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Systematic Review

Application of Remote Sensing and Geographic Information Systems for Monitoring and Managing Chili Crops: A Systematic Review

by
Ziyue Wang
*,
Md Ali Akber
and
Ammar Abdul Aziz
School of Agriculture and Food Sustainability, The University of Queensland, Gatton, QLD 4343, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(16), 2827; https://doi.org/10.3390/rs17162827
Submission received: 27 June 2025 / Revised: 1 August 2025 / Accepted: 11 August 2025 / Published: 14 August 2025
(This article belongs to the Special Issue Advances in Multi-Sensor Remote Sensing for Vegetation Monitoring)
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Abstract

Chili (Capsicum sp.) is a high-value crop cultivated by farmers, but its production is vulnerable to weather extremes (such as irregular rainfall, high temperatures, and storms), pest and disease outbreaks, and spatially fragmented cultivation, resulting in unstable yields and income. Remote sensing (RS) and geographic information systems (GIS) offer promising tools for the timely, spatially explicit monitoring of chili crops. Despite growing interest in agricultural applications of these technologies, no systematic review has yet synthesized how RS and GIS have been used in chili production. This systematic review addresses this gap by evaluating existing literature on methodological approaches and thematic trends in the use of RS and GIS in chili crop monitoring and management. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines a comprehensive literature search was conducted using predefined keywords across Scopus, Web of Science, and Google Scholar. Sixty-five peer-reviewed articles published through January 2025 were identified and grouped into different thematic areas: crop mapping, biotic stress, abiotic stress, land suitability, crop health, soil and fertilizer management, and others. The findings indicate RS predominantly serves as the primary analytical method (82% of studies), while GIS primarily supports spatial integration and visualization. Key research gaps identified include limitations in spatial resolution, insufficient integration of intelligent predictive models, and limited scalability for smallholder farming contexts. The review highlights the need for future research incorporating high-resolution RS data, advanced modelling techniques, and spatial decision-support frameworks. These insights aim to guide researchers, agronomists, and policymakers toward enhanced precision monitoring and digital innovation in chili crop production.

1. Introduction

Chili (Capsicum sp.) is a staple spice in global cuisines [1]. Originally native to the Americas, it was introduced to other parts of the world following Christopher Columbus’s 1492 expedition to the New World [2]. Today, chili cultivation is concentrated in South America and Asia, particularly in countries such as China, India, Mexico, Turkey, and Indonesia, where the tropical and subtropical climates provide optimal growing conditions [3]. China is the world’s largest producer, with over two million hectares under chili cultivation, accounting for approximately 30% of global output. Indonesia follows, contributing about 12% of the total cultivated area [4].
In addition to its culinary importance, chili is a high-value crop that is essential to the livelihoods of millions of smallholder farmers, especially in Indonesia and India, where cultivation is largely labor-intensive and small-scale [5,6]. In Indonesia, approximately 30% of smallholder vegetable farmers integrate chili into their cropping systems [7]. Despite its economic significance, the sustainability and profitability of chili farming are increasingly challenged by various production risks, such as climatic variations [8], pest and disease outbreaks [9,10,11,12], and declining soil quality [13,14]. These risks are intensified by the structural characteristics of smallholder systems, which often rely on traditional knowledge and manual practices with limited capacity to adapt under dynamic environmental conditions [15]. Consequently, chili yields can be unstable, directly affecting household income. For financially vulnerable farming households, such fluctuations can reinforce poverty cycles and diminish long-term resilience [16].
These production uncertainties underscore the need for timely, accurate, and spatially explicit monitoring systems to inform adaptive decision-making in chili cultivation. Effective monitoring allows farmers to detect emerging risks, optimize input use, and implement both corrective and preventive measures [17]. Conventional monitoring in chili farming primarily relies on field-based surveys and manual data collection, which are time-consuming, labor-intensive, and spatially constrained [10]. These limitations hinder the generation of comprehensive, real-time insights necessary for responsive chili crop management, particularly at regional or national scales [18].
In this context, RS and GIS technologies offer promising solutions for improving the efficiency and accuracy of agricultural monitoring. RS technology rapidly acquires large volumes of data via satellites or aircraft platforms, offering information far beyond human visual perception [19]. GIS facilitates complex decision-making by integrating heterogeneous data sources for spatial analysis and visualization [20]. The integration of RS and GIS has served as a fundamental tool for crop monitoring at local, regional, and global scales since the 1970s [21]. GIS- and RS-based crop monitoring includes several core components: the analysis of agroclimatic conditions [22], monitoring of crop health [23] and stress levels [24], and the prediction of crop yields [25]. Additionally, some systems extend their scope to include food security assessments, offering early warnings of potential food shortages [26].
Over the past decades, the significance of RS has been well documented in systematic reviews focusing on staple crops [27,28,29]. For example, Mulla [17] reviewed 25 years of remote sensing applications in precision agriculture, while Weiss et al. [19] synthesized RS-based approaches for crop classification, yield estimation, and biotic and abiotic stress detection. However, these reviews have focused mainly on cereals such as rice, wheat, and maize, which dominate large-scale farming systems. To date, no systematic review has examined the application of RS technologies in the monitoring and management of chili—a high-value horticultural crop predominantly cultivated by smallholder farmers in Asia, where its production holds both economic and social significance [5]. Furthermore, most existing reviews analyze RS technologies in isolation, giving limited attention to the complementary role of GIS in fine-scale spatial analysis, land suitability evaluation, and integrated crop management [17]. This methodological gap may constrain the full potential of spatial technologies in delivering data-driven, adaptive strategies for complex and fragmented farming systems.
This paper presents a systematic review of the application of RS and GIS technologies in chili crop monitoring and management. The objectives of this study are to: (1) categorize the spatial technologies and modeling methods applied in chili crop studies; (2) evaluate the accuracy, strengths, and limitations of different methodological frameworks; and (3) identify key research gaps and propose directions for future work. To achieve these aims, the discussion is structured into three thematic sections: Section 4.1 and Section 4.2 examine the temporal and geographic distribution of studies, as well as the technologies and models employed; Section 4.2 and Section 4.3 assess methodological performance and trade-offs; and Section 4.4 synthesizes key limitations and outlines future research directions. By synthesizing existing research and identifying critical gaps, this review provides valuable insights into the potential of RS and GIS technologies to enhance chili cultivation, particularly in the face of climate variability and food security challenges. This is especially relevant for regions such as Indonesia and India, where chili cultivation is dominated by smallholder systems facing heightened production risks. The findings offer practical guidance for agronomists, farmers, and policymakers, supporting the development of data-driven strategies to strengthen the sustainability and resilience of chili production systems in vulnerable regions. Furthermore, the review contributes to the growing body of knowledge in precision horticulture.

2. Materials and Methods

This systematic review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines [30], a four-step iterative process consisting of (1) identification, (2) screening, (3) eligibility, and (4) inclusion (Figure 1). A systematic review can select, filter, and analyze the overview of the evidence in a clear, transparent, complete, and consistent way [31]. Unlike traditional literature reviews, this rigorous approach ensures a comprehensive synthesis of existing data, enhancing the reliability and generalizability of findings and thus providing a more objective basis for policy and practice decisions. This systematic review was registered on the Open Science Framework (OSF) under the registration DOI: https://osf.io/qc9kz, accessed on 29 June 2025. The review protocol was developed in accordance with PRISMA guidelines.

2.1. Scope

The initial phase of the study delineated the inclusion and exclusion criteria (Table 1). The focus of this paper predominantly centers on RS and GIS as tools to manage and monitor chili crops, steering clear of extraneous subjects such as chili trade, improvement of chili varieties, and unrelated topics. The study is limited to documents written in English, prioritizing those with comprehensive coverage. Only complete documents are included, while reviews, commentaries, news, and project briefs are excluded, ensuring a thorough and reliable examination of the pertinent literature.

2.2. Search

Given that the review focused on various technological applications in the agri-food system domain, a set of keywords was logically constructed using Boolean operators and applied to searches within the title, abstract, author keywords, and keywords plus fields. The specified keywords included: (“chili” OR “chilli”) AND (“remote sensing” OR “satellite image” OR “Geographic information science” OR “GIS”). This search strategy was tailored to ensure a comprehensive exploration of the relevant literature.
A keyword search was conducted across the Web of Science (WoS), Scopus, ScienceDirect, and Google Scholar databases. These platforms are recognized for their extensive compilation of publications from reputable sources such as Springer, IEEE, Elsevier, Taylor and Francis, and others, ensuring a thorough inclusion of high-impact journals primarily published in the English language [32]. Additionally, to address potential gaps in academic databases, a manual search was also performed on the Food and Agriculture Organization (FAO) website. Only studies published before January 2025 were included in the review.

2.3. Screening

All identified documents were assessed against predefined inclusion criteria, and those that did not meet the standards were excluded. The initial search yielded 14,133 entries, comprising 687 papers sourced from Web of Science (WOS), Scopus, and Science Direct, and 14,000 from Google Scholar. Given Google Scholar’s reference limit of 1000 results, the total number of unique documents initially considered was 1687.
Following the removal of 687 duplicates, a preliminary screening of titles and abstracts led to the exclusion of 878 papers deemed irrelevant to the study’s scope. Through this multi-stage screening process, 59 documents were identified for full-text review. Additionally, a manual search of references from these selected studies identified six additional relevant papers. In total, 65 papers were included in the final review.

2.4. Data Extraction and Analysis

Data extraction captured the key characteristics of the selected studies, including publication details, spatial technologies applications (crop mapping, biotic stress, abiotic stress, land suitability, crop yield prediction, soil fertilizer, crop health condition, and others), study objectives, geographical focus, methodology, and the accuracy and efficiency of these technologies. Data extraction and analysis were conducted using Microsoft Excel to facilitate a systematic review. The process involved organizing the extracted information into structured datasets, enabling cross-tabulation, frequency distribution analysis, and data mapping. These analytical techniques enabled the identification of trends, research gaps, and variations in the application of RS and GIS across different contexts. Additionally, patterns in study locations, methodological approaches, and technological advancements were examined to provide insights into the effectiveness of these tools in chili crop monitoring and management.

3. Results

3.1. Annual Distribution of Selected Studies

In this study, a total of 65 documents were selected for analysis (Figure 2). Among these, 49 are articles from peer-reviewed journals, while the remainder comprises nine conference papers, six book chapters, and one thesis. The application of RS and GIS in chili crop monitoring was first documented in 2006, with a sharp increase in research activity observed since 2019.
The 65 selected documents were categorized into eight main research areas based on their focus: (1) mapping, (2) yield prediction, (3) biotic stress management, (4) abiotic stress management, (5) growth and health condition monitoring, (6) soil fertilizer management, (7) land suitability, and (8) others (Figure 3). Studies that covered multiple categories were classified under the “others” category. Early studies on chili monitoring using RS and GIS technologies primarily focused on crop mapping, abiotic stress, and biotic stress. Among these, crop mapping and biotic stress have remained the dominant research themes. Studies on land suitability, growth, health condition monitoring, and soil fertilizer assessment emerged mainly after 2019.

3.2. Geographic Distribution of Selected Studies

Studies on chili crop monitoring and management using GIS and RS technologies have been conducted in 13 countries (Figure 4), with a strong concentration in Asia. India, Indonesia, and China represent the primary research hubs, with 23, 11, and seven papers, respectively. Other countries, including Mexico, Australia, Turkey, Belgium, the USA, Bangladesh, Spain, and the Republic of Ecuador, have also contributed to this research, though to a lesser extent, ranging from one to three papers. No studies on chili crop monitoring were identified from Africa.

3.3. GIS and RS Application in Chili Monitoring

As shown in Figure 5, RS technology was used in more than 80% of the studies, dominating seven research categories, except for soil fertilizer assessment. Although GIS accounted for less than 20% overall, it was widely integrated into RS-based studies, particularly in biotic stress monitoring, mapping, yield prediction, and others, with one study combining GIS and RS techniques for each. All studies on abiotic stress monitoring and growth and health condition monitoring relied exclusively on RS technology, while two on soil fertilizer assessment applied only GIS technology. Each study was assigned to one primary research category based on its main objective, even if it covered multiple aspects. This classification approach was adopted to maintain clarity and avoid double-counting.

3.4. RS Applied in Chili Monitoring and Management

The application of RS in chili crop monitoring and management encompasses a diverse technological landscape, involving a range of platforms, sensors, and methods (Figure 6). In this context, satellites emerge as the predominant platforms in 44% of research studies. UAVs and ground-based platforms were utilized less frequently, appearing in 27% and 25% of the articles, respectively. Laboratory platforms were mentioned in only 4% of the articles. There are four main types of sensors based on physical sensing mechanisms and spectral regions: optical sensors, which include multispectral, RGB, and hyperspectral sensors, and radar, thermal, and quantum sensors. In this study, RGB sensors refer specifically to devices that capture only the visible spectrum (i.e., red, green, and blue bands), such as standard UAVs or ground-based digital cameras. In contrast, multispectral sensors refer to those that include both visible and non-visible bands (e.g., NIR, red edge). Multispectral and RGB sensors are the most commonly used optical sensors and are widely employed data sources among all types. The remaining four types of sensors, ranked from highest to lowest usage, are hyperspectral (9%), radar (7%), thermal (4%), and quantum (1%) sensors. Over 80% of studies utilized machine learning (ML) and deep learning (DL) for RS data analysis.
Figure 7 provides a detailed breakdown of platform, sensor, and method usage across various RS-based applications for chili crop monitoring, including crop mapping, biotic and abiotic stress detection, land suitability assessment, yield prediction, and growth and health condition monitoring. Satellite platforms are predominantly applied in land suitability studies and are widely used for crop mapping, with over 70% of crop mapping studies utilizing satellite imagery. However, their application is absent in yield prediction and growth and health monitoring tasks. In contrast, although less frequently used overall, UAVs and ground-based platforms play a central role in detecting biotic and abiotic stress and predicting yield. Ground-based observations remain essential for detailed field measurements, such as yield prediction, while laboratory-based approaches are primarily applied in studies of abiotic stress. One study integrated UAVs and satellite data for chili crop mapping, highlighting the potential of multi-source data fusion [29].
Among sensor types, multispectral sensors are the most widely used and appear across all application categories. They are universally adopted in land suitability assessments and studies categorized as “others.” RGB sensors are predominantly used for biotic stress monitoring, accounting for nearly 60% of studies in this category. Hyperspectral sensors are favored for yield prediction, appearing in over 60% of relevant studies. Radar sensors are exclusively used in crop mapping, whereas thermal and quantum sensors are solely applied in abiotic stress detection. Notably, several studies utilized combinations of sensors rather than relying on a single type. These include integrations of multispectral and RGB sensors, as well as multispectral and radar sensors. The combination of multispectral and radar sensors was exclusively applied for crop mapping. In contrast, multispectral and RGB sensors were integrated for mapping and growth analysis, with 50% of the growth assessment conducted using this combination. From a methodological standpoint, ML and DL approaches dominate most application categories, reflecting their growing importance in RS-based chili crop monitoring. The only exceptions are land suitability and yield prediction studies, where statistical methods remain the primary analytical tool. Specifically, all yield prediction studies relied exclusively on statistical approaches.

3.4.1. Platform and Sensors

Each sensor type comprises specific sensors used for monitoring various aspects of chili production (Figure 8). The RGB sensor category encompasses devices that capture only the visible spectrum (red, green, and blue bands). In the reviewed studies, these devices primarily consisted of standard digital cameras and UAV-mounted RGB cameras. This category was the leading source (combined 27%) among all sensors and was primarily used in 10 studies related to biotic stress monitoring. The multispectral sensor category comprises sensors onboard various satellite platforms, including the Multispectral Instrument (MSI) on Sentinel-2, Operational Land Imager (OLI) on Landsat-7/8, the MODIS sensor on Terra/Aqua, and sensors on high-resolution platforms such as WorldView-2, QuickBird, PlanetScope, and LISS-IV. Among these, medium-resolution platforms such as Sentinel-2 and Landsat were more frequently used than lower-resolution platforms (e.g., MODIS, NOAA) (Table 2), particularly for crop mapping and monitoring biotic stress. High-resolution platforms (e.g., PlanetScope, QuickBird, and WorldView-2) were each applied in three mapping studies. For hyperspectral sensing, the ASD FieldSpec Pro spectroradiometer was the primary device used in studies on yield prediction and assessment of abiotic stress. The only radar sensor used was the C-band synthetic aperture radar (SAR) onboard Sentinel-1, which was applied in two chili crop mapping studies to overcome the limitations of cloud cover in optical imagery. Additionally, thermal sensors and quantum sensors (e.g., the LI-190 quantum sensor) were utilized in detecting abiotic stress. In terms of sensor fusion, four reviewed studies integrated multiple sensor types. One study integrated Sentinel-2 and RGB sensors, while another combined Sentinel-1 and Sentinel-2 data for chili mapping. Another two studies combined UAV-based multispectral cameras with an RGB camera to monitor crop health conditions.

3.4.2. RS Data Analysis Methods

The primary analytical methods used for chili crop monitoring include machine learning (ML), deep learning (DL), and statistical approaches. Among these, ML methods heavily rely on training data to build and validate models. According to our analysis, 88% of the studies employing ML methods obtained their training data through field surveys (Figure 9). A smaller proportion of studies relied on visual interpretation (4%), a combination of field surveys and visual interpretation (4%), government data (2%), or questionnaire-based data (2%).
ML and DL methods have emerged as key primary analytical approaches for processing RS data. A total of 27 models are freely available and widely used in RS-based chili monitoring (Figure 10). These models include 14 ML models and 13 DL models. Of the 13 DL models, nine were applied to biotic stress monitoring, while the remaining four were used for other applications—one for abiotic stress detection, two for crop mapping, and one for growth and health monitoring. Among the ML models, Support Vector Machine (SVM) was used the most, followed by Convolutional Neural Network (CNN) and Random Forest (RF), but they were only applied to biotic stress and crop mapping. Furthermore, the health condition of chili crops was predominantly assessed using Region-based Convolutional Neural Network (RCNN), Least Squares Support Vector Machine (LSSVM), YOLOx8, and a Geographic Object-Based Model.
Table 3 presents the different classification algorithms and their respective accuracies as reported in the selected studies. Mean accuracy values are calculated by averaging the overall classification accuracies reported across studies that used the respective algorithm. Out of the 56 papers that mentioned using one or more algorithm types, only 40 papers provided accurate results across 23 distinct algorithm types. According to this table, the accuracy of the three most frequently used parameters (SVM, RF, and CNN) is also relatively high, all exceeding 90%. Except for seven algorithms, including Bayes, the Geographic Object-Based model, LSSVM, KNN, ANN, and unsupervised classification, the majority of classification algorithms’ mean accuracy was greater than 80%.
In addition to the ML and DL models mentioned above, statistical algorithms have also been applied in monitoring chili crops (Figure 11). However, their application is mainly focused on three areas: yield prediction, biotic stress monitoring, and abiotic stress monitoring in chili crops. Among these, stepwise multiple linear regression was the most frequently applied method, used in two studies on yield prediction and one study on abiotic stress monitoring. For biotic stress monitoring, three main statistical methods were applied, with Moran’s I test being relatively more popular. In contrast, the three main statistical methods used for abiotic stress were applied with equal frequency.

3.5. GIS Applied in Chili Monitoring

The application of GIS technologies requires appropriate geospatial data. While some GIS datasets can be derived from RS, this section focuses on datasets used independently of RS, which are classified as a separate category (Table 4). In five chili monitoring studies, GIS data containing location information were used to map the geographic distribution of chili crops. These datasets were often combined with primary data collected through interviews or focus group discussions to support spatial visualization of land suitability and biotic stress conditions. Additional GIS layers, such as land use and land cover data, offer valuable insights into both current and historical land use patterns. This information supports the assessment of soil quality and helps determine the nutrient status of cultivated areas.

4. Discussion

4.1. Temporal and Geographic Distribution of Studies of RS and GIS-Based Chili Monitoring and Management

This paper presents a comprehensive review of articles that apply GIS and RS technologies in chili monitoring and management. Research in this field began relatively late, with a noticeable rise in interest after 2019 (Figure 3). Although satellite-based RS and GIS have been widely applied to monitor staple crops since the 1970s [38], their use for horticultural crops such as chili has developed more slowly. This delay can be attributed to the limitations of early sensors, which lacked the spatial, spectral, and temporal resolutions necessary for capturing the characteristics of smallholder chili plots [21]. Technological improvements over the past two decades, including advances in satellites, UAVs, aircraft, and ground-based systems [39], along with the integration of GIS with big data, Artificial Intelligence (AI), and the Internet of Things (IoT) technologies [40], have significantly enhanced monitoring capacities, leading to more extensive research activity since 2019. Geographically, studies have mainly been concentrated in India and Indonesia (Figure 4), where chili production holds primary economic importance. The research concentration in these countries reflects a clear alignment between academic efforts and regions with substantial production, consumption, and export of chili [4,40].

4.2. Platforms, Sensors, and Approaches of RS in Chili Crop Monitoring

The effectiveness of RS-based chili crop monitoring depends on integrating appropriate platforms, sensors, and approaches. This section examines the effectiveness of various platforms, sensors, and methods in chili monitoring across different types.
The choice of platform plays a fundamental role in determining the spatial and temporal resolution of chili monitoring. The selection of RS platforms is closely linked to the required level of detail in specific monitoring tasks. Satellite-based platforms predominantly delineate large-scale chili cultivation zones (Figure 7). Their broad spatial coverage and regular revisit cycles make them well-suited for extensive monitoring [21]. However, the relatively coarse spatial resolution of satellites limits their ability to detect fine-scale physiological changes and localized stress events, which are essential for timely management interventions [41]. In contrast, UAVs and ground-based systems, offering much higher spatial and temporal resolution, could capture subtle spectral and structural changes before visible symptoms occur [41,42], and they are more commonly employed for monitoring abiotic stress, biotic stress, and yield estimation (Figure 7). While current platform choices reflect a rational allocation based on technical capabilities, they also reveal a limitation: the lack of integrated monitoring approaches capable of simultaneously addressing large-scale mapping and fine-grained field observations.
Although platform selection defines the scale of observation, the ability to capture relevant crop features ultimately depends on the sensors deployed. Different monitoring tasks in chili production require sensors with specific spectral and spatial capabilities (Figure 8). RGB cameras, due to their high spatial resolution, low cost, and operational simplicity, have been widely used for detecting visible symptoms of biotic stress, such as leaf discoloration and structural damage caused by pests or diseases [9,10,11,12,34,43,44,45,46,47,48,49,50,51]. However, their reliance on surface-level visual cues limits their capacity to capture early physiological changes before visible symptoms manifest [52]. Multispectral sensors extend observation capabilities beyond the visible range by capturing reflectance in discrete bands across the visible and near-infrared regions. This enables the calculation of vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI), which are sensitive to variations in canopy structure, chlorophyll content, and plant biomass [53,54].
While multispectral imagery provides critical insights into crop growth and health dynamics, its relatively coarse spectral resolution restricts its ability to detect subtle biochemical shifts associated with early stress or physiological changes [55]. To overcome these limitations, hyperspectral sensors such as the ASD FieldSpec Pro spectroradiometer have been employed. Hyperspectral sensors acquire dense spectral information across hundreds of narrow bands, allowing for the early detection of fine-scale biochemical and structural variations closely associated with crop productivity [56]. Despite their analytical advantages, hyperspectral applications remain largely experimental due to high instrumentation costs and the operational complexity of data processing [57]. RGB, multispectral, and hyperspectral sensors are all optical systems that rely on the passive detection of reflected sunlight to indirectly infer chili crop conditions, primarily capturing surface and pigment-related characteristics. Still, they are inherently constrained in monitoring internal physiological processes such as water status or photosynthetic activity [22].
Thermal and quantum sensors have been increasingly utilized to provide more direct assessments to address these limitations. Thermal sensors detect canopy temperature variations indicative of water stress [58], while quantum sensors measure photosynthetically active radiation (PAR), offering insights into photosynthetic efficiency under varying environmental stresses [59]. Nevertheless, optical and thermal observations become challenging under persistent cloud cover or during periods of limited solar radiation [21,41]. In such cases, active microwave systems, such as Sentinel-1, provide a valuable alternative. SAR sensors capture the structural features of the chili canopies and soil surfaces independently of atmospheric and illumination conditions. These make them particularly useful for land cover mapping and monitoring during the wet season. However, due to their limited sensitivity to biochemical properties such as chlorophyll content or water status, SAR data alone are insufficient for early stress detection or yield estimation tasks. Recognizing the complementary strengths and limitations of different sensor types, several studies have advocated for sensor fusion approaches. For example, integrating Sentinel-1 SAR and Sentinel-2 optical imagery enables simultaneous structural and spectral information capture, achieving higher mapping accuracies [60]. Similarly, merging RGB and multispectral data improves the detection of both visual and physiological indicators of crop health [61]. Such multi-sensor integration strategies offer promising avenues for developing more comprehensive, resilient, and scalable frameworks for chili crop monitoring.
While different sensors provide complementary information on crop structure, physiology, and biochemistry, extracting meaningful insights from these complex datasets increasingly relies on advanced analytical methods. ML and DL techniques have gained substantial attention in chili crop monitoring, particularly in crop mapping, stress detection, and growth assessment (Figure 7). These models can handle the high-dimensional and nonlinear relationships in RS data, outperforming traditional threshold-based approaches [62]. These models often utilize ground-truth data obtained through field surveys as training inputs (Figure 9) to uncover nonlinear relationships within the dataset and maintain robustness under noisy or heterogeneous conditions, thereby achieving the intended research objectives [63]. ML algorithms such as SVM, RF, and DL architectures, such as CNN, are the most frequently applied methods (Figure 10), valued for their robustness, computational efficiency, and ability to generalize across heterogeneous landscapes [64,65,66]. However, this methodological concentration may also reflect evidence of methodological inertia within the research community, whereby the repeated use of well-established models has inadvertently hindered the exploration of emerging, potentially more powerful algorithms.
Beyond ML and DL approaches, several reviewed studies employed traditional statistical methods to address specific analytical needs in chili crop monitoring (Figure 11). The decision to apply models was often driven by the characteristics of the data and the study objectives. For yield prediction, stepwise regression enabled researchers to model relationships between crop output and environmental or spectral variables, particularly in contexts where training data were limited or interpretability was prioritized [67,68]. In abiotic stress monitoring, Tukey’s HSD was used to compare treatment effects across experimental conditions statistically [69]. These statistical models offer transparency, require fewer training samples, and are well-suited to exploratory or confirmatory analyses in early-stage or small-scale studies [70]. Their application reflects the need for robust, interpretable, and computationally efficient tools in resource-constrained environments, such as smallholder-dominated systems.

4.3. GIS Application in Chili Crop Suitability and Spatial Analysis

Although GIS has been relatively underutilized in chili crop monitoring, it has primarily served as a supporting or visualization tool, playing a significant role in evaluating land suitability and conducting soil fertility analysis (Section 3.5). Soil fertility analysis integrates laboratory assessments of nutrient status with GIS-based mapping, whereas land suitability assessment is a broader concept that considers multiple factors beyond soil properties, including topography, hydrology, climate, and infrastructure [71]. Chili-planting land suitability is determined by aligning specific crop requirements with land characteristics, incorporating diverse factors such as irrigation availability, elevation, slope, rainfall, land use/land cover, NDVI, NDWI, and proximity to infrastructure [13,14,35,37,72,73,74,75]. In addition to these multi-criteria spatial analyses, GIS also plays a foundational role in mapping and interpreting point-based agricultural observations. These discrete data can be visualized as symbolized point layers or interpolated into continuous spatial surfaces. For example, IDW was applied in one of the reviewed studies [12] to interpolate NDVI values derived from UAV imagery for early detection of disease incidence. IDW is a deterministic spatial interpolation method that estimates unknown values based on a weighted average of nearby observations, with weights decreasing as distance increases [76]. In ArcGIS, this method is implemented via the Spatial Analyst toolbox, where parameters such as the power coefficient and search radius can be adjusted to control the influence of sample points [77]. This approach assumes spatial autocorrelation and is particularly valuable in smallholder production systems where ground-truth data are sparse and spatial variability is high [78]. By enabling the mapping of fine-scale variability in crop condition or environmental factors, spatial estimators such as IDW enhance the utility of remote sensing data and support site-specific decision-making. However, only one study explicitly applied spatial interpolation methods, suggesting a key methodological gap and the need for broader integration of spatial estimators in chili crop monitoring and management.

4.4. Limitations and Future Work

Although RS has become a widely used technology for chili crop monitoring, several limitations remain. Relying on a single RS platform is often inadequate for large-scale monitoring of smallholder chili farms. UAV-based RS provides high-resolution, field-level phenotypic information but is constrained by flight duration, battery capacity, and escalating data processing costs as spatial coverage increases [79]. In contrast, freely available satellite data offer broad coverage but typically lack the spatial and temporal resolution necessary for detecting variability in smallholder systems [80,81]. To address these challenges, integrating high-resolution UAV data with satellite observations through ML offers a promising approach. UAV imagery can serve as valuable ground-truth data to calibrate satellite-based models, improving mapping accuracy. Recent work in wheat monitoring demonstrated that combining UAV data and Sentinel-2 imagery using pixel aggregation and random forest models significantly enhanced county-scale predictions of growth and nitrogen status [42]. Although this approach has not yet been applied to chili crops, future research could leverage UAV-derived phenotypic traits to facilitate large-scale, high-accuracy monitoring of chili crops.
Optical RS typically detects stress symptoms only at relatively advanced stages, when spectral reflectance alterations become visually discernible. While effective for capturing structural and pigment-related changes, it remains limited in identifying early physiological responses critical for timely agronomic interventions. [82]. This reliance on post-symptomatic indicators restricts precision stress management, particularly in smallholder systems where early diagnosis is vital [23,83]. Emerging technologies such as solar-induced fluorescence (SIF) offer a promising solution by directly measuring chlorophyll fluorescence, providing mechanistic insights into plant photosynthetic efficiency, and enabling earlier detection of stress compared to conventional vegetation indices [84]. Recent studies have demonstrated the potential of SIF for early-stage stress monitoring. For instance, Wu et al. [85] detected water stress in sugar beet crops at the field level, and Du et al. [86] successfully identified stripe rust infection in winter wheat, confirming that SIF captures physiological variability associated with biotic stress. Thus, integrating SIF into future chili monitoring could substantially advance early stress diagnosis and improve management outcomes.
While ML and DL based on RS imagery have significantly advanced chili crop monitoring, their applications remain constrained in several aspects. One major constraint is the models’ reliance on high-quality training datasets, while field data collection is typically time-consuming, labor-intensive, and costly [19]. Transfer learning (TL) offers a promising solution by mitigating the need for large, labelled datasets and addressing domain shifts between crop types [87]. TL has demonstrated success in agricultural remote sensing tasks such as crop mapping [88,89], biotic stress monitoring [90,91], and crop yield prediction [92,93,94], suggesting potential applicability to chili crop monitoring. Another challenge is that ML and DL models are fundamentally data-driven, focusing on pattern recognition rather than causal inference, which limits their robustness under novel environmental conditions [95]. In contrast, mechanistic models, such as crop growth and radiative transfer models, offer physics-based frameworks that capture causal relationships between input variables and crop responses [21]. Integrating ML approaches with mechanistic models to build hybrid frameworks represents a promising future direction for enhancing chili crop monitoring systems’ scalability, interpretability, and robustness. Furthermore, although conventional architectures such as SVM, RF, and CNN dominate current studies, emerging deep learning models remain underexplored. Advanced architectures like Vision Transformers (ViTs) and Graph Neural Networks (GNNs) have shown superior capabilities in modeling complex spatial and temporal patterns in other agricultural domains [96,97]. These models offer advantages such as improved long-range dependency modeling and spatial relationship preservation, which are especially relevant for high-resolution, time-series RS data [98]. The absence of such models in the reviewed chili studies may be attributed to high computational demands, limited labeled samples, and a lack of domain adaptation strategies. Nevertheless, future research should explore their applicability, especially in combination with TL or hybrid modeling, to unlock deeper insights into smallholder chili production systems.
The application of GIS technology in chili crop monitoring and management has largely centered on spatial visualization tasks, such as delineating planting zones or mapping soil fertilizer distribution (Figure 4). However, its capability to facilitate spatial decision-making, such as integrating multi-source datasets to support planting strategies, risk evaluations, and resource allocation, remains underutilized. Numerous studies have demonstrated the value of GIS as a decision support tool in agriculture across multiple spatial scales [99]. GIS has been employed in diverse contexts by integrating spatial and non-spatial information. At broader spatial scales (e.g., national or global levels), GIS-based crop modelling is often used to assess how agricultural systems respond to environmental pressures. For instance, Liu [100] utilized the GEPIC model, which combines ArcGIS with the EPIC crop model, to explore the relationship between water management practices and global crop production. Similarly, Parry et al. [101] examined the worldwide impact of climate change on crop yields by linking geospatial datasets with the IBSNAT-ISASA crop growth model. GIS-based crop simulation studies at more minor scales (ranging from field to regional levels) typically investigate how spatial variability in factors such as soil conditions, cultivars, and management practices influences crop yield outcomes. For example, Li et al. [102] predicted and evaluated the annual yields of dominant crops with real rotation scenarios. These cases demonstrate that GIS holds significant potential beyond basic visualization tasks. Nonetheless, such integrated applications remain limited within chili crop monitoring and management, revealing a critical research gap. Advancing this field presents significant opportunities to develop integrated monitoring systems that enhance decision-making in chili production, improve resource use efficiency, and build resilience to climate variability and market fluctuations.
While GIS offers substantial advantages in spatial modeling, land suitability assessment, and integration of diverse environmental layers, it also shares several limitations with remote sensing (RS) in practical agricultural applications. Optical RS platforms are particularly susceptible to atmospheric conditions, such as cloud cover and haze [103], which can significantly reduce data availability and quality in tropical regions that are often important for chili cultivation. Additionally, freely available RS imagery (e.g., Sentinel-2, Landsat) may lack the spatial and temporal resolution necessary to detect fine-scale heterogeneity in smallholder fields [65]. In contrast, GIS-based analyses typically rely on static, infrequently updated ancillary datasets, such as administrative boundaries or historical land use layers, which may not accurately reflect current field-level dynamics [104]. Furthermore, GIS applications are prone to spatial scale mismatches and temporal misalignments when integrating multi-source datasets, leading to analytical inconsistencies [105]. These limitations indicate that neither RS nor GIS is sufficient on its own to support high-resolution, adaptive chili crop monitoring. However, their complementary capabilities offer significant potential for integration. While RS provides timely, dynamic observations of crop growth, phenology, and stress signals, GIS supports spatial reasoning, historical context, and decision-support functionalities. Future research should therefore focus on developing integrated RS–GIS frameworks that leverage the strengths of both technologies to enable more precise, scalable, and context-aware monitoring and management of chili production systems.

5. Conclusions

This systematic review highlights the application of spatial technologies in chili crop monitoring and management. Eighty-two percent of the reviewed studies employed RS, with algorithms such as SVM, CNN, and RF demonstrating high accuracy across various applications, particularly in crop mapping, accurate yield prediction, and early detection of biotic stress. Geographically, research efforts remain disproportionately concentrated in Asia, particularly in India, Indonesia, and China.
Technological diversity is evident in the use of various platforms, including satellites, UAVs, ground-based sensors, and laboratory analyses. However, multispectral and RGB sensors dominate, while other potentially valuable tools, such as hyperspectral, radar, and thermal sensors, are underexplored. Few studies have implemented multi-sensor integration, despite its demonstrated potential to enhance monitoring accuracy and robustness.
The challenge of utilizing spatial technologies in monitoring and managing chili crops at various scales, particularly for smallholder farmers, remains inadequately addressed. Fragmented landholdings, diverse cropping patterns, limited access to advanced technologies, and a lack of technical expertise typically characterize smallholder farming systems. Bridging this gap through cross-regional studies and collaborative research is essential to make spatial technology applications more globally applicable and inclusive.
Future research should focus on expanding geographic coverage, particularly to African and other underrepresented regions, and integrating GIS more comprehensively with RS technologies. The development of multi-sensor frameworks and the leveraging of advanced analytical methods for yield prediction and growth monitoring also warrant greater attention. The development of high-precision ML models for early stress detection, ensuring appropriate training, validation, and adaptation to local conditions, is crucial for maintaining model robustness and relevance. Additionally, developing lightweight, low-cost ML models adapted for smallholder farming contexts is critical to achieving broader adoption and sustainable implementation. Researchers should explore decentralized and scalable monitoring approaches tailored explicitly to fragmented, small-scale agricultural systems, with a focus on developing low-cost, user-friendly tools. Policymakers and practitioners must engage with these technological advancements to ensure their accessibility and applicability for chili farmers worldwide. Policymakers should prioritize investments in capacity-building initiatives that enhance the technical skills of agricultural stakeholders and promote the accessibility of RS and AI technologies. Future research should explore the use of emerging technologies, including IoT devices, smartphone-based sensing, and advanced satellite systems, to develop cost-effective and scalable solutions tailored to fragmented and resource-constrained farming systems.

Author Contributions

Conceptualization, Z.W. and M.A.A.; methodology, Z.W. and M.A.A.; formal analysis, Z.W.; data curation, Z.W.; writing—original draft preparation, Z.W.; writing—review and editing, M.A.A. and A.A.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Australia-Indonesia Knowledge Partnerships Platform (KONEKSI), a bilateral initiative supported by the Government of Australia through the Department of Foreign Affairs and Trade (DFAT). The project was conducted under the title “Addressing vulnerabilities and enhancing resilience in the smallholder value chains of Java’s peri-urban food supply systems”, with grant number 1447/CRG/2023/28-UQ.

Data Availability Statement

Data sharing is not applicable to this article.

Acknowledgments

We thank the reviewers for their meticulous and insightful reviews that has helped improve the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Remotesensing 17 02827 g001
Figure 1. PRISMA flow diagram.
Figure 1. PRISMA flow diagram.
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Figure 2. Chili monitoring studies grouped by year published and by publication types, from 2006 to 2024.
Figure 2. Chili monitoring studies grouped by year published and by publication types, from 2006 to 2024.
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Figure 3. Studies related to chili monitoring grouped by year published and by conservation practice type, from 2006 to 2024.
Figure 3. Studies related to chili monitoring grouped by year published and by conservation practice type, from 2006 to 2024.
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Figure 4. Geographical distribution of selected documents on RS and GIS in chili crop monitoring.
Figure 4. Geographical distribution of selected documents on RS and GIS in chili crop monitoring.
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Figure 5. Studies related to chili mapping, yield prediction, biotic stress monitoring, abiotic stress monitoring, growth and health condition monitoring, soil fertilizer assessment, land suitability, and other applied RS and GIS.
Figure 5. Studies related to chili mapping, yield prediction, biotic stress monitoring, abiotic stress monitoring, growth and health condition monitoring, soil fertilizer assessment, land suitability, and other applied RS and GIS.
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Figure 6. Summary statistics of the technological and methodological distribution across different platforms and sensors in the reviewed studies.
Figure 6. Summary statistics of the technological and methodological distribution across different platforms and sensors in the reviewed studies.
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Figure 7. Summary statistics of the technological and methodological distribution across different platforms and sensors, categorized by application type: crop mapping, biotic stress, abiotic stress, land suitability, yield prediction, growth and health condition monitoring, and others.
Figure 7. Summary statistics of the technological and methodological distribution across different platforms and sensors, categorized by application type: crop mapping, biotic stress, abiotic stress, land suitability, yield prediction, growth and health condition monitoring, and others.
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Figure 8. Usage of different types of sensors across seven categories of chili monitoring, including crop mapping, biotic stress, abiotic stress, land suitability, yield prediction, growth and health condition monitoring, and others.
Figure 8. Usage of different types of sensors across seven categories of chili monitoring, including crop mapping, biotic stress, abiotic stress, land suitability, yield prediction, growth and health condition monitoring, and others.
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Figure 9. Proportions of training data acquisition methods employed across the reviewed studies.
Figure 9. Proportions of training data acquisition methods employed across the reviewed studies.
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Figure 10. Application of 27 ML and DL models across five categories of chili monitoring, including crop mapping, biotic stress monitoring, abiotic stress monitoring, crop health condition monitoring, and others.
Figure 10. Application of 27 ML and DL models across five categories of chili monitoring, including crop mapping, biotic stress monitoring, abiotic stress monitoring, crop health condition monitoring, and others.
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Figure 11. Application of seven statistical methods across three categories of chili monitoring, including crop yield prediction, abiotic stress monitoring, and biotic stress monitoring.
Figure 11. Application of seven statistical methods across three categories of chili monitoring, including crop yield prediction, abiotic stress monitoring, and biotic stress monitoring.
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Table 1. Inclusion and exclusion criteria for paper selection.
Table 1. Inclusion and exclusion criteria for paper selection.
Inclusion CriteriaExclusion Criteria
Documents discuss and focus on the application of remote sensing and GIS in monitoring chili cropDocuments mention remote sensing and GIS technologies but do not use them as a tool to monitor chili crop
Written in EnglishNot written in English
The full document is availableReviews, commentaries, news, and project studies
Table 2. Spatial and temporal resolutions of sensors and platforms used in the reviewed studies.
Table 2. Spatial and temporal resolutions of sensors and platforms used in the reviewed studies.
PlatformSensorsSpatial ResolutionTemporal Resolution
Landsat 5TM30 m16 days
Landsat 8OLI30 m16 days
Landsat 7ETM+30 m16 days
Sentinel-2A/BMSI10 m5 days
Terra/AquaMODIS250 m1–2 days
WorldView-2WV1101.84 m1.1 days
QuickBirdQuickBird Imaging Sensor2.62 m1–3.5 days
PlanetScopePS23–5 m1 days
NOAAAVHRR11.1 km0.5 days
IRS-P6LISS-IV5.8 m5 days
EO-1Hyperion30 m16 days
Sentinel-1A/BC-SAR10 m6 days
UAVs/ground-basedRGB cameraup to centimetersFlexibility in data capture
UAVsMultispectral cameraup to centimetersFlexibility in data capture
UAVsHyperspectral cameraup to centimetersFlexibility in data capture
Ground-basedHyperspectral cameraup to centimetersFlexibility in data capture
Ground-basedASD FieldSpec Pro spectroradiometerup to centimetersFlexibility in data capture
Ground-based/UAVsThermal cameraup to centimetersFlexibility in data capture
Ground-basedIL-190 Quantumup to centimetersFlexibility in data capture
Table 3. ML and DL algorithms for chili monitoring and accuracy.
Table 3. ML and DL algorithms for chili monitoring and accuracy.
AlgorithmMean AccuracyAccuracy RangeNumber of Studies
ResNet97.41%-1
Prototypical network96.46%-1
YOLOv4 and YOLOv4 tiny model88.11%-1
CNN94.10%86–99%5
RF91.65%83.2–96%5
SVM92.08%82.69–97.32%6
Bayes77.90%-1
Geographic Object-Based model78.92%-3
TWDWS distances86.00%-1
TwDTW and twDTWS80.00%-1
SAM88.80%-2
PLSDA88.50%-1
LSSVM75.00%-1
BPNN89.74%-1
Two-dimensional local discriminant bases93.00%-1
KNN63.00%-1
RCNN89.31%82.61–96%2
ANN76.67%-1
Geographic object-based image analysis (GEOBIA)80.00%-1
Decision tree classification87.86%-1
Unsupervised classification77.00%-1
ViT-AlexNet94.80%-1
YOLOv863.8%-1
Table 4. Comparison of GIS databases used in different application categories.
Table 4. Comparison of GIS databases used in different application categories.
Application CategoryGIS DatabaseStudy Reference
Crop mappingLocation information[33]
Biotic stress[34]
Land suitability[14,35]
Crop yield prediction[14,36]
Land suitabilityLand cover or land use layers derived from google map, cadastral map, and satellite imagery (i.e., Cartosat-1 (PAN) or survey)[13,14,37]
Soil fertilizer[37,38]
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Wang, Z.; Akber, M.A.; Aziz, A.A. Application of Remote Sensing and Geographic Information Systems for Monitoring and Managing Chili Crops: A Systematic Review. Remote Sens. 2025, 17, 2827. https://doi.org/10.3390/rs17162827

AMA Style

Wang Z, Akber MA, Aziz AA. Application of Remote Sensing and Geographic Information Systems for Monitoring and Managing Chili Crops: A Systematic Review. Remote Sensing. 2025; 17(16):2827. https://doi.org/10.3390/rs17162827

Chicago/Turabian Style

Wang, Ziyue, Md Ali Akber, and Ammar Abdul Aziz. 2025. "Application of Remote Sensing and Geographic Information Systems for Monitoring and Managing Chili Crops: A Systematic Review" Remote Sensing 17, no. 16: 2827. https://doi.org/10.3390/rs17162827

APA Style

Wang, Z., Akber, M. A., & Aziz, A. A. (2025). Application of Remote Sensing and Geographic Information Systems for Monitoring and Managing Chili Crops: A Systematic Review. Remote Sensing, 17(16), 2827. https://doi.org/10.3390/rs17162827

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