi, F., Albusaymi, S., Alotaibi, S.: A new mobile application of agricultural pests recognition using deep learning in cloud computing system. Alex. Eng. J. 60, 4423–4432 (2021)
Kiki, M.P., Ahouandjinou, S.A., Assogba, K.M.: Towards A Smart farming for early detection of fall armyworm pest. SmartNets 2019, In: The 2019 International Conference on Smart Applications, Communications and Networking. Sharm El Scheikh, Egypt; pp. 1–5 (2019)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process Syst., 1097–1105 (2012)
Lin, T. Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A. C.: Ssd: Single shot multi-box detector. In: European Conference on Computer Vision. Springer, pp. 21–37 (2016)
Liu, G.: Real-time object detection for autonomous driving-based on deep learning. Doctoral dissertation, Texas A&M University-Corpus Christi (2017)
Liu, H., Cocea, M.: Semi-random partitioning of data into training and test sets in granular computing context. Granul. Comput. 2, 357–386 (2017)
Nam, N.T., Hung, P.D.: Pest detection on traps using deep convolutional neural networks. In: Proceedings of the 2018 International Conference on Control and Computer Vision, pp. 33–38 (2018)
Nieuwenhuizen, A.T., Hemming, J., Suh, H.: Detection and classification of insects on stick-traps in a tomato crop using Faster R-CNN. NCCV18, In: Proceedings of the Netherlands Conference on Computer Vision, Eindhoven, Netherlands (2018)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. pp. 91–99 (2015)
Shen, Y., Huiling, Z., Jiangtao, L., Fuji, J., Digvir, S.J.: Detection of stored-grain insects using deep learning. Comput Electron Agric. 145, 319–325 (2018)
Shi, J., Zhou, Y., Zhang, W.X.: Target detection based on improved mask rcnn in service robot. In: 2019 Chinese Control Conference, Guangzhou, China, pp. 8519–8524 (2017)
Sisodiya, D.B., Raghunandan, B.L., Bhatt, N.A., Verma, H.S., Shewale, C.P., Timbadiya, B.G., Borad, P.K.: The fall armyworm, Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae); first report of new invasive pest in maize fields of Gujarat. India. J Entomol Zool Stud. 6, 2089–2091 (2018)
Sun, Y., Liu, X., Yuan, M., Ren, L., Wang, J., Chen, Z.: Automatic in-trap pest detection using deep learning for pheromone-based Dendroctonus valens monitoring. Biosyst. Eng. 176, 140–150 (2018)
Venkateswarlu, U., Johnson, M., Narasimhulu, R., Muralikrishna, T.: Occurrence of the fall armyworm, Spodoptera frugiperda (JE Smith) (Lepidoptera, Noctuidae), a new pest on bajra and sorghum in the fields of agricultural research station, Ananthapuramu, Andhra Pradesh, India. J Entomol Zool. 811–823 (2018)
Wu, X., Wen, S., Xie, Y.A.: Improvement of Mask-RCNN Object Segmentation Algorithm. In: International Conference on Intelligent Robotics and Applications Springer, Cham. 582–591 (2019)
Xia, D., Peng, C., Bing, W., Jun, Z., Chengjun, X.: Insect detection and classification based on an improved convolutional neural network. Sensors. 18, 4169 (2018)
Xie, C., Wang, R., Zhang, J., Chen, P., Li, R., Chen, T., Chen, H.: Multi-level learning features for automatic classification of field crop pests. Comput Electron Agric. 152, 233–241 (2018)
Xie, C., Zhang, J., Li, R., Li, J., Hong, P., Xia, J., Chen, P.: Automatic classification for field crop insects via multiple-task sparse representation and multiple-kernel learning. Comput Electron Agric. 119, 123–132 (2015)
Zhang, C., Pengyou, W., Hui, G., Gaojuan, F., Ke, C., Joni-Kristian, K.: Turning wingbeat sounds into spectrum images for acoustic insect classification. Electron. Lett. 53, 1674–1676 (2017)
Acknowledgements
Author K. Thenmozhi acknowledges to Department of Science and Technology, India, for financial support of the work under women scientist scheme B, Grant Number: DST/Disha/SoRF-PM/059/2013. The authors also gratefully acknowledge infrastructural supports from Department of Computer Applications, National Institute of Technology, Tiruchirappalli, India.
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Kasinathan, T., Uyyala, S.R. Detection of fall armyworm (spodoptera frugiperda) in field crops based on mask R-CNN. SIViP 17, 2689–2695 (2023). https://doi.org/10.1007/s11760-023-02485-3
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DOI: https://doi.org/10.1007/s11760-023-02485-3