Digital image processing techniques for detection of pests and diseases in crops: a review

https://doi.org/10.25100/iyc.v24i1.10973
Agriculture Crop Disease Image Processing Plague

Article Sidebar

PDF XML (Español)

Published: Jan 15, 2022
Issue: Vol. 24 No. 1 (2022): Ingenieria y Competitividad
Section Review

Publcation metrics

3309 | 1256 | 108




 


Main Article Content

Authors

  July Gómez-Camperos Universidad Francisco de Paula Santander Ocaña, Facultad de Ingenierías, Grupo de investigación GINSTI y GITYD, Ocaña, Colombia
  Haidee Jaramillo Universidad Francisco de Paula Santander Ocaña, Facultad de Ingenierías, Grupo de investigación GINSTI y GITYD, Ocaña, Colombia
  Gustavo Guerrero-Gómez Universidad Francisco de Paula Santander Ocaña, Facultad de Ingenierías, Grupo de investigación GINSTI y GITYD, Ocaña, Colombia
Abstract

Proper detection of pests and diseases in crop production is essential to increase agricultural production in a sustainable way. For this reason, the term Agriculture 4.0 is incorporated, which integrates a set of technologies, devices, protocols, and computational paradigms to improve agricultural processes. Information on climatic conditions, soils, diseases, insects, seeds, fertilizers. It constitutes an essential contribution to the economic and sustainable development of this sector. Digital image processing techniques are a tool that allows early identification of pests or diseases in crops such as cereals, fruit trees, roots, leaves, and tubers mainly. In this way, mitigate economic losses in the agricultural sector. Globally, about 40% of crops are discarded by various diseases and pests. In most cases, crop diseases produce visible symptoms and characteristics during plant growth. Due to the scarcity of technologies used in crops, the diagnosis of diseases and pests is supported mainly by human inspection, generating errors caused by the subjectivity of individuals.


This literature review was carried out to identify different digital image processing techniques for pests and disease prevention in crops from different agricultural sectors. The results showed that the diagnostic system is composed of the acquisition of images, pre-image processing, segmentation, characteristics extraction, characteristics selection, and the subsequent classification of pests or diseases. Likewise, current trends and challenges on the subject are presented.

How to Cite
1.
Gómez-Camperos J, Jaramillo H, Guerrero-Gómez G. Digital image processing techniques for detection of pests and diseases in crops: a review. inycomp [Internet]. 2022 Jan. 15 [cited 2024 Nov. 18];24(1). Available from: https://revistaingenieria.univalle.edu.co/index.php/ingenieria_y_competitividad/article/view/10973
References

(1) Rahul MSP, Rajesh M. Image processing based Automatic Plant Disease Detection and Stem Cutting Robot. In: Third International Conference on Smart Systems and Inventive Technology (ICSSIT). Tirunelveli: IEEE; 2020.p.889–94.https://doi.org/10.1109/ICSSIT48917.2020.9214257.

(2) Burhan SA, Minhas S, Tariq A, Hassan NB. Estudio comparativo de algoritmos de aprendizaje profundo para la detección de enfermedades y plagas en cultivos de arroz. In: 12a Conferencia Internacional sobre Electrónica, Computadoras e Inteligencia Artificial. Bucharest. IEEE; 2020. p.1-5. https://doi.org/10.1109/ECAI50035.2020.9223239.

(3) Johannes A, Picón A, Álvarez-Gila A, Echazarra J, Rodriguez-Vaamonde S, Navajas AD, et al. Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Comput Electron Agric. 2017;138:200–9. https://doi.org/10.1016/j.compag.2017.04.013.

(4) Patil R, Kumar S. Bibliometric Survey on Diagnosis of Plant Leaf Diseases Using Artificial Intelligence. Int. J. Mod. Agric. 2020 Sep; 9(3):1111–31. Available on: http://modern-journals.com/index.php/ijma/article/view/316.

(5) Yigit E, Sabanci K, Toktas A, Kayabasi A. A study on visual features of leaves in plant identification using artificial intelligence techniques. Computers and Electronics in Agriculture. 2019; 156:369-77. https://doi.org/10.1016/j.compag.2018.11.036.

(6) Tavakoli H, Alirezazadeh P, Hedayatipour A, Banijamali N, Landwehr A.H. Leaf image-based classification of some common bean cultivars using discriminative convolutional neural networks. Computers and Electronics in Agriculture. 2021;181:105935. https://doi.org/10.1016/j.compag.2020.105935.

(7) Aakif A, Faisal M. Automatic classification of plants based on their leaves. Biosystems Engineering. 2015;139:66-75. https://doi.org/10.1016/j.biosystemseng.2015.08.003.

(8) Chaki J, Parekh J, Bhattacharya S. Plant leaf classification using multiple descriptors: A hierarchical approach. Journal of King Saud University - Computer and Information Sciences. 2020;32(10):1158-72. https://doi.org/10.1016/j.jksuci.2018.01.007.

(9) Horaisová K, Kukal J. Leaf classification from binary image via artificial intelligence. Biosystems Engineering. 2016;142:83-100. https://doi.org/10.1016/j.biosystemseng.2015.12.007.

(10) Wang B, Gao Y, Yuan X, Xiong S, Feng X. From species to cultivar: Soybean cultivar recognition using joint leaf image patterns by multiscale sliding chord matching. Biosystems Engineering. 2020;194:99-111. https://doi.org/10.1016/j.biosystemseng.2020.03.019.

(11) Huijser, MP, Camel, W, Hardy, A. Reliability of the animal detection system along US Hwy 191 in Yellowstone National Park, Montana, USA. In: Proceedings of the Int. Conf. on Ecology and Transportation. (CEOT). USA; 2005. p. 509-523. Available on: https://escholarship.org/uc/item/6cg9f98f.

(12) Eli-Chukwu, NC. Applications of artificial intelligence in agriculture: A review. Engineering, Technology & Applied Science Research. 2019;9(4):4377-83. https://doi.org/10.48084/etasr.2756

(13) Bannerjee G, Sarkar U, Das S, Ghosh I. Inteligencia artificial en la agricultura: un estudio de la literatura. Revista Internacional de Investigación Científica en Aplicaciones de Ciencias de la Computación y Estudios de Gestión. 2018;7(3):1-6.

(14) Huang W, Lamb DW, Niu Z, Zhang Y, Lui L, Wang J. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agric. 2007;8:187–97. https://doi.org/10.1007/s11119-007-9038-9.

(15) Barman U, Choudhury RD, Sahu D, Barman GG. Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease. Computers and Electronics in Agriculture. 2020;177:105661. https://doi.org/10.1016/j.compag.2020.105661.

(16) Zhang B, Huang W, Li J, Zhao C, Fan S, Wu J. Chengliang Liu, Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Research International. 2014;62:326-43.: https://doi.org/10.1016/j.foodres.2014.03.012.

(17) Huili T, Jiyao Y, Lianqing Z, Zhou S. Agriculture disease diagnosis expert system based on knowledge and fuzzy reasoning: A case study of flower. In: 6th Int. Conf. Fuzzy Syst. Knowl. Discov. (FSKD). China; IEEE; 2009. p. 39–43. https://doi.org/10.1109/FSKD.2009.512.

(18) Zhou Z, Zang Y, Li Y, Zhang Y, Wang P, Luo X. Rice plant-hopper infestation detection and classification algorithms based on fractal dimension values and fuzzy C-means. Mathematical and Computer Modelling. 2013;58(3–4):701-09. https://doi.org/10.1016/j.mcm.2011.10.028.

(19) Gomez S, Vergara M, Montenegro A, Ruiz FA, Safari H, Raymaekers N, et al. Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin. ISPRS Journal of Photogrammetry and Remote Sensing. 2020;169:110-24. https://doi.org/10.1016/j.isprsjprs.2020.08.025.

(20) Romero MP, Chang YM, Brunton LA, Parry J, Prosser A, Upton A. et al. Decision tree machine learning applied to bovine tuberculosis risk factors to aid disease control decision making. Preventive Veterinary Medicine. 2020;175:104860. https://doi.org/10.1016/j.prevetmed.2019.104860.

(21) Ozyilmaz U. Evaluation of the effectiveness of antagonistic bacteria against Phytophthora blight disease in pepper with artificial intelligence. Biological Control. 2020;151:104379. https://doi.org/10.1016/j.biocontrol.2020.104379.

(22) Abdulridha J, Ampatzidis Y, Roberts P, Kakarla S. Detecting powdery mildew disease in squash at different stages using UAV-based hyperspectral imaging and artificial intelligence. Biosystems Engineering. 2020;197:135-48. https://doi.org/10.1016/j.biosystemseng.2020.07.001.

(23) Jha K, Doshi A, Patel P, Shah M. A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture. 2019;2:1-12. https://doi.org/10.1016/j.aiia.2019.05.004.

(24) Mishra S, Sachan R, Rajpal D. Deep Convolutional Neural Network based Detection System for Real-time Corn Plant Disease Recognition. Procedia Computer Science. 2020;167:2003-10. https://doi.org/10.1016/j.procs.2020.03.236.

(25) Geetharamani G, Pandian JA. Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers & Electrical Engineering,. 2019;76:323-38, https://doi.org/10.1016/j.compeleceng.2019.04.011.

(26) Deeba, K. Amutha, B. ResNet - deep neural network architecture for leaf disease classification. Microprocessors and Microsystems. 2020;(InPress):103364. https://doi.org/10.1016/j.micpro.2020.103364.

(27) Toseef M, Khan MJ. An intelligent mobile application for diagnosis of crop diseases in Pakistan using fuzzy inference system. Computers and Electronics in Agriculture. 2018;153:1-11. https://doi.org/10.1016/j.compag.2018.07.034.

(28) Kolhe S, Kamal R, Harvinder, Saini HS, Gupta GK. A web-based intelligent disease-diagnosis system using a new fuzzy-logic based approach for drawing the inferences in crops. Computers and Electronics in Agriculture. 2011;76(1):16-27. https://doi.org/10.1016/j.compag.2011.01.002.

(29) Picon A, Seitz M, Alvarez-Gila A, Mohnke P, Ortiz-Barredo A, Echazarra J. Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions. Computers and Electronics in Agriculture. 2019;167:105093. https://doi.org/10.1016/j.compag.2019.105093.

(30) Afonso M, Blok PM, Polder G, Van der Wolf JM, Kamp J. Blackleg Detection in Potato Plants using Convolutional Neural Networks. IFAC-PapersOnLine. 2019;52(30):6-11. https://doi.org/10.1016/j.ifacol.2019.12.481.

(31) Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S. ToLeD: Tomato Leaf Disease Detection using Convolution Neural Network. Procedia Computer Science. 2020;167:293-301. https://doi.org/10.1016/j.procs.2020.03.225.

(32) Devaraj A, Rathan K, Jaahnavi S, Indira K. Identification of plant disease using image processing technique. In: Proc. Int. Conf. Commun. Signal Process. (ICCSP). China: IEEE; 2019. p. 749–53. https://doi.org/10.1109/ICCSP.2019.8698056.

(33) Francis J, Sahaya A, Dhas D, Anoop BK. Identification of leaf diseases in pepper plants using soft computing techniques. In: 2016 Conference on Emerging Devices and Smart Systems (ICEDSS). India: IEEE; 2016. p. 168-73. https://doi.org/10.1109/ICEDSS.2016.7587787.

(34) Phadikar S, Sil J. Rice disease identification using pattern recognition techniques. In: Proc. 11th Int. Conf. Comput. Inf. Technol. (ICCIT). USA: IEEE; 2008. p. 420–23. https://doi.org/10.1109/ICCITECHN.2008.4803079.

(35) Raiu, R. Subburaman, A. Haribabu, S. Shrub Ailment Recognization Using Advanced Image Processing. In: 7th Int. Conf. Comput. Power, Energy, Inf. Commun. (ICCPEIC). Melmaruvathur: IEEE; 2018. p. 215–20. https://doi.org/10.1109/ICCPEIC.2018.8525159.

(36) Ikhwan AN, Kamal M. Digital image processing technique for palm oil leaf disease detection using multiclass SVM classifier. In: Int. Conf. Smart Instrumentation, Meas. Appl. (ICSIMA). Malasya: IEEE; 2017. p. 1–6. https://doi.org/10.1109/ICSIMA.2017.8311978.

(37) Prajapati BS, Dabhi VK, Prajapati HB. A survey on detection and classification of cotton leaf diseases. In: Int. Conf. Electr. Electron. Optim. Tech. (ICEEOT). India: IEEE; 2016. p. 2499–06. https://doi.org/10.1109/ICEEOT.2016.7755143.

(38) Jiao L, Dong S, Zhang S, Xie C, Wang H. AF-RCNN: An anchor-free convolutional neural network for multi-categories agricultural pest detection. Computers and Electronics in Agriculture. 2020;174:105522. https://doi.org/10.1016/j.compag.2020.105522.

(39) Kantale p, Thakare S. A Review on Pomegranate Disease Classification Using Machine Learning and Image Segmentation Techniques. In: Proc. Int. Conf. Intell. Comput. Control Syst. (ICICCS). India: IEEE; 2020. p. 455–60. https://doi.org/10.1109/ICICCS48265.2020.9121161.

(40) Thenmozhi K, Srinivasulu U. Image processing techniques for insect shape detection in field crops. In: Proc. Int. Conf. Inven. Comput. Informatics (ICICI). India: IEEE; 2017. p. 699–04. https://doi.org/10.1109/ICICI.2017.8365226.

(41) Li Y, Wang H, Dang LM, Sadeghi-Niaraki M, Moon H. Crop pest recognition in natural scenes using convolutional neural networks. Computers and Electronics in Agriculture. 2020;169:105174. https://doi.org/10.1016/j.compag.2019.105174.

(42) Preetha, B, Radhakrishnan L, Suresh P. Detection and classification of pests from crop images using Support Vector Machine. In: Proc. Int. Conf. Emerg. Technol. Trends Comput. Commun. Electr. Eng. (ICETT). India: IEEE; 2016. p. 99–04. https://doi.org/10.1109/ICETT.2016.7873750.

(43) Al-Amin M, Dewan Ziaul K, Tasfia Anika B. Prediction of rice disease from leaves using deep convolution neural network towards a digital agricultural system. In: 22nd Int. Conf. Comput. Inf. Technol. (ICCIT). Bangladesh: IEEE; 2019. p. 18–20. https://doi.org/10.1109/ICCIT48885.2019.9038229.

(44) Patil JK, Kumar R. Feature extraction of diseased leaf images. Journal of Signal and Image Processing. 2012;3(1):65–71. Available on: https://bioinfopublication.org/files/articles/3_1_1_JSIP.pdf.

(45) Xiong Y, Liang L, Wang L, She J, Wu M. Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset. Computers and Electronics in Agriculture. 2020;177:105712. https://doi.org/10.1016/j.compag.2020.105712.

(46) Roldán-Serrato KL, Escalante-Estrada JAS, Rodríguez-González MT. Automatic pest detection on bean and potato crops by applying neural classifiers. Engineering in Agriculture, Environment and Food. 2018;11(4):245-55. https://doi.org/10.1016/j.eaef.2018.08.003.

(47) Khan MA, Akram T, Sharif M, Awais M, Javed K, Ali H, Saba T. CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features. Computers and Electronics in Agriculture. 2018;155:220-36. https://doi.org/10.1016/j.compag.2018.10.013.

(48) Wan-Soo K, Dae-Hyun L, Yong-Joo K. Machine vision-based automatic disease symptom detection of onion downy mildew. Computers and Electronics in Agriculture. 2020;168:105099. https://doi.org/10.1016/j.compag.2019.105099.

(49) Konstantinos PF. Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture. 2018;145:311-18. https://doi.org/10.1016/j.compag.2018.01.009.

(50) Nanni L, Maguolo G, Pancino F. Insect pest image detection and recognition based on bio-inspired methods. Ecological Informatics. 2020;57:101089. https://doi.org/10.1016/j.ecoinf.2020.101089.

(51) 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. Computers and Electronics in Agriculture. 2015;119:123-32. https://doi.org/10.1016/j.compag.2015.10.015.

(52) Cheng X, Zhang Y, Chen Y, Wu Y, Yue Y. Pest identification via deep residual learning in complex background. Computers and Electronics in Agriculture. 2017;141:351-56.https://doi.org/10.1016/j.compag.2017.08.005.

(53) Roldan-Serrato L, Tetyana Kussul B, Escalante-Estrada A, Gonzales Rodriguez M. Recognition of pests on crops with a random subspace classifier. In: Int. Work Conf. Bio-Inspired Intell. Intell. Syst. Biodivers. Conserv. Proc. (IWOBI). USA: IEEE; 2015. p. 21–26. https://doi.org/10.1109/IWOBI.2015.7160138.

(54) Bollis E, Pedrini E, Avila S. Weakly supervised learning guided by activation mapping applied to a novel citrus pest benchmark. In: Conf. on Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (CVPRW). USA: IEEE; 2020. P. 310–19. https://doi.org/10.1109/CVPRW50498.2020.00043.

(55) Castelão E, Brandoli B, Astolfi G, Alessandro N, Paraguassu W, Railda A. et. al. Detection and classification of soybean pests using deep learning with UAV images. Computers and Electronics in Agriculture. 2020;179:105836. https://doi.org/10.1016/j.compag.2020.105836.

(56) Ebrahimi MA, Khoshtaghaza MH, Minaei S, Jamshidi B. Vision-based pest detection based on SVM classification method. Computers and Electronics in Agriculture. 2017;137:52-58. https://doi.org/10.1016/j.compag.2017.03.016.

(57) Zhang J, Huang Y, Pu R, Gonzalez-Moreno P, Yuan L, Wu K. et al. Monitoring plant diseases and pests through remote sensing technology: A review. Computers and Electronics in Agriculture. 2019;165:104943. https://doi.org/10.1016/j.compag.2019.104943.

(58) Sankaran S, Mishra A, Ehsani R, Davis C. A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture. 2010;72(1):1-13. https://doi.org/10.1016/j.compag.2010.02.007.

(59) Bürling K, Hunsche M, Noga G. Use of blue–green and chlorophyll fluorescence measurements for differentiation between nitrogen deficiency and pathogen infection in winter wheat. Journal of Plant Physiology. 2011;168(14):1641-48. https://doi.org/10.1016/j.jplph.2011.03.016.

(60) Zhenchuan Y, Congshun W, Guizhen Y, Yilong H, Guoying W. A bulk micromachined lateral axis gyroscope with vertical sensing comb capacitors. In: 13th International Conference on Solid State Sensors Actuators Microsystems, 2005, Digest of Technical Papers. TRANSDUCERS ‘05. 2005;1121-24. Available on: https://doi.org/10.1109/SENSOR.2005.1496374.

(61) Barbedo J, Tibola C, Fernandes J. Detecting Fusarium head blight in wheat kernels using hyperspectral imaging. Biosystems Engineering. 2015;131:65-76. https://doi.org/10.1016/j.biosystemseng.2015.01.003.

(62) Galed G, Fernández-Valle ME, Martı́nez A, Heras A. Application of MRI to monitor the process of ripening and decay in citrus treated with chitosan solutions. Magnetic Resonance Imaging. 2004;22(1):127-37. https://doi.org/10.1016/j.mri.2003.05.006.

Received 2021-01-30
Accepted 2021-05-05
Published 2022-01-15
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Authors grant the journal and Universidad del Valle the economic rights over accepted manuscripts, but may make any reuse they deem appropriate for professional, educational, academic or scientific reasons, in accordance with the terms of the license granted by the journal to all its articles.

Articles will be published under the Creative Commons 4.0 BY-NC-SA licence (Attribution-NonCommercial-ShareAlike).