A Deep Learning-based Mobile Application for Segmenting Tuta Absoluta’s Damage on Tomato Plants
Received: 22 July 2021 | Revised: 5 September 2021 | Accepted: 12 September 2021 | Online: 4 October 2021
With the advances in technology, computer vision applications using deep learning methods like Convolutional Neural Networks (CNNs) have been extensively applied in agriculture. Deploying these CNN models on mobile phones is beneficial in making them accessible to everyone, especially farmers and agricultural extension officers. This paper aims to automate the detection of damages caused by a devastating tomato pest known as Tuta Absoluta. To accomplish this objective, a CNN segmentation model trained on a tomato leaf image dataset is deployed on a smartphone application for early and real-time diagnosis of the pest and effective management at early tomato growth stages. The application can precisely detect and segment the shapes of Tuta Absoluta-infected areas on tomato leaves with a minimum confidence of 70% in 5 seconds only.
Keywords:mobile applications for agriculture, Tuta Absoluta, deep learning, convolutional neural networks, segmentation
Annual Agriculture Sample Survey Crop and Livestock Report. Tanzania: Ministry of Agriculture, 2017.
S. R. Rupanagudi, B. S. Ranjani, P. Nagaraj, V. G. Bhat, and T. G, "A novel cloud computing based smart farming system for early detection of borer insects in tomatoes," in International Conference on Communication, Information & Computing Technology, Mumbai, India, Jan. 2015, pp. 1-6. https://doi.org/10.1109/ICCICT.2015.7045722
V. Mutayoba and D. Ngaruko, "Assessing Tomato Farming and Marketing Among Smallholders in High Potential Agricultural Areas of Tanzania," International Journal of Economics, Commerce and Management, vol. 6, no. 8, pp. 577-590, 2018.
A. H. R. Gonring, A. H. Walerius, M. M. Picanco, L. Bacci, J. C. Martins, and M. C. Picanco, "Feasible sampling plan for Tuta absoluta egg densities evaluation in commercial field tomato," Crop Protection, vol. 136, Oct. 2020, Art. no. 105239. https://doi.org/10.1016/j.cropro.2020.105239
N. Desneux et al., "Biological invasion of European tomato crops by Tuta absoluta: ecology, geographic expansion and prospects for biological control," Journal of Pest Science, vol. 83, no. 3, pp. 197-215, Aug. 2010. https://doi.org/10.1007/s10340-010-0321-6
"Phthorimaea absoluta (tomato leafminer)," CABI. https://www.cabi.org/isc/datasheet/49260#tosummaryOfInvasiveness (accessed Sep. 30, 2021).
H. E. Z. Tonnang, S. F. Mohamed, F. Khamis, and S. Ekesi, "Identification and Risk Assessment for Worldwide Invasion and Spread of Tuta absoluta with a Focus on Sub-Saharan Africa: Implications for Phytosanitary Measures and Management," PLOS ONE, vol. 10, no. 8, 2015, Art. no. e0135283. https://doi.org/10.1371/journal.pone.0135283
V. Van Damme et al., "Overwintering potential of the invasive leafminer Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) as a pest in greenhouse tomato production in Western Europe," Journal of Pest Science, vol. 88, no. 3, pp. 533-541, Sep. 2015. https://doi.org/10.1007/s10340-014-0636-9
M. Chidege, S. Al-zaidi, N. Hassan, A. Julie, E. Kaaya, and S. Mrogoro, "First record of tomato leaf miner Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) in Tanzania," Agriculture & Food Security, vol. 5, no. 1, Aug. 2016, Art. no. 17. https://doi.org/10.1186/s40066-016-0066-4
T. J. Maginga, T. Nordey, and M. Ally, "Extension System for Improving the Management of Vegetable Cropping Systems," Journal of Information Systems Engineering and Management, vol. 3, no. 4, Nov. 2018, Art. no. 29. https://doi.org/10.20897/jisem/3940
D. I. Patricio and R. Rieder, "Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review," Computers and Electronics in Agriculture, vol. 153, pp. 69-81, Oct. 2018. https://doi.org/10.1016/j.compag.2018.08.001
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. https://doi.org/10.48084/etasr.2756
M. T. Linaza et al., "Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture," Agronomy, vol. 11, no. 6, Jun. 2021, Art. no. 1227. https://doi.org/10.3390/agronomy11061227
J. Amara, B. Bouaziz, and A. Algergawy, "A Deep Learning-based Approach for Banana Leaf Diseases Classification," in Datenbanksysteme fur Business, Technologie und Web, Gesellschaft für Informatik e.V., 2017, pp. 79-88.
J. G. A. Barbedo, "An Automatic Method to Detect and Measure Leaf Disease Symptoms Using Digital Image Processing," Plant Disease, vol. 98, no. 12, pp. 1709-1716, Dec. 2014. https://doi.org/10.1094/PDIS-03-14-0290-RE
M. Brahimi, K. Boukhalfa, and A. Moussaoui, "Deep Learning for Tomato Diseases: Classification and Symptoms Visualization," Applied Artificial Intelligence, vol. 31, no. 4, pp. 299-315, Apr. 2017. https://doi.org/10.1080/08839514.2017.1315516
A. Fuentes, S. Yoon, S. C. Kim, and D. S. Park, "A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition," Sensors, vol. 17, no. 9, Sep. 2017, Art. no. 2022. https://doi.org/10.3390/s17092022
L. Mkonyi et al., "Early identification of Tuta absoluta in tomato plants using deep learning," Scientific African, vol. 10, Nov. 2020, Art. no. e00590. https://doi.org/10.1016/j.sciaf.2020.e00590
S. P. Mohanty, D. P. Hughes, and M. Salathe, "Using Deep Learning for Image-Based Plant Disease Detection," Frontiers in Plant Science, vol. 7, 2016, Art. no. 1419. https://doi.org/10.3389/fpls.2016.01419
A. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg, and D. P. Hughes, "Deep Learning for Image-Based Cassava Disease Detection," Frontiers in Plant Science, vol. 8, 2017, Art. no. 1852. https://doi.org/10.3389/fpls.2017.01852
S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, "Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification," Computational Intelligence and Neuroscience, vol. 2016, Jun. 2016, Art. no. e3289801. https://doi.org/10.1155/2016/3289801
N. Petrellis, "A smart phone image processing application for plant disease diagnosis," in 6th International Conference on Modern Circuits and Systems Technologies, Thessaloniki, Greece, May 2017, pp. 1-4. https://doi.org/10.1109/MOCAST.2017.7937683
N. Petrellis, "Plant lesion characterization for disease recognition A Windows Phone application," in 2nd International Conference on Frontiers of Signal Processing, Warsaw, Poland, Oct. 2016, pp. 10-14. https://doi.org/10.1109/ICFSP.2016.7802948
N. Petrellis, "Mobile Application for Plant Disease Classification Based on Symptom Signatures," in 21st Pan-Hellenic Conference on Informatics, Larissa, Greece, Sep. 2017, pp. 1-6. https://doi.org/10.1145/3139367.3139368
N. Petrellis, "Plant Disease Diagnosis for Smart Phone Applications with Extensible Set of Diseases," Applied Sciences, vol. 9, no. 9, Jan. 2019, Art. no. 1952. https://doi.org/10.3390/app9091952
A. Johannes et al., "Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case," Computers and Electronics in Agriculture, vol. 138, pp. 200-209, Jun. 2017. https://doi.org/10.1016/j.compag.2017.04.013
A. Ramcharan et al., "A Mobile-Based Deep Learning Model for Cassava Disease Diagnosis," Frontiers in Plant Science, vol. 10, 2019, Art. no. 272. https://doi.org/10.3389/fpls.2019.00272
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. https://doi.org/10.48084/etasr.3452
Ranjith, S. Anas, I. Badhusha, O. T. Zaheema, K. Faseela, and M. Shelly, "Cloud based automated irrigation and plant leaf disease detection system using an android application," in International conference of Electronics, Communication and Aerospace Technology, Coimbatore, India, Apr. 2017, vol. 2, pp. 211-214. https://doi.org/10.1109/ICECA.2017.8212798
L. M. Mrisho et al., "Accuracy of a Smartphone-Based Object Detection Model, PlantVillage Nuru, in Identifying the Foliar Symptoms of the Viral Diseases of Cassava-CMD and CBSD," Frontiers in Plant Science, vol. 11, 2020, Art. no. 1964. https://doi.org/10.3389/fpls.2020.590889
S. Verma, A. Chug, A. P. Singh, S. Sharma, and P. Rajvanshi, "Deep Learning-Based Mobile Application for Plant Disease Diagnosis," in Applications of Image Processing and Soft Computing Systems in Agriculture, Hershey, PA, USA: IGI Global, 2019, pp. 242-271. https://doi.org/10.4018/978-1-5225-8027-0.ch010
D. P. Hughes and M. Salathe, "An open access repository of images on plant health to enable the development of mobile disease diagnostics," arXiv:1511.08060 [cs], Apr. 2016, Accessed: Sep. 30, 2021. [Online]. Available: http://arxiv.org/abs/1511.08060.
L. Loyani, "TutaSegmenter," Google Play Store. https://play.google.com/store/apps/details?id=org.tensorflow.lite.examples.tutasegmentation (accessed Sep. 30, 2021).
I. Arganda-Carreras et al., "Crowdsourcing the creation of image segmentation algorithms for connectomics," Frontiers in Neuroanatomy, vol. 9, 2015, Art. no. 142. https://doi.org/10.3389/fnana.2015.00142
O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," in International Conference on Medical image computing and computer-assisted intervention, Munich, Germany, Oct. 2015, pp. 234-241. https://doi.org/10.1007/978-3-319-24574-4_28
D. C. Ciresan, A. Giusti, L. M. Gambardella, and J. Schmidhuber, "Deep neural networks segment neuronal membranes in electron microscopy images," in 25th International Conference on Neural Information Processing Systems, Red Hook, NY, USA, Dec. 2012, pp. 2843-2851.
F. Chollet, "Introduction to Keras," in Deep Learning with Python, 1st ed., New York, NY, USA: Manning Publications Co., 2017, pp. 60-62.
M. Abadi, "TensorFlow: learning functions at scale," in 21st ACM SIGPLAN International Conference on Functional Programming, New York, NY, USA, Sep. 2016, Art. no. 1. https://doi.org/10.1145/2951913.2976746
L. K. Loyani, K. Bradshaw, and D. Machuve, "Segmentation of Tuta Absoluta's Damage on Tomato Plants: A Computer Vision Approach," Applied Artificial Intelligence, Sep. 2021. https://doi.org/10.1080/08839514.2021.1972254
D. P. Rubanga et al., A Deep Learning Dataset for Tomato Pest Leafminer TUTA ABSOLUTA. 2020.
B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman, "LabelMe: A Database and Web-Based Tool for Image Annotation," International Journal of Computer Vision, vol. 77, no. 1, pp. 157-173, May 2008. https://doi.org/10.1007/s11263-007-0090-8
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, "The Pascal Visual Object Classes (VOC) Challenge," International Journal of Computer Vision, vol. 88, no. 2, pp. 303-338, Jun. 2010. https://doi.org/10.1007/s11263-009-0275-4
D. P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," arXiv:1412.6980 [cs], Jan. 2017, Accessed: Sep. 30, 2021. [Online]. Available: http://arxiv.org/abs/1412.6980.
M. N. A. Khan, A. M. Mirza, R. A. Wagan, M. Shahid, and I. Saleem, "A Literature Review on Software Testing Techniques for Smartphone Applications," Engineering, Technology & Applied Science Research, vol. 10, no. 6, pp. 6578-6583, Dec. 2020. https://doi.org/10.48084/etasr.3844
"Mobile Operating System Market Share Africa," StatCounter Global Stats. https://gs.statcounter.com/os-market-share/mobile/africa (accessed Sep. 30, 2021).
How to Cite
MetricsAbstract Views: 540
PDF Downloads: 303
Copyright (c) 2021 L. Loyani, D. Machuve
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.