Remote sensing and automated monitoring systems (AMS) for Insect pest detection and surveillance (IDPS)

Introduction

Insect pests pose a serious challenge to agriculture, leading to substantial reductions in crop yields and significant economic losses globally. Annual crop losses attributed to these pests are estimated at 20-30% (Oerke, 2006) [29] . To reduce damage and support food security, it is essential to monitor and manage pest populations effectively. Conventional approaches, such as field inspections and the use of sticky traps, tend to be labor-heavy, slow, and often unable to deliver prompt or precise data on pest activity (Pimentel, 2009) [30] . Additionally, their limited spatial reach and detail make large-scale pest detection and tracking difficult. Recent progress in remote sensing and automated monitoring has created new opportunities for early pest detection in farming systems. Technologies like satellite imaging, drones, and hyperspectral sensors allow for non-intrusive, wide-area assessment of both crop conditions and pest outbreaks (Zhang et al., 2019) [28] . Meanwhile, automated tools such as wireless sensor networks, sound-based detection systems, and computer vision enable real-time, continuous observation of pest numbers and behaviors (Jiang et al., 2018) [31] . The combination of remote sensing, automated monitoring systems, and precision agriculture technologies along with decision support tools holds significant promise for transforming pest management approaches. Precision agriculture relies on advanced tools such as global positioning systems (GPS), geographic information systems (GIS), and variable rate application (VRA) to enhance crop productivity and improve resource efficiency (Gebbers & Adamchuk, 2010) [32] . When data from remote sensing and automated monitoring with these technologies, farmers can implement more precise pest control measures, minimising dependence on broad-spectrum pesticides and supporting more sustainable practices. This paper offers a detailed review of current remote sensing and automated monitoring methods used for detecting and tracking insect pests. It examines the underlying principles, real-world applications, benefits, and constraints of these technologies. Examples of successful deployments in managing key insect pests are included to illustrate practical outcomes. The discussion also covers how these monitoring systems can be effectively combined with precision agriculture and decision-making tools. Lastly, the paper identifies existing challenges and suggests future research priorities for creating monitoring solutions that are affordable, scalable, and dependable.

Remote sensing techniques for insect pest detection

Remote sensing has become an effective approach for tracking crop conditions and identifying insect pest outbreaks across different spatial and temporal scales. It relies on collecting and interpreting data from sensors carried by satellites, aircraft, or unmanned aerial vehicles (UAVs). The combination of spectral, spatial, and temporal detail in these data enables the recognition of minor changes in plant health and helps pinpoint areas affected by pests (Zhang et al., 2019). This section outlines the underlying principles, practical uses, benefits, and constraints of using satellite imagery, UAVs, and hyperspectral imaging in detecting insect pests. Satellite imagery is commonly used to monitor crop conditions and identify insect pest outbreaks across broad regional or global areas. Sensors like Landsat, MODIS, and Sentinel capture multispectral data with differing spatial and temporal resolutions (Zhang et al., 2019). Their spectral bands, especially in the visible and near-infrared ranges, respond to variations in plant health and support the calculation of vegetation indices such as NDVI and EVI (Huang et al., 2018). These tools are useful for pest detection because crops affected by insects often display unique spectral patterns compared to healthy ones. For example, wheat fields infested with aphids tend to show reduced NDVI values due to lower chlorophyll levels and diminished leaf area (Mulla et al., 2017). Likewise, infestations of red palm weevil (Rhynchophorus ferrugineus) in date palms have been identified through satellite observations of altered spectral reflectance in affected trees (Javeedalee et al., 2019). A key strength of satellite monitoring is its capacity to cover extensive regions and deliver repeated observations, enabling tracking of pest spread and pinpointing areas needing intervention. However, limitations exist spatial resolution may be too coarse to detect minor infestations or individual pests, and factors like cloud cover, atmospheric interference, and canopy structure can affect detection accuracy (Zhang et al., 2019). Despite these challenges, several studies highlight successful applications. Mulla et al. (2017) used Landsat data to identify Russian wheat aphid (Diuraphis noxia) outbreaks in U.S. wheat fields, noting significantly lower NDVI in infested areas, suggesting promise for early detection. Similarly, (Javeedalee et al., 2019) applied SPOT-6 imagery to detect red palm weevil in UAE date palm plantations, creating a spectral index using red and near-infrared bands that effectively distinguished healthy from infected trees [28] .

Satellite Imagery

Satellite imagery is commonly employed to assess crop conditions and identify insect pest outbreaks across broad regional or global areas. Sensors like those on Landsat, MODIS, and Sentinel satellites capture multispectral data with differing spatial and temporal resolutions (Zhang et al., 2019). These sensors detect light in the visible and near-infrared ranges, which are particularly responsive to variations in plant health. This information supports the calculation of vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI), both widely used indicators of vegetation status (Huang et al., 2018). Detecting insect pests via satellite relies on the fact that affected crops often display unique spectral patterns compared to healthy ones. For example, wheat fields infested with aphids tend to show reduced NDVI values due to diminished chlorophyll levels and leaf coverage (Mulla et al., 2017) [28, 33, 34, 39] . Likewise, infestations of red palm weevil (Rhynchophorus ferrugineus) in date palms have been identified by analyzing shifts in the spectral reflectance of infected trees (Javeedalee et al., 2019). A key strength of satellite-based monitoring is its capacity to survey vast areas repeatedly over time, enabling tracking of pest spread and pinpointing high-risk zones for focused intervention. However, limitations exist such as insufficient resolution for spotting small or isolated infestations and factors like cloud cover, atmospheric interference, and canopy complexity can affect detection accuracy (Zhang et al., 2019). Numerous studies highlight successful applications of this technology. (Mulla et al. 2017) utilized Landsat data to monitor Russian wheat aphid (Diuraphis noxia) outbreaks in U.S. wheat fields, observing notably lower NDVI readings in affected areas, suggesting promise for early detection. In another example, (Javeedalee et al. 2019) applied SPOT-6 imagery to identify red palm weevil damage in date palm groves in the United Arab Emirates. They created a spectral index using red and nearinfrared bands that effectively distinguished between healthy and infested trees [28, 33, 34] .

Unmanned Aerial Vehicles (UAVs)

Unmanned aerial vehicles (UAVs), commonly referred to as drones, have become increasingly valuable for monitoring insect pests in agriculture with high resolution and adaptability. These systems can carry a range of sensors such as RGB, multispectral, and thermal cameras to collect detailed images of crop canopies and individual plants (Maes & Steppe, 2019). The low-altitude flights and fine spatial resolution (down to the centimeter scale) of UAV imagery make it possible to spot early signs of pest activity and even identify specific insect species. UAVs used in pest monitoring generally fall into two categories: fixed-wing and rotary-wing models. Fixed-wing drones offer longer flight durations and are better suited for covering large agricultural areas efficiently. In contrast, rotary-wing drones like quadcopters and hexacopters provide superior maneuverability and the ability to hover, allowing for close-up observations of particular plants or localized infestations (Maes & Steppe, 2019). The choice of sensor depends on the pest and crop being monitored. RGB cameras are widely used to visually assess damage and identify pest life stages, such as eggs, larvae, or adults. Multispectral sensors capture data across several wavelengths, enabling the calculation of vegetation indices that reveal changes in plant health caused by pests. Thermal sensors detect temperature differences in crops, which can signal stress or disease resulting from pest attacks (Maes & Steppe, 2019). Processing UAV data typically involves image pre-processing (e.g., orthorectification, mosaicking, radiometric calibration) to ensure consistency (Zhang et al., 2019), followed by feature extraction methods like texture analysis, edge detection, or objectbased image analysis (OBIA) to locate affected areas. Machine learning techniques such as support vector machines (SVM), random forests (RF), and convolutional neural networks (CNN) are then applied to classify and map infestations based on these features (Ghosh et al., 2021). Several real-world applications highlight the effectiveness of UAVs in pest monitoring. (Hoffmann et al. 2021), for instance, employed RGB imagery from drones to detect European corn borer (Ostrinia nubilalis) infestations in maize, using a CNN model to distinguish between healthy and damaged plants with high accuracy. Similarly, Kalischuk et al. (2022) utilized multispectral drone data to track wheat stem sawfly (Cephus cinctus) infestations in wheat fields, showing that indices like Normalized Difference Red Edge (NDRE) could effectively differentiate infested from healthy plants. Key benefits of UAVbased pest monitoring include fine image detail, operational flexibility, and rapid coverage of extensive areas, supporting timely and precise pest management. However, challenges remain, including operational expenses, regulatory constraints, and the complexity of processing large volumes of image data (Maes & Steppe, 2019) [28, 35, 36] .

Hyperspectral and Multispectral Imaging

Hyperspectral and multispectral imaging have demonstrated significant promise in identifying insect pest infestations by analyzing the unique spectral signatures of affected plants.

Hyperspectral imaging collects data across hundreds of narrow, contiguous wavelength bands, offering detailed spectral profiles of plant conditions. In contrast, multispectral imaging uses a smaller number of bands usually between four and ten carefully chosen to enhance the ability to distinguish specific features (Zhang et al., 2019). These imaging methods work on the principle that plants under pest stress display measurable changes in their spectral reflectance compared to healthy ones. Such differences arise from alterations in leaf pigments, water content, and internal cell structure caused by feeding or infection (Huang et al., 2018). For example, wheat leaves damaged by aphids show reduced reflectance in the near-infrared range and increased reflectance in the visible spectrum relative to uninfested leaves (Yuan et al., 2014). These imaging techniques are applied using ground-based sensors, drones, or satellites to capture high-resolution spectral data from crop canopies. The collected data is then analyzed using tools like vegetation indices, spectral angle mapping, and machine learning models to detect and map areas affected by pests (Zhang et al., 2019). A key challenge lies in identifying the most effective spectral bands or indices that respond specifically to certain pests or diseases, which requires in-depth knowledge of both pest biology and host plant characteristics. Additionally, the large volume of data generated by hyperspectral systems [28, 38, 39] 2019) [28, 38, 39] .

Automated monitoring systems for insect pest surveillance

Automated monitoring systems have become a valuable solution for continuously tracking insect pest populations in agriculture, offering real-time surveillance capabilities. These systems rely on a range of technologies including wireless sensor networks, acoustic sensors, and computer vision to gather data on pest behavior, activity levels, and surrounding environmental factors. By applying sophisticated algorithms to this data, it becomes possible to generate practical insights that support more informed pest management strategies. This section explores the underlying principles, practical uses, benefits, and constraints associated with these three key technologies in the context of monitoring agricultural pests. [2, 16, 31] .

Acoustic Detection Systems

Acoustic detection systems have become a promising method for monitoring insect pests by capturing and analyzing their species-specific sounds and vibrations. Numerous insect species generate unique acoustic signals during activities such as feeding, mating, or communication, which can be recorded using microphones or piezoelectric sensors (Mankin et al., 2011). These systems work by collecting sound data, then applying signal processing and pattern recognition techniques to identify pest species and assess their population levels (Koubaa et al., 2021). This technology has been successfully applied in various agricultural settings, including grain storage units, orchards, and vineyards. For instance, pests like the rice weevil (Sitophilus oryzae) and the red flour beetle (Tribolium castaneum) have been detected in stored grain by recording the sounds they produce while feeding and moving (Mankin et al., 2011). In citrus orchards, acoustic sensors have also proven effective in identifying the Asian citrus psyllid (Diaphorina citri), a key carrier of citrus greening disease (Mankin et al., 2019) [10, 11] .

A crucial aspect of these systems is the development of sound recognition algorithms. [7, 11, 13] .

Computer Vision and Machine Learning Techniques

Computer vision and machine learning techniques have gained significant attention for automated insect pest monitoring in recent years. These techniques involve the acquisition of digital images or videos of insect pests using cameras or smartphones, followed by image processing and analysis using advanced algorithms (Biswas et al., 2022). The principles of computer vision for pest detection rely on the extraction of visual features, such as color, shape, texture, and motion, from the images or videos, which are then used to identify and classify the insect pests (Kour et al., 2022). Image processing algorithms for pest detection typically involve a series of steps, including image pre-processing, segmentation, feature extraction, and classification. Image pre-processing techniques, such as noise reduction, contrast enhancement, and color space transformation, are applied to improve the quality and consistency of the images (Biswas et al., 2022). Image segmentation algorithms, such as [1, 8] machine learning techniques for insect pest monitoring in various crop systems. For instance, (Nath et al. 2021) developed a CNN-based system for the detection and classification of the rice brown planthopper (Nilaparvata lugens) in rice fields. The system achieved an accuracy of 98.7% in classifying the planthopper from field-captured images, outperforming traditional machine learning algorithms such as SVM and RF. Similarly, (Kour et al. 2022) used a combination of image processing and machine learning techniques to detect and classify the damage caused by the tomato leafminer (Tuta absoluta) in tomato plants. The system achieved an accuracy of 96.3% in classifying the damage levels based on the extracted color and texture features. The main advantages of computer vision and machine learning techniques for insect pest monitoring include their high accuracy, automation potential, and scalability. These techniques can process large volumes of image data and provide real-time information on pest infestations, enabling timely and targeted control measures (Biswas et al., 2022). However, the performance of these techniques depends on the quality and diversity of the training data, the selection of appropriate features and algorithms, and the computational resources available (Kour et al., 2022) [1, 8, 12] . [5, 9, 27, 28, 31] . Combining remote and automated monitoring with emerging innovations like artificial intelligence, big data analytics, and cloud computing holds strong potential to enhance system performance, flexibility, and decision-making accuracy (Biswas et al., 2022). Another critical area for advancement is the creation of integrated monitoring frameworks that operate across multiple scales, incorporate various sensors, and track multiple pest species simultaneously, enabling a more complete understanding of pest dynamics and crop status (Yao et al., 2022). Embedding ecological knowledge such as interactions between pests and crops, populations of natural predators, and landscape-level influences can strengthen the predictive ability of these systems and support more sustainable pest control strategies (Fleisher et al., 2020). Additionally, rigorous testing and validation of these technologies across diverse farming systems and environmental conditions are necessary to evaluate their reliability, resilience, and applicability in different contexts (Zhang et al., 2019). Building collaborative research networks, shared data infrastructures, and public-private partnerships will be key to advancing, evaluating, and deploying these tools among varied users and regions (Magarey et al., 2018) [1, 3, 9, 27, 28, 31] .

Conclusion:

Remote sensing and automated monitoring have become valuable tools for detecting and tracking insect pests in agriculture. Technologies such as satellite imaging, drones, wireless sensor networks, and computer vision enable noninvasive, real-time, and detailed observation of pest activity and crop conditions. When combined with precision farming methods and decision-support systems, these tools can transform pest management by minimizing losses, boosting yields, and supporting sustainable practices. Yet, their broader application faces obstacles, including technical constraints, difficulties in managing and integrating data, limited user adoption, and the need for consistent validation across varied field environments.

Ongoing research should aim to overcome these barriers by creating affordable, dependable, and easyto-use sensors and platforms, incorporating innovations like artificial intelligence and big data analytics, and fostering collaborative research and data-sharing initiatives. Effectively deploying these systems requires interdisciplinary collaboration among entomologists, plant pathologists, agronomists, computer scientists, and engineers. Combining biological and ecological insights with advanced monitoring capabilities is essential for building effective and sustainable pest control strategies. Leveraging these technologies can strengthen agricultural resilience, improve productivity, safeguard food supplies, and contribute to environmental protection for future generations.

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