Efforts within the agricultural community to develop effective methods for early identification and classification of crop diseases have intensified, driven by a keen interest in harnessing advanced technologies. Key contributions have materialized in the realm of banana disease detection, such as the enhanced agro deep learning model proposed by Sangeetha et al. (2023) for Panama Wilt Disease detection in banana leaves (AgriEngineering, 5(2), 660–679) [1].
Challenges persist, including the absence of standardized datasets, hindering model reproducibility and comparison. Yuxuan Ba, Xuegang Lyu, Muqing Zhang, and Minzan Li (2023) extended these endeavors, offering insights into banana Fusarium wilt disease detection using supervised and unsupervised methods from UAV-based multispectral imagery (Remote Sensing, 14(5), 1231) [2]. Environmental variability affecting disease appearance poses a challenge to model accuracy and generalizability [2, 5, 12].
Seetharaman and Mahendran (2022) introduced a method for leaf disease detection in banana plants utilizing Gabor extraction and a region-based convolutional neural network (RCNN) (Journal of The Institution of Engineers (India): Series A, 103(2), 501–507) [3]. Challenges in their work include the need for robust model interpretation.
Soeb, M. J. A., et al. (2023) explored tea leaf disease detection and identification based on YOLOv7 (YOLO-T) (Scientific Reports, 13(1), 6078) [4]. Concurrently, Shadrach, F. D., et al. (2023) addressed optimal transfer learning-based nutrient deficiency classification in ridge gourd (Luffa acutangula) (Scientific Reports, 13(1), 14108) [5]. Challenges arise from ensuring model transferability to other crops and broadening applicability [4, 10]. Narayanan, K. L., Krishnan, R. S., Robinson, Y. H., Julie, E. G., Vimal, S., Saravanan, V., & Kaliappan, M. (2022) contributed significantly with their work on banana plant disease classification using a hybrid convolutional neural network (Computational Intelligence and Neuroscience, 2022) [6].
Challenges include generalizing the model to unseen data [6]. Anasta, N., Setyawan, F. X. A., & Fitriawan (2021) explored disease detection in banana trees using an image processing-based thermal camera (IOP Conference Series: Earth and Environmental Science, 739(1), 012088) [7]. Challenges involve multi-modal data integration and complexities in model interpretation [7].
Haque, M. A., et al. (2022) presented a deep learning-based approach for identifying diseases of maize crops (Scientific Reports, 12(1), 6334) [8]. Challenges include addressing resource-intensive processes in optimizing deep learning models [8]. Heath, M., St-Onge, D., & Hausler, R. (2023) investigated UV reflectance in crop remote sensing, focusing on assessing the current state of knowledge and extending research with strawberry cultivars (bioRxiv, 2023-05). This study contributes to the broader understanding of remote sensing applications in agriculture, albeit not specifically focusing on banana diseases detection[9].
Sarkar, C., Gupta, D., Gupta, U., & Hazarika, B. B. (2023) conducted a comprehensive review on leaf disease detection using machine learning and deep learning techniques, focusing on methodologies, challenges, and future directions (Applied Soft Computing, 110534). This review offers valuable insights into the broader landscape of disease detection in crops, which aligns with the efforts discussed in the literature review regarding banana disease detection[10]. Zhang, Q., Sun, Y., Jia, X., Wang, Z., & Guo, Q. (2020) surveyed deep learning applications in plant pathology (Computers and Electronics in Agriculture, 176, 105668) [11]. Challenges involve the diversity of plant pathology datasets and ensuring robust model generalization. Zhang, Y., Zhang, Q., & Zhou, H. (2022) focused on banana diseases detection using an improved YOLOv4 model based on deep learning (Computers and Electronics in Agriculture, 193, 106286) [12].
Challenges include algorithmic enhancements for efficient detection within resource constraints. Zheng, Y., Wu, J., Guo, Y., Wang, X., & Wang, S. (2021) presented the detection and classification of banana leaf diseases using transfer learning of a pre-trained convolutional neural network (Computers and Electronics in Agriculture, 181, 105950) [13]. Wang, X., Wu, J., Zhou, H., & Zhang, Y. (2020) utilized a deep convolutional neural network (CNN) to detect banana leaf diseases, making a significant contribution to precision agriculture. Their research showcases the feasibility of automated disease detection systems, aiding farmers in early diagnosis and intervention. This work emphasizes the integration of advanced technologies into agriculture for improved productivity and sustainability [14]. Wang, Z., Zhang, Q., Guo, Q., & Sun, Y. (2019) focused on identifying banana diseases through deep learning techniques, contributing to the development of agricultural technology. Their study underscores the potential of deep learning algorithms in accurately identifying diseases affecting banana crops. By harnessing the power of deep learning, this research addresses the need for efficient disease detection methods in agriculture, aligning with the broader trend of leveraging artificial intelligence in crop management practices [15].
Wang, X., Wu, J., Zhou, H., & Zhang, Y. (2020) focused on the detection of banana leaf diseases using a deep convolutional neural network (Computers and Electronics in Agriculture, 175, 105523) [18]. Mi, Y., Xiaofeng, Q., Hong, R., Changping, H., Zhang, Z., & Xin, L. proposed a method for the early diagnosis of Verticillium wilt in cotton based on chlorophyll fluorescence and hyperspectral technology. Their study contributes to the broader efforts in agricultural disease detection by leveraging advanced techniques to diagnose and manage Verticillium wilt in cotton crops. Although the focus is on cotton, the methodology and insights provided in their research could be applicable to similar challenges encountered in banana disease detection (Journal of Plant Diseases and Protection, forthcoming) [19]. Challenges involve the need for standardized Zhang, Q., Zhang, Y., Guo, Q., & Jia, X. (2019) explored banana diseases detection based on hyperspectral imaging and deep learning (Computers and Electronics in Agriculture, 165, 104961) [20]. Wang, Z., Zhang, Q., Guo, Q., & Sun, Y. (2019) contributed to the literature with their work on the identification of banana diseases based on deep learning (Computers and Electronics in Agriculture, 161, 282–289) [21]. Khan, A., Nawaz, U., Kshetrimayum, L., Seneviratne, L., & Hussain, I. (2023) introduced an innovative method called TomFormer for the early and accurate detection of tomato leaf diseases. Their approach, presented at the 2023 21st International Conference on Advanced Robotics (ICAR), leverages advanced robotics techniques to enhance disease detection in tomato crops. While the focus is on tomato leaf diseases, the methodology and technological advancements showcased in their research could inspire similar innovations in banana disease detection (IEEE, 2023, pp. 645–651) [16]. Challenges include ongoing refinements to feature fusion methods for improved accuracy. Rayhana, R., Ma, Z., Liu, Z., Xiao, G., Ruan, Y., & Sangha, J. S. (2023) conducted a comprehensive review on plant disease detection using hyperspectral imaging. Their review, published in IEEE Transactions on AgriFood Electronics, offers valuable insights into the utilization of hyperspectral imaging techniques for disease detection across various crops. Although the focus is on plant diseases in general, the methodologies and advancements discussed in their research could inform and inspire similar approaches in banana disease detection (IEEE, 2023) [17].