Identifying region specifc seasonal crop for leaf borne diseases by utilizing deep learning techniques

• Autorzy: Jayagopal Prabhu , Rajendran Sukumar , Mathivanan Sandeep Kumar , Sathish Kumar Sree Dharinya , Raja Kiruba Thangam , Paneerselvam Surekha

Identyfikatory

DOI

10.1007/s11600-022-00759-x

• Języki publikacji: EN

Abstrakty

EN

India economy depends on agriculture with severe climatic changes and a heavy infestation of diseases depleting food crop yield substantially. Rapid identification and real-time infestation feedback that affects plants are accomplished through computer vision and IoT, thereby providing a reliable system for farmers to increase the season’s growth yield. With LSTM, CNN provides an efficient way of identifying diseases specific leaf in plants through image recognition techniques. An extensive collection of plant leaf images is trained to recognize season-specific diseases like early blight and late blight, leaf mold, and yellow leaf curl. The proposed CNN model identifies the infestation with high accuracy and precision with significantly fewer training epochs. The proposed model provides an efficient way of identifying leaf borne infestation pertained to a particular agricultural region. Furthermore, there is a need to increase and improve different region-specific infestations that arise due to climatic and seasonal changes.

Słowa kluczowe

EN

conventional neural network; agriculture; back propagation; disease; plant; leaf; CNN

PL

konwencjonalna sieć neuronowa; rolnictwo; propagacja wsteczna; choroba; zakład; liść

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2022
• Tom: Vol. 70, no 6
• Strony: 2841--2854
• Opis fizyczny: Bibliogr. 19 poz.

Twórcy

autor

Jayagopal Prabhu

• School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India

autor

Rajendran Sukumar

• School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India

autor

Mathivanan Sandeep Kumar

• School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India

autor

Sathish Kumar Sree Dharinya

• School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India

autor

Raja Kiruba Thangam

• School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, TamilNadu, India

autor

Paneerselvam Surekha

• Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India

Bibliografia

• 1. Atila Ü, Uçar M, Akyol K, Uçar E (2021) Plant leaf disease classification using EfficientNet deep learning model. Ecol Inform 61:101182
• 2. Bari BS, Islam MN, Rashid M, Hasan MJ, Razman MAM, Musa RM, Majeed APA (2021) A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework. PeerJ Computer Sci 7:e432
• 3. Darwish A, Ezzat D, Hassanien AE (2020) An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis. Swarm Evol Comput 52:100616
• 4. Department of Agriculture, Cooperation & Farmers Welfare, Annual Report, 2018–19 http://agricoop.nic.in/annual-report
• 5. Dyrmann M, Karstoft H, Midtiby HS (2016) Plant species classification using deep convolutional neural network. Biosys Eng 151:72–80
• 6. Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318
• 7. Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, Cambridge
• 8. Hassan SM, Maji AK, Jasiński M, Leonowicz Z, Jasińska E (2021) Identification of plant-leaf diseases using CNN and transfer-learning approach. Electronics 10(12):1388
• 9. Jogekar RN, Tiwari N (2021) A review of deep learning techniques for identification and diagnosis of plant leaf disease. Smart Trends Comput Commun Proc SmartCom 2020:435–441
• 10. Khamparia A, Saini G, Gupta D, Khanna A, Tiwari S, de Albuquerque VHC (2020) Seasonal crops disease prediction and classification using deep convolutional encoder network. Circuits Syst Signal Process 39(2):818–836
• 11. Kumar S, Jayagopal P (2021) Delineation of field boundary from multispectral satellite images through U-Net segmentation and template matching. Ecol Inform 64:101370
• 12. Li Y, Nie J, Chao X (2020) Do we really need deep CNN for plant diseases identification? Computers Electron Agric 178:105803
• 13. Li L, Zhang S, Wang B (2021) Plant disease detection and classification by deep learning—a review. IEEE Access 9:56683–56698
• 14. Lu J, Hu J, Zhao G, Mei F, Zhang C (2017) An in-field automatic wheat disease diagnosis system. Comput Electron Agric 142:369–379
• 15. Lu J, Tan L, Jiang H (2021) Review on convolutional neural network (CNN) applied to plant leaf disease classification. Agriculture 11(8):707
• 16. Mathivanan S, Jayagopal P (2019) A big data virtualization role in agriculture: a comprehensive review. Walailak J Sci Technol (WJST) 16(2):55–70
• 17. Sharma P, Berwal YPS, Ghai W (2020) Performance analysis of deep learning CNN models for disease detection in plants using image segmentation. Information Process Agric 7(4):566–574
• 18. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/cvpr.2016.308
• 19. Zhao S, Peng Y, Liu J, Wu S (2021) Tomato leaf disease diagnosis based on improved convolution neural network by attention module. Agriculture 11(7):651

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).