Guava Disease Detection and Classification: A Systematic Literature Review

Muhammad Bayu Kurniawan, Ema Utami

Abstract


Guavas (Psidium guajava) are nutrient-rich fruits that provide significant health benefits. However, guava cultivation faces persistent threats from various diseases affecting both leaves and fruits, leading to substantial yield and quality losses. The early and accurate detection of these diseases is crucial but remains challenging due to economic constraints and limited infrastructure. While plant pathologists employ various diagnostic methods, these approaches are often time-consuming, costly, and sometimes inconsistent. Recent advancements in deep learning (DL) and machine learning (ML) have introduced innovative techniques for guava disease identification. This study conducts a Systematic Literature Review (SLR) to evaluate the existing research on guava leaf and fruit disease detection, focusing on dataset sources, identified disease categories, preprocessing and augmentation techniques, applied algorithms, and reported evaluation metrics. A comprehensive search was conducted across multiple databases, covering publications from 2017 to 2023, leading to the identification of 47 relevant studies. After applying exclusion criteria, 16 studies were selected for in-depth analysis. The findings highlight the most commonly used datasets, the predominant classification techniques, and the effectiveness of various deep learning models based on multiple performance metrics, providing insights into current research trends, existing limitations, and potential directions for future studies. This review serves as a valuable reference for researchers aiming to enhance the accuracy and efficiency of guava leaf and fruit disease diagnosis through data-driven approaches.

Keywords


Machine learning; Deep learning; Detection; Classification; Guava diseases

Full Text:

Link Download

References


Abbasi, Rabiya, Pablo Martinez, and Rafiq Ahmad. 2022. “The Digitization of Agricultural Industry – a Systematic Literature Review on Agriculture 4.0.” Smart Agricultural Technology 2(February):100042. doi: 10.1016/j.atech.2022.100042.

Akhtar, Mushir, M. Tanveer, and Mohd Arshad. 2024. “Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approach.” Pattern Recognition 155(November 2023). doi: 10.1016/j.patcog.2024.110637.

Almadhor, Ahmad, Hafiz Tayyab Rauf, Muhammad Ikram Ullah Lali, Robertas Damaševičius, Bader Alouffi, and Abdullah Alharbi. 2021. “Ai-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from Dslr Camera Sensor Based High Resolution Imagery.” Sensors 21(11):1–19. doi: 10.3390/s21113830.

Almutiry, Omar, Muhammad Ayaz, Tariq Sadad, Ikram Ullah Lali, Awais Mahmood, Najam Ul Hassan, and Habib Dhahri. 2021. “A Novel Framework for Multi-Classification of Guava Disease.” Computers, Materials and Continua 69(2):1915–26. doi: 10.32604/cmc.2021.017702.

Angulo-López, Jorge E., Adriana C. Flores-Gallegos, Cristian Torres-León, Karen N. Ramírez-Guzmán, Gloria A. Martínez, and Cristóbal N. Aguilar. 2021. “Guava (Psidium Guajava l.) Fruit and Valorization of Industrialization by-Products.” Processes 9(6):1–17. doi: 10.3390/pr9061075.

Ashurov, Asadulla Y., Mehdhar S. A. M. Al-Gaashani, Nagwan A. Samee, Reem Alkanhel, Ghada Atteia, Hanaa A. Abdallah, and Mohammed Saleh Ali Muthanna. 2024. “Enhancing Plant Disease Detection through Deep Learning: A Depthwise CNN with Squeeze and Excitation Integration and Residual Skip Connections.” Frontiers in Plant Science 15(January):1–16. doi: 10.3389/fpls.2024.1505857.

Chiou, Kuo-dung, Yen-xue Chen, Po-sung Chen, Ying-tzy Jou, and Shang-han Tsai. 2025. “Application of Deep Learning for Fruit Defect Recognition in Psidium Guajava L.” Scientific Reports 15. doi: 10.1038/s41598-025-88936-y.

Doutoum, Assad S., Recep Eryigit, and Bulent Tugrul. 2023. “Classification of Guava Leaf Disease Using Deep Learning.” WSEAS TRANSACTIONS ON INFORMATION SCIENCE AND APPLICATIONS 20(October):356–63. doi: 10.37394/23209.2023.20.38.

El-Hasnony, Ibrahim M., Omar M. Elzeki, Ali Alshehri, and Hanaa Salem. 2022. “Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction.” Sensors 22(3). doi: 10.3390/s22031184.

Farhan Al Haque, A. S. M., Rubaiya Hafiz, Md Azizul Hakim, and G. M. Rasiqul Islam. 2019. “A Computer Vision System for Guava Disease Detection and Recommend Curative Solution Using Deep Learning Approach.” 2019 22nd International Conference on Computer and Information Technology, ICCIT 2019 (December). doi: 10.1109/ICCIT48885.2019.9038598.

Gaikwad, Sukanya S., Shivanand S. Rumma, and Mallikarjun Hangarge. 2021a. Classification of Fungi Effected Psidium Guajava Leaves Using ML and DL Techniques. In Computer Vision and Machine Intelligence Paradigms for SDGs. Vol. 967. edited by R. J. Kannan, S. M. Thampi, and S.-H. Wang.

Gaikwad, Sukanya S., Shivanand S. Rumma, and Mallikarjun Hangarge. 2021b. “Identification of Fungi Infected Leaf Diseases Using Deep Learning Techniques.” Turkish Journal of Computer and Mathematics Education (TURCOMAT) 6(12):5618–25.

Howlader, Md Rasel, Umme Habiba, Rahat Hossain Faisal, and Md Mostafijur Rahman. 2019. “Automatic Recognition of Guava Leaf Diseases Using Deep Convolution Neural Network.” 2nd International Conference on Electrical, Computer and Communication Engineering, ECCE 2019 1–5. doi: 10.1109/ECACE.2019.8679421.

Joseph, Diana Susan, Pranav M. Pawar, and Kaustubh Chakradeo. 2024. “Real-Time Plant Disease Dataset Development and Detection of Plant Disease Using Deep Learning.” IEEE Access 12(January):16310–33. doi: 10.1109/ACCESS.2024.3358333.

Liang, Wen, Youzhi Liang, and Jianguo Jia. 2023. “MiAMix: Enhancing Image Classification through a Multi-Stage Augmented Mixed Sample Data Augmentation Method.” Processes 11(12):1–19. doi: 10.3390/pr11123284.

MacNish, Tessa R., Monica F. Danilevicz, Philipp E. Bayer, Mitchell S. Bestry, and David Edwards. 2025. “Application of Machine Learning and Genomics for Orphan Crop Improvement.” Nature Communications 16(1):982. doi: 10.1038/s41467-025-56330-x.

Maldonado-Canca, Luis Alfonso, Ana María Casado-Molina, Juan Pedro Cabrera-Sánchez, and Guillermo Bermúdez-González. 2024. “Beyond the Post: An SLR of Enterprise Artificial Intelligence in Social Media.” Social Network Analysis and Mining 14(1). doi: 10.1007/s13278-024-01382-y.

Meng, Yu, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, and Jiawei Han. 2020. “Text Classification Using Label Names Only: A Language Model Self-Training Approach.” EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference 9006–17. doi: 10.18653/v1/2020.emnlp-main.724.

Mohamed Shaffril, Hayrol Azril, Samsul Farid Samsuddin, and Asnarulkhadi Abu Samah. 2021. “The ABC of Systematic Literature Review: The Basic Methodological Guidance for Beginners.” Quality and Quantity 55(4):1319–46. doi: 10.1007/s11135-020-01059-6.

Mostafa, Almetwally M., Swarn Avinash Kumar, Talha Meraj, Hafiz Tayyab Rauf, Abeer Ali Alnuaim, and Maram Abdullah Alkhayyal. 2022. “Guava Disease Detection Using Deep Convolutional Neural Networks: A Case Study of Guava Plants.” Applied Sciences (Switzerland) 12(1). doi: 10.3390/app12010239.

Moupojou, Emmanuel, Appolinaire Tagne, Florent Retraint, Anicet Tadonkemwa, Dongmo Wilfried, Hyppolite Tapamo, and Marcellin Nkenlifack. 2023. “FieldPlant: A Dataset of Field Plant Images for Plant Disease Detection and Classification With Deep Learning.” IEEE Access 11(March):35398–410. doi: 10.1109/ACCESS.2023.3263042.

Muhammad, Abdulhamid, Supavadee Aramvith, Khanita Duangchaemkarn, and Ming Ting Sun. 2024. “Brain MRI Image Super-Resolution Reconstruction: A Systematic Review.” IEEE Access 12(November):156347–62. doi: 10.1109/ACCESS.2024.3478829.

Mumtaz, Sidrah, Mudassar Raza, Ofonime Dominic Okon, Saeed Ur Rehman, Adham E. Ragab, and Hafiz Tayyab Rauf. 2023. “A Hybrid Framework for Detection and Analysis of Leaf Blight Using Guava Leaves Imaging.” Agriculture (Switzerland) 13(3). doi: 10.3390/agriculture13030667.

Mustak Un Nobi, Md., Md. Rifat, M. F. Mridha, Sultan Alfarhood, Mejdl Safran, and Dunren Che. 2023. “GLD-Det: Guava Leaf Disease Detection in Real-Time Using Lightweight Deep Learning Approach Based on MobileNet.” Agronomy 13(9):2240. doi: 10.3390/agronomy13092240.

Nandi, Rabindra Nath, Aminul Haque Palash, Nazmul Siddique, and Mohammed Golam Zilani. 2023. “Device-Friendly Guava Fruit and Leaf Disease Detection Using Deep Learning.” Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST 490 LNICST:49–59. doi: 10.1007/978-3-031-34619-4_5.

Ojo, Mike O., and Azlan Zahid. 2023. “Improving Deep Learning Classifiers Performance via Preprocessing and Class Imbalance Approaches in a Plant Disease Detection Pipeline.” Agronomy 13(3). doi: 10.3390/agronomy13030887.

Orchi, Houda, Mohamed Sadik, and Mohammed Khaldoun. 2022. “On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey.” Agriculture (Switzerland) 12(1). doi: 10.3390/agriculture12010009.

Perumal, P., Kandasamy Sellamuthu, K. Vanitha, and V. K. Manavalasundaram D A Professor. 2021. “Guava Leaf Disease Classification Using Support Vector Machine.” Turkish Journal of Computer and Mathematics Education 12(7):1177–83.

Ramadhan, Nur Ghaniaviyanto, Adiwijaya, Warih Maharani, and Alfian Akbar Gozali. 2024. “Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review.” IEEE Access 12(March):80698–730. doi: 10.1109/ACCESS.2024.3406748.

Rashid, Javed, Imran Khan, Ghulam Ali, Shafiq ur Rehman, Fahad Alturise, and Tamim Alkhalifah. 2023. “Real-Time Multiple Guava Leaf Disease Detection from a Single Leaf Using Hybrid Deep Learning Technique.” Computers, Materials and Continua 74(1):1235–57. doi: 10.32604/cmc.2023.032005.

Reda, Oumaima, Naoual Chaouni Benabdellah, and Ahmed Zellou. 2023. “A Systematic Literature Review on Data Quality Assessment.” Bulletin of Electrical Engineering and Informatics 12(6):3736–57. doi: 10.11591/eei.v12i6.5667.

S.Abirami, M. Thilagavathi. 2017. “Application of Image Processing in Diagnosing Guava Leaf Diseases.” International Journal of Scientific Research and Management (IJSRM) 5(7):5927–33. doi: 10.18535/ijsrm/v5i7.19.

Sarki, Rubina, Khandakar Ahmed, Hua Wang, Yanchun Zhang, Jiangang Ma, and Kate Wang. 2021. “Image Preprocessing in Classification and Identification of Diabetic Eye Diseases.” Data Science and Engineering 6(4):455–71. doi: 10.1007/s41019-021-00167-z.

Shakil, Rashiduzzaman, Bonna Akter, Aditya Rajbongshi, Umme Sara, Mala Rani Barman, and Aditi Dhali. 2023. “A Transfer Learning Approach to the Development of an Automation System for Recognizing Guava Disease Using CNN Models for Feasible Fruit Production.” Lecture Notes in Networks and Systems 647 LNNS:127–41. doi: 10.1007/978-3-031-27409-1_12.

Shihab, Montasir Rahman, Nafiu Islam Saim, Mayen Uddin Mojumdar, Dewan Mamun Raza, Shah Md Tanvir Siddiquee, Sheak Rashed Haider Noori, and Narayan Ranjan Chakraborty. 2025. “Image Dataset for Classification of Diseases in Guava Fruits and Leaves.” Data in Brief 59:111378. doi: 10.1016/j.dib.2025.111378.

Srinivas, B., P. Satheesh, P. Rama Santosh Naidu, and U. Neelima. 2021. Prediction of Guava Plant Diseases Using Deep Learning. Vol. 698. Springer Singapore.

Upadhyay, Abhishek, Narendra Singh Chandel, Krishna Pratap Singh, Subir Kumar Chakraborty, Balaji M. Nandede, Mohit Kumar, A. Subeesh, Konga Upendar, Ali Salem, and Ahmed Elbeltagi. 2025. “Deep Learning and Computer Vision in Plant Disease Detection: A Comprehensive Review of Techniques, Models, and Trends in Precision Agriculture.” Artificial Intelligence Review 58(3). doi: 10.1007/s10462-024-11100-x.

Valero-Carreras, Daniel, Javier Alcaraz, and Mercedes Landete. 2023. “Comparing Two SVM Models through Different Metrics Based on the Confusion Matrix.” Computers and Operations Research 152(April 2022):106131. doi: 10.1016/j.cor.2022.106131.

Vitti, Karine Alessandra, Lilian Maluf de Lima, and João Gomes Martines Filho. 2020. “Agricultural and Economic Characterization of Guava Production in Brazil.” Revista Brasileira de Fruticultura 42(1):1–11. doi: 10.1590/0100-29452020447.

Xuejie Hao, Lu Liu, Rongjin Yang, Lizeyan Yin, Le Zhang and Xiuhong Li. 2023. “A Review of Data Augmentation Methods of Remote Sensing Image Target Recognition.” Remote Sensing. doi: https://doi.org/10.3390/rs15030827.




DOI: http://dx.doi.org/10.35671/telematika.v18i1.2901

Refbacks

  • There are currently no refbacks.


 



Indexed by:

 

Telematika
ISSN: 2442-4528 (online) | ISSN: 1979-925X (print)
Published by : Universitas Amikom Purwokerto
Jl. Let. Jend. POL SUMARTO Watumas, Purwonegoro - Purwokerto, Indonesia


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