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A Lightweight Low-Power Model for the Detection of Plant Leaf Diseases

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Abstract

To meet the need for food on a worldwide scale, both in terms of quality and quantity, it is essential to protect plants generally against disease. Even while the problem with diseases is well understood, it remains difficult to quickly identify them, especially in regions where the required infrastructure is missing. One potential answer is to employ edge devices for disease diagnostics. As a proof of idea, the Raspberry Pi is utilized to highlight the potential of smaller variants on compute devices with minimal power. Smartphones are one type of gadget. Currently, 83.72% of people use cell phones worldwide. It would be advantageous to use cell phones to serve this purpose; this project proposes a framework to do so. These images depict real-world situations and are updated using a publicly available PlantVillage dataset, additional images from Google Photographs, the inaturalist website, and real-time image capturing. SqueezeNet and EfficientDet-Lite0 are two deep learning models that are employed. EfficientDet-Lite0 is utilized for object detection, such as leaves, while SqueezeNet is utilized for picture classification, such as plant and disease diagnosis. On a Raspberry Pi 4, the model that was trained can get 97.89% accuracy in less than a second during inference. This should demonstrate how cell phones and other low-powered edge devices may be used to solve this issue.

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Public available dataset.

References

  1. Oerke E-C, Dehne H-W. Safeguarding production-losses in major crops and the role of crop protection. Crop Prot. 2004;23(4):275–85.

    Article  Google Scholar 

  2. Avnery S, Mauzerall DL, Liu J, Horowitz LW. Global crop yield reductions due to surface ozone exposure: 1. Year 2000 crop production losses and economic damage. Atmos Environ. 2011;45(13):2284–96.

    Article  CAS  ADS  Google Scholar 

  3. Dhaliwal G, Jindal V, Mohindru B, et al. Crop losses due to insect pests: global and Indian scenario. Indian J Entomol. 2015;77(2):165–8.

    Article  Google Scholar 

  4. Pandey A, Jain K. A robust deep attention dense convolutional neural network for plant leaf disease identification and classification from smart phone captured real world images. Eco Inform. 2022;70: 101725.

    Article  Google Scholar 

  5. Deshpande R, Patidar H. Detection of plant leaf disease by generative adversarial and deep convolutional neural network. J Inst Eng (India) Ser B. 2023;5:1–10.

    Google Scholar 

  6. Picon A, Alvarez-Gila A, Seitz M, Ortiz-Barredo A, Echazarra J, Johannes A. Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Comput Electron Agric. 2019;161:280–90.

    Article  Google Scholar 

  7. Hughes D, Salathé M, et al. An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv:1511.08060 (arXiv preprint) (2015).

  8. Ferentinos KP. Deep learning models for plant disease detection and diagnosis. Comput Electron Agric. 2018;145:311–8.

    Article  Google Scholar 

  9. Liang Q, Xiang S, Hu Y, Coppola G, Zhang D, Sun W. Pd2se-net: computer-assisted plant disease diagnosis and severity estimation network. Comput Electron Agric. 2019;157:518–29.

    Article  Google Scholar 

  10. Singh KK. An artificial intelligence and cloud based collaborative platform for plant disease identification, tracking and forecasting for farmers. In: 2018 IEEE international conference on cloud computing in emerging markets (CCEM), pp 49–56. IEEE; 2018.

  11. Singh D, Jain N, Jain P, Kayal P, Kumawat S, Batra N. Plantdoc: a dataset for visual plant disease detection. In: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, 2020;249–253.

  12. Parraga-Alava J, Cusme K, Loor A, Santander E. Rocole: a robusta coffee leaf images dataset for evaluation of machine learning based methods in plant diseases recognition. Data Brief. 2019;25: 104414.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Oyewola DO, Dada EG, Misra S, Damaševičius R. Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing. PeerJ Comput Sci. 2021;7:352.

    Article  Google Scholar 

  14. Barbedo JGA, Koenigkan LV, Halfeld-Vieira BA, Costa RV, Nechet KL, Godoy CV, Junior ML, Patricio FRA, Talamini V, Chitarra LG, et al. Annotated plant pathology databases for image-based detection and recognition of diseases. IEEE Lat Am Trans. 2018;16(6):1749–57.

    Article  Google Scholar 

  15. Wiesner-Hanks T, Stewart EL, Kaczmar N, DeChant C, Wu H, Nelson RJ, Lipson H, Gore MA. Image set for deep learning: field images of maize annotated with disease symptoms. BMC Res Notes. 2018;11(1):1–3.

    Article  Google Scholar 

  16. Prajapati HB, Shah JP, Dabhi VK. Detection and classification of rice plant diseases. Intell Decis Technol. 2017;11(3):357–73.

    Google Scholar 

  17. Kim W-S, Lee D-H, Kim Y-J. Machine vision-based automatic disease symptom detection of onion downy mildew. Comput Electron Agric. 2020;168: 105099.

    Article  Google Scholar 

  18. Wang D, Xiang Z, Fesenmaier DR. Adapting to the mobile world: a model of smartphone use. Ann Tour Res. 2014;48:11–26.

    Article  CAS  Google Scholar 

  19. Johannes A, Picon A, Alvarez-Gila A, Echazarra J, Rodriguez-Vaamonde S, Navajas AD, Ortiz-Barredo A. Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Comput Electron Agric. 2017;138:200–9.

    Article  Google Scholar 

  20. Kondaveeti HK, Bandi D, Mathe SE, Vappangi S, Subramanian M. A review of image processing applications based on raspberry-pi. In: 2022 8th international conference on advanced computing and communication systems (ICACCS), 2022;1:22–28. IEEE

  21. Gonzalez-Huitron V, León-Borges JA, Rodriguez-Mata A, Amabilis-Sosa LE, Ramírez-Pereda B, Rodriguez H. Disease detection in tomato leaves via CNN with lightweight architectures implemented in raspberry pi 4. Comput Electron Agric. 2021;181: 105951.

    Article  Google Scholar 

  22. Albattah W, Nawaz M, Javed A, Masood M, Albahli S. A novel deep learning method for detection and classification of plant diseases. Complex Intell Syst. 2022;20:1–18.

    Google Scholar 

  23. Dong J, Xiao X, Menarguez MA, Zhang G, Qin Y, Thau D, Biradar C, Moore III. B: mapping paddy rice planting area in northeastern Asia with landsat 8 images, phenology-based algorithm and google earth engine. Remote Sens Environ. 2016;185:142–54.

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  24. Hart AG, Bosley H, Hooper C, Perry J, Sellors-Moore J, Moore O, Goodenough AE. Assessing the accuracy of free automated plant identification applications. People Nat. 2023;20:23.

    Google Scholar 

  25. Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and \(<\) 0.5 mb model size. arXiv:1602.07360 (arXiv preprint) (2016).

  26. Tan M, Pang R, Le QV. Efficientdet: scalable and efficient object detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020;10781–10790.

  27. Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA. Using deep transfer learning for image-based plant disease identification. Comput Electron Agric. 2020;173: 105393.

    Article  Google Scholar 

  28. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014).

  29. Liu X, Min W, Mei S, Wang L, Jiang S. Plant disease recognition: a large-scale benchmark dataset and a visual region and loss reweighting approach. IEEE Trans Image Process. 2021;30:2003–15.

    Article  PubMed  ADS  Google Scholar 

  30. Chen J-W, Lin W-J, Cheng H-J, Hung C-L, Lin C-Y, Chen S-P. A smartphone-based application for scale pest detection using multiple-object detection methods. Electronics. 2021;10(4):372.

    Article  CAS  Google Scholar 

  31. Zhang Y, Song C, Zhang D. Deep learning-based object detection improvement for tomato disease. IEEE Access. 2020;8:56607–14.

    Article  Google Scholar 

  32. Chauhan NK, Singh K. A review on conventional machine learning vs deep learning. In: 2018 international conference on computing, power and communication technologies (GUCON), 2018;347–352. IEEE

  33. Elfatimi E, Eryigit R, Elfatimi L. Beans leaf diseases classification using mobilenet models. IEEE Access. 2022;10:9471–82.

    Article  Google Scholar 

  34. Sekulska-Nalewajko J, Goclawski J. A semi-automatic method for the discrimination of diseased regions in detached leaf images using fuzzy c-means clustering. In: Perspective technologies and methods in MEMS design, 2011;172–175. IEEE

  35. Khamparia A, Saini G, Gupta D, Khanna A, Tiwari S, Albuquerque VHC. Seasonal crops disease prediction and classification using deep convolutional encoder network. Circ Syst Signal Process. 2020;39:818–36.

    Article  Google Scholar 

  36. Sunil C, Jaidhar C, Patil N. Cardamom plant disease detection approach using efficientnetv2. IEEE Access. 2021;10:789–804.

    Google Scholar 

  37. Mohanty SP, Hughes DP, Salathé M. Using deep learning for image-based plant disease detection. Front Plant Sci. 2016;7:1419.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Praveen P, Nischitha M, Supriya C, Yogitha M, Suryanandh A. To detect plant disease identification on leaf using machine learning algorithms. In: Intelligent system design: proceedings of INDIA 2022, 2022;239–249.

  39. Sundararajan M, Taly A, Yan Q. Axiomatic attribution for deep networks. In: International conference on machine learning, 2017;3319–3328. PMLR

  40. Khan K, Khan RU, Albattah W, Qamar AM. End-to-end semantic leaf segmentation framework for plants disease classification. Complexity. 2022;20:22.

    Google Scholar 

  41. Durmuş H, Güneş EO, Kırcı M. Disease detection on the leaves of the tomato plants by using deep learning. In: 2017 6th international conference on agro-geoinformatics, 2017;1–5. IEEE

  42. Arathi B, Dulhare UN. Classification of cotton leaf diseases using transfer learning-densenet-121. In: Proceedings of third international conference on advances in computer engineering and communication systems: ICACECS 2022, 2023;393–405. Springer

  43. Ahmad A, El Gamal A, Saraswat D. Toward generalization of deep learning-based plant disease identification under controlled and field conditions. IEEE Access. 2023;11:9042–57.

    Article  Google Scholar 

  44. Shrivastava VK, Pradhan MK. Rice plant disease classification using color features: a machine learning paradigm. J Plant Pathol. 2021;103:17–26.

    Article  Google Scholar 

  45. Kumar P, Raghavendran S, Silambarasan K, Kannan KS, Krishnan N. Mobile application using dcdm and cloud-based automatic plant disease detection. Environ Monit Assess. 2023;195(1):44.

    Article  Google Scholar 

  46. Chouhan SS, Singh UP, Jain S. Automated plant leaf disease detection and classification using fuzzy based function network. Wireless Pers Commun. 2021;121:1757–79.

    Article  Google Scholar 

  47. Reda M, Suwwan R, Alkafri S, Rashed Y, Shanableh T. Agroaid: a mobile app system for visual classification of plant species and diseases using deep learning and tensorflow lite. In: Informatics, 2022;9:55. MDPI

  48. Atila Ü, Uçar M, Akyol K, Uçar E. Plant leaf disease classification using efficientnet deep learning model. Eco Inform. 2021;61: 101182.

    Article  Google Scholar 

  49. Van Horn G, Mac Aodha O, Song Y, Cui Y, Sun C, Shepard A, Adam H, Perona P, Belongie S. The inaturalist species classification and detection dataset. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018;8769–8778.

  50. Noyan MA. Uncovering bias in the plantvillage dataset. arXiv:2206.04374 (arXiv preprint) (2022).

  51. Borugadda P, Lakshmi R, Sahoo S. Transfer learning vgg16 model for classification of tomato plant leaf diseases: a novel approach for multi-level dimensional reduction. Pertan J Sci Technol. 2023;31:2.

    Article  Google Scholar 

  52. Rajasree R, Latha CBC, Paul S. Application of transfer learning with a fine-tuned resnet-152 for evaluation of disease severity in tomato plants. In: Mobile computing and sustainable informatics: proceedings of ICMCSI 2022, 2022;695–710.

  53. Pavithra A, Kalpana G, Vigneswaran T. Deep learning-based automated disease detection and classification model for precision agriculture. Soft Comput. 2023;20:1–12.

    Google Scholar 

  54. Kavitha Lakshmi R, Savarimuthu N. A deep learning paradigm for detection and segmentation of plant leaves diseases. Comput Vis Mach Learn Agric. 2022;2:229–43.

    Google Scholar 

  55. Pemasinghe S, Abeygunawardhana PK. Development of an elephant detection and repellent system based on efficientdet-lite models. In: 2023 international conference for advancement in technology (ICONAT), 2023;1–6. IEEE.

  56. Obaid OI, Mohammed MA, Salman AO, Mostafa SA, Elngar AA. Comparing the performance of pre-trained deep learning models in object detection and recognition. J Inf Technol Manage. 2022;14(4):40–56.

    Google Scholar 

  57. Deng C, Wang M, Liu L, Liu Y, Jiang Y. Extended feature pyramid network for small object detection. IEEE Trans Multimed. 2021;24:1968–79.

    Article  Google Scholar 

  58. Pei D, Jing M, Liu H, Sun F, Jiang L. A fast retinanet fusion framework for multi-spectral pedestrian detection. Infrared Phys Technol. 2020;105: 103178.

    Article  Google Scholar 

  59. Wang, K, Liew, JH, Zou, Y, Zhou, D, Feng, J: Panet: few-shot image semantic segmentation with prototype alignment. In: Proceedings of the IEEE/CVF international conference on computer vision, 2019;9197–9206.

  60. Hu G, Wang H, Zhang Y, Wan M. Detection and severity analysis of tea leaf blight based on deep learning. Comput Electr Eng. 2021;90: 107023.

    Article  Google Scholar 

  61. Yu C, Shin Y. Sar ship detection based on improved yolov5 and bifpn. ICT Express. 2023. https://doi.org/10.1016/j.icte.2023.03.009.

    Article  Google Scholar 

  62. Erion G, Janizek JD, Sturmfels P, Lundberg SM, Lee S-I. Improving performance of deep learning models with axiomatic attribution priors and expected gradients. Nat Mach Intell. 2021;3(7):620–31.

    Article  Google Scholar 

  63. Sturmfels P, Lundberg S, Lee S-I. Visualizing the impact of feature attribution baselines. Distill 2020 https://doi.org/10.23915/distill.00022. https://distill.pub/2020/attribution-baselines.

  64. Aftab S, Lal C, Beejal SK, Fatima A. Raspberry pi (python ai) for plant disease detection. Int J Curr Res Rev. 2022;14:36–42.

    Article  Google Scholar 

  65. Buzzy M, Thesma V, Davoodi M, Mohammadpour Velni J. Real-time plant leaf counting using deep object detection networks. Sensors. 2020;20(23):6896.

    Article  PubMed  PubMed Central  ADS  Google Scholar 

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  1. Department of Electronics and Communication Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, Rajasthan, 302017, India

    Uday Chandra Akuthota,  Abhishek & Lava Bhargava

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  1. Uday Chandra Akuthota
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This article is part of the topical collection “Emerging Applications of Data Science for Real-World Problems” guest edited by Satyasai Jagannath Nanda, Rajendra Prasad Yadav, and Mukesh Saraswat.

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Akuthota, U.C., Abhishek & Bhargava, L. A Lightweight Low-Power Model for the Detection of Plant Leaf Diseases. SN COMPUT. SCI. 5, 327 (2024). https://doi.org/10.1007/s42979-024-02658-y

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