When conducting a systematic literature review, having research questions (RQs) helps set the context of the research and guides how data is collected and analyzed to answer the investigation [8][10]. Thus, a systematic literature review is viewed as a process of aggregating knowledge from empirical studies on a particular topic to address research questions [11]. To objectively achieve the research aim, the research seeks to investigate the following research questions:

RQ1: What AI architectures are used for NCLB disease detection using drone imagery?

RQ2: What data preprocessing methods were used to achieve high performance metrics?

RQ3: What AI model architectural techniques are applied to mitigate occlusion and small object detection problems?

A search strategy is essential when conducting a systematic literature review because it ensures that relevant empirical studies of interest are identified. To identify relevant primary sources of primary interest, the study adopts a database search strategy on online research publication databases. The study utilized Google Scholar as the chosen database to perform a Boolean search string because it is a widely used free indexing tool in the field of computer engineering [11] due to its ability to index top journal publication databases such as IEEE Xplore, ScienceDirect, Wiley, MDPI, and other databases like ResearchGate. The Google Scholar search engine maximizes the retrieval density and ensures strict reproducibility since Google Scholar indexes over 90% of elite academic literature, effectively acting as a superset that encompasses records from various databases including top publication databases such as IEEE Xplore, Wiley, MDPI, and ScienceDirect. This study intentionally relies on a singular search engine because automated multi-database queries often introduce irreproducibility due to different Boolean syntax execution and fluctuating institutional subscription walls across indexing services. Furthermore, Google Scholar’s natural language processing and citation framework have the ability to index grey literature, preprints and regional institutional repositories, which ensures that highly specific emerging primary studies on UAV imagery based NCLB detection using AI are not missed due to the strict indexing lag typical of traditional databases. Thus, standardizing the search strategy within Google Scholar eliminates inter-database distortions that occur when translating complex Boolean logic.

A Boolean search string (“AI models” OR “AI architectures” OR “Deep learning” OR “CNN” OR “Computer Vision”) AND (“Drone-Imagery” OR “UAVs Imagery” OR “Aerial imagery”) AND (“Northern Corn leaf blight” OR “Northern Leaf blight”) was used to perform a database search to identify relevant primary sources for the study. Additionally, to capture current state-of-the-art AI model architectures and methods, the search results are filtered from the year 2015 to 31 March 2026, the current date the search was conducted. The publication titles retrieved by the search string highlighted the current AI architectures and data preprocessing techniques for UAV imagery spanning a period of 10 years. The search result is processed to select the most relevant publications for the synthesis analysis based on the selection criterion defined in the next subsection.

Selection criteria involve specifying the inclusion and exclusion criteria an article is assessed by to be selected for a systematic literature review study. Having a selection criterion strategy minimizes research bias and ensures the selected articles meet all conditions stated in the inclusion criterion and vice versa. In this study a rigorous two-stage screening procedure was executed independently by the two researchers to reduce selection bias. The researchers evaluated all retrieved titles exported with Zotero reference manager against the predefined inclusion and exclusion criteria during the initial stage of the selection process. Both researchers achieved a high interrater agreement without disagreements. The second stage involved independent examination of the full text of the remaining candidate articles to finalize the inclusion list. Any disagreement and conflicting classification decisions on the full-text examination between the researchers were formally resolved through weekly structured collaborative discussions until a consensus was reached, ensuring the final dataset of the primary selected studies remains entirely objective and free from individual selection bias.

Table 1 illustrates the coded inclusion and exclusion criteria for article selection for the study.

The paper selection process followed the key steps of the PRISMA review process chart adopted by Tedja et al. [12]. The key steps can be summarized into four (4) stages, with results feed forward after each stage. These stages are chronologically as follows: database search, articles selected based on title and keywords, articles selected after reading the entire content, and articles used in the research. Figure 1 selection process diagram illustrates the selection process following

Table 1. Inclusion and exclusion criteria.

Figure 1. PRISMA election process.

PRISMA election process.

From the database search, 174 articles were found. The filtering process was based on article titles that have maize or northern corn leaf blight or northern blight. A total number of 137 titles did not have one of those keywords; thus, they were irrelevant. After reading the entire content of the 37 relevant articles, only seven (7) papers met the inclusion criteria. Therefore, the research utilizes the seven (7) selected papers for the synthesis review.

Table 2 lists the titles of primary studies based on the inclusion criterion strategy, and a code is assigned to each paper title.

Table 2. Selected papers.

Establishing a quality assessment strategy is important when conducting a systematic literature review, as it ensures bias is minimized and the study is conducted objectively. A quality assessment strategy is a checklist with specific guidelines for selecting the primary sources of empirical studies to be reviewed in a systematic review, apart from the inclusion and exclusion criteria. In this study, a checklist containing seven (7) generic questions was utilized to assess the quality of an article by assigning scores to each article using a three-point Likert scale. The researchers ensured that the assessment questions are balanced in accordance with the research questions.

The first question assesses whether the study targets NCLB and uses drone imagery for the research experiment (Q1). This question sets the theme of the disease under investigation. After establishing the target of the study, the second question (Q2) focuses on the AI methodology used in the paper to ensure the paper describes the model architecture and the learning task it is trying to solve. (Q3) checks whether the study compares various models or AI model baselines to justify the performance improvements. Drone imagery datasets for maize NCLB disease are limited publicly online, and to ensure study variability based on studies, (Q4) ensures the study reports the dataset source, labelling, and description. Furthermore, (Q5) checks the image preprocessing and augmentation techniques used in the paper, such as image resizing, image tiling, and any domain adaptation methods described. (Q6) assesses evaluation rigor; the paper should report relevant performance metrics applicable to the choice of AI architectures used in the paper. A report on performance metrics also provides empirical evidence for the methods applied during the experimentation and ensures research reproducibility. Lastly, (Q7) checks for evidence of work that addresses real-world challenges such as small object detection, occlusion, and environment variation. Table 3 highlights the quality assessment questions used to evaluate each primary study and supports the study objective of identifying the AI model frameworks or architectures for NCLB disease detection using UAV imagery, with specific methods that address occlusion problems and small object detection.

Table 3. Quality assessment questions.

After selecting papers, quality scores are computed based on the relevance, rigor, reporting, and credibility criteria. This structured scoring approach reduces subjectivity during the evaluation assessment of each included study, and the total quality score for a paper is obtained by summing all the scores across all criteria. To obtain the scores for paper quality assessment, a three-point scale is used, where yes is given a score of 1 when a paper clearly satisfies the criteria, 0 is assigned to no when the criteria are not reported clearly, and lastly, somewhat is assigned a value of 0.5. Papers with a higher quality assessment score are classified as high quality because they provide clear methodology, strong evaluation evidence, and strong alignment with the review’s objective and focus area. On the other hand, studies with a low-quality score are classified as low quality due to weak reporting, limited relevance to NCLB detection using drone imagery, and less emphasis on occlusion and small object detection problems. Therefore, more robust interpretation of results and conclusions can be drawn from studies with higher scores, exposing suitable AI architectures and processing techniques for drone imagery data for NCLB disease detection using computer vision algorithms.

The study adopts an 8-point maximum possible score to evaluate the methodological integrity of the selected studies by applying a structured quality assessment (QA) framework. The study established a categorization threshold to classify studies into high, medium and low-quality cohorts based on the paper’s cumulative total QA score obtained. To interpret these scores objectively, studies achieving a cumulative score of 7.0 to 8.0 are classified as high quality, demonstrating robust data curation process, rigorous validation and reporting of results and transparent model architecture for drone-imagery-based NCLB disease detection. Papers between 5.0 and 6.5 points are designated as medium quality, indicating valid scientific contributions that possess minor limitations in methodology when dealing with small-object detection. Any study scoring below 5.0 points is categorized as low quality, signifying critical methodological deficiencies or insufficient documentation that answer the study questions. To ensure the review’s conclusions rest on reliable evidence, only studies categorized as medium or high quality are considered to influence the final synthesis.

Table 4. Quality assessment scores.

The quality assessment questions were grouped to assess paper relevance, results reporting, rigor, and credibility. Table 4 illustrates the quality assessment scores for each primary paper. Relevance determines if the research solves the problem of NCLB disease detection using AI models and drone imagery. Results reporting determines whether the results of the experiments were reported in the paper. Research rigor determines the scientific backbone that ensures the methodology is sound, and lastly, credibility ensures research trustworthiness and reproducibility.

The research utilized the Zotero reference manager to extract article data from the database search results. The data extracted by the Zotero reference manager include authors’ names, titles, years, abstracts, publisher, type of paper [20], etc. The data obtained by Zotero was exported to a .csv file to create a Data Frame for further manipulation using the Python programming language and the Pandas library to filter paper titles using the keywords northern corn leaf blight or northern leaf blight. A minimized dataset containing 37 records was created from the filtering results and manually analyzed strictly following the inclusion and exclusion criteria as described in subsection 3.3 Selection criteria.

The research adopts a narrative synthesis method, which emphasizes understanding the study context and methodologies of included studies without statistical pooling [20]. Therefore, the research data synthesis method focuses on descriptive aggregation of research findings, focusing on findings across studies, focusing on themes, similarities, and patterns, and incorporating subjective interpretation [20].

To ensure a transparent analytic trace from the primary literature to the final review conclusion, the study adopts a standardized data extraction matrix with six (6) explicit descriptive and technical fields highlighting the paper’s core technical methodological themes. These fields were coded systematically from each of the seven selected primary studies and mapped directly onto the review’s research questions RQ1, RQ2, and RQ3 to generate synthesis themes. The fields are model/architecture, model attention mechanism, core technical focus, dataset characteristic, data preprocessing/augmentation, and occlusion/small-object solution. The core themes are as follows:

Theme1: Computer Vision Architecture and Core Technical Focus—the theme maps to RQ1. The information extracted specifies the performance metrics, core deep learning network and backbone adopted and the application scope of the computer vision model.

Theme2: Dataset Characteristics and Curation—this theme is mapped to RQ2. To evaluate data architectural constraints, the data extracted include data augmentation and preprocessing methods, and dataset source.

Theme3: Structural Adaptations—this theme is mapped to RQ3, it addresses the techniques and mechanics deployed in the structural design of deep learning network to mitigate occlusion and small-object detection problems which are associated with UAV data. The extracted and recorded data focused on detailing model attention mechanism.

The qualitative findings from the source papers were seamlessly translated into direct empirical answers for the study’s research questions by clustering these six (6) standardized data fields into localized synthesis themes.

The synthesis of the selected studies has shown the technological landscape evolution of deep learning algorithms for NCLB disease detection from Convolutional Neural Networks (CNN) utilized in papers (P2 and P4) to real-time detection models that focus on fine-grained pixel-level analysis like the Attention YOLACT++ in paper (P5). In addition, the synthesis of selected studies highlighted that early works utilized standard backbones to identify NCLB disease lesions. For example, paper (P2) utilized a Res-Net-101 as the backbone for the R-CNN model, and paper (P4) utilized ResNet-34 as the backbone for the CNN architecture. The CNN backbone consists of spatial pooling and scale-invariant layers that perform specific roles for feature extraction by aggregating shallow features and rich semantic information [21] into a feature map before forwarding it to the neck and head of a CNN. Based on the findings, ResNet architectures are the most widely used as backbones for CNN architectures because they can achieve high accuracy and have high performance in object detection [22].

Furthermore, there is a noticeable transition from traditional feature-based models to Hybrid and Lightweight models in paper (P7), and the Multimodal Fusion model in paper (P6). Earlier papers (P2, P4) established the efficiency of deep residual networks for NCLB disease phenotyping, increasing throughput in disease detection as compared to manual assessments, which are slow and subjective. Recent studies by authors of (P5 and P7) emphasized architectures for real-time segmentation of NCLB disease lesions. However, deep learning models for the semantic segmentation task are mostly considered for real-time phenotyping tasks because the architectures focus more on high-performance accuracy [23], but less attention is given to computational cost and inference speed for resource-constrained processing on embedded platforms [24]. To address these challenges, paper (P7) proposes a lightweight segmentation model, the YOLOv5-Mobile-seg architecture, to attain low computational complexity suitable for edge-node processing of drone imagery for NCLB disease with high accuracy.

With the current developments of lightweight models as a way of enhancing the performance of semantic segmentation architectures, the integration of spatial attention modules in paper (P7) marks a transition from general feature extraction to selective focusing, which is essential for isolating different sizes of NCLB lesions from sophisticated backgrounds. In addition, model pruning and ablation may increase model performance, as evidenced by the average precision (AP) increments in the experimental results of (P7).

Drone imagery data is affected by environmental noise, such as when the NCLB lesion color is similar to other objects such as maize tassels, soil, and other crop blights, which remains a bottleneck in UAV-based NCLB detection. Therefore, it is essential to ensure high data quality and to utilize data optimization strategies when working with such a complex dataset that is greatly affected by environmental noise. From the synthesis of selected studies, three (3) dominant data optimization strategies were identified, addressing the credibility gap to ensure the dataset is reliable for AI model training. These strategies are identified as synthetic data generation, weakly supervised learning, and data augmentation.

4.2.1. Data Augmentation

Data augmentation strategies consist of applying various types of transformations to the training dataset for the purpose of artificially increasing data diversity [25]. The most widely used transformations include rotation, flipping, zooming, scaling, and brightness adjustment. Almost all the papers in this research adopted these transforms. In a similar approach, all the papers have performed image cropping or resizing before applying other data augmentation transformations for the sake of reducing computational cost during the training process if the original drone image size was used. The drone-captured images are very high in resolution, which requires data compression techniques such as applying slicers, grids, and cropping to mitigate challenges in data management.

4.2.2. Weakly Supervised Learning

The authors of (P3) introduced a weakly supervised segmentation model for NCLB segmentation by generating pseudo labels of NCLB lesions for training the model. Their proposed model achieved 84.6% precision, proving that high performance can be achieved without exhaustive manual annotation. Weakly supervised learning is a paradigm in the machine learning taxonomy, which sits between supervised learning and unsupervised learning [26], and machine learning models are trained using a small amount of labeled data and a large amount of unannotated data. There are three approaches under weakly supervised learning, which are incomplete supervision, inexact supervision, and inaccurate supervision [27].

4.2.3. Synthetic Data Generation

The paper (P7) adopted a deep learning-based data augmentation by utilizing CycleGAN to perform data optimization, to artificially increase the dataset images, NCLB lesion size variability, and variant lighting and illumination conditions depicting real-world scenarios. This approach of utilizing GANs for data optimization reduces labelling workload, and also the deep learning-based augmented dataset improves model performance and robustness under complex situations where occlusion problems are often encountered [28].

The main objective of this SLR is to investigate the methods and techniques applied to handle drone imagery problems focusing on NCLB disease detection, focusing on small object detection and occlusion problems.

4.3.1. Small Object Detection

Recent studies (P3, P5, P7) have shown that implementing Attention modules has become the standard for the segmentation task and for achieving high performance, although the papers did not explicitly mention small object detection. Paper (P3) adopted the ACoL pixel attention model for feature extraction in a weakly supervised learning setting. The module utilizes two (2) parallel classifiers, namely classifier A path, which uses an Adversarial Mechanism to learn to detect the most prominent part of the object, then erases it from the feature map before passing it to the other classifier. Meanwhile, the classifier B path is trained to perform the complementary learning of the remaining regions of the object that the first classifier did not focus on, then fuses the feature maps together [29]. In addition, paper (P5) introduced the Convolutional Block Attention Module (CBAM) to improve the accuracy of the YOLACT++ model architecture, and the performance metrics results were tremendously good, ranging above 98%. In the same context, the paper (P7) introduced the CBAM integrated with the Fused MobileNet Bottleneck Convolution Module (FusedMBConv), and the model achieved an average precision of 88.0% on the segmentation task and 88.8% on the recognition task. Therefore, this mechanism can successfully increase performance.

The CBAM comprises channel attention mechanism (CAM) and spatial attention mechanism (SAM) modules that enhance the focus on crucial locations of features while suppressing the irrelevant ones [30], and the advantage of these modules is that they improve the classification performance of CNNs [31] without adding a lot of network parameters [32] since the CBAM is a lightweight module [33]. Furthermore, the Fused-MBConv module improves the recognition accuracy of occluded objects [34] and improves computational complexity.

Therefore, these attention models are essential when mitigating challenges associated with small object detection, such as background noise, as these modules can learn to isolate high semantic information from noise.

4.3.2. Occlusion Constraints

The occlusion problem remains a challenge in computer vision, especially when detecting objects from drone imagery. This might lead to reduced AI model accuracy and high complexities, such as intricate development processes involved in developing, training, and deploying AI systems to mitigate the occlusion problem. According to Huang Y et al. [35], the use of data augmentation, attention modules, and improved loss functions can solve occlusion problems. Since all the selected papers for this synthesis have utilized either data augmentation techniques or attention modules, this may solve the problem of extracting sufficient features due to occlusion.