1 Introduction

Tea plants are an essential crop, especially in Asia, where tea production plays a significant economic and cultural role. However, tea plantations are highly susceptible to various pests and diseases that can drastically reduce yield and quality. Common pests, such as tea looper and tea aphids, and diseases like tea blister blight can lead to significant economic losses. Traditionally, pest management in tea plantations has relied heavily on the use of chemical pesticides. While these chemicals can effectively reduce pest populations, they pose considerable risks to the environment, such as soil and water contamination, and can lead to pesticide resistance over time. This makes pest control less effective and increases the cost of tea production due to the need for higher pesticide usage. Moreover, excessive pesticide application can result in harmful residues on tea leaves, which poses a risk to consumer health. As awareness grows regarding sustainable agricultural practices, there is a shift toward more environmentally friendly and effective pest management approaches. Tea growers are increasingly adopting Integrated Pest Management (IPM) strategies, which combine biological, mechanical, and chemical methods to control pests while minimizing environmental impacts. However, even IPM can be resource-intensive without the aid of advanced technologies. This is where Precision Pest Management (PPM) enters the picture, offering a more targeted and efficient approach to tea plant protection (Koshariya et al., 2023).

Precision Pest Management (PPM) has emerged as a vital component of modern agriculture. It allows farmers to control pests more effectively by using data-driven insights and real-time monitoring tools. Unlike traditional methods that apply broad-spectrum pesticides across large areas, PPM targets specific infestations in precise locations and at the optimal time, thus reducing unnecessary pesticide use and minimizing environmental impacts. This method is especially beneficial in crops like tea, where consumer health and safety are paramount, as tea leaves are directly consumed. By using PPM, tea growers can ensure that the plants receive just the right amount of treatment at the right time, optimizing resource usage while maintaining product quality. PPM also contributes to economic sustainability by reducing input costs, such as pesticides and labor, while increasing the overall efficiency of pest control measures. The ability to make informed decisions based on real-time data helps prevent crop losses, which is crucial in high-value crops like tea. Additionally, PPM can help mitigate the issue of pesticide resistance, a growing concern in agricultural pest management, by allowing for more strategic and varied application of pest control measures. In essence, PPM is not just about pest control; it represents a comprehensive approach to sustainable agriculture that protects crops, preserves the environment, and ensures economic viability (Ennouri et al., 2019).

The integration of the Internet of Things (IoT) and remote sensing technologies has revolutionized agriculture, particularly in the realm of precision farming. IoT refers to a network of physical devices, such as sensors and cameras, connected to the internet, enabling real-time data collection and transmission. In agriculture, these devices can monitor critical factors like soil moisture, temperature, humidity, and pest activity. Farmers can access this information remotely, allowing for continuous monitoring and timely interventions. For instance, soil sensors can trigger automated irrigation systems, while pest detection cameras can alert farmers to potential infestations, enabling quick responses to prevent widespread damage (Sawant et al., 2017). Remote sensing, on the other hand, involves the use of satellite or drone-based imaging to monitor crops over large areas. This technology can detect early signs of pest infestation or stress in plants by analyzing changes in color, temperature, and other factors invisible to the naked eye. By combining data from IoT devices with high-resolution remote sensing imagery, farmers can make more informed decisions regarding pest management, irrigation, and crop health. These technologies not only reduce the need for manual inspections, which are time-consuming and labor-intensive, but also allow for more precise application of pesticides and fertilizers, further enhancing sustainability (Filho et al., 2019).

This study explores the use of Internet of Things (IoT) and remote sensing technologies in improving Precision Pest Management (PPM) for tea plantations. The main objective is to evaluate how these technologies can enhance pest control strategies, reduce pesticide use, and support sustainable agricultural practices. IoT devices enable real-time monitoring of pest activity and crop health, while remote sensing technologies, such as drones and satellites, provide large-scale monitoring and early detection of pest infestations. The key objectives include assessing the effectiveness of IoT-based pest monitoring systems in tea plantations, analyzing how these technologies can reduce pesticide use through targeted interventions, and evaluating the economic benefits for tea growers, such as cost savings and improved yields. By focusing on real-time data and precision interventions, this study aims to offer practical insights for tea farmers to adopt advanced technologies, ultimately demonstrating how precision agriculture can enhance both productivity and sustainability in tea farming.

2 IoT in Tea Plant Protection

2.1 Sensors for pest detection

In the context of tea plant protection, sensors play a pivotal role in detecting pest infestations early, thus enabling timely interventions. IoT-based sensors can monitor various environmental factors and insect activities in tea plantations. These sensors include motion detectors, image-based sensors, and multispectral imaging systems, which provide accurate and continuous data on pest populations. For example, camera-equipped traps can capture high-resolution images of insects, which are then processed through machine learning algorithms for identification and classification. This allows for real-time detection of pest outbreaks, which is critical for minimizing damage to crops and reducing the need for widespread pesticide applications (Chen and zhao, 2024).

The use of AI in conjunction with sensors enhances the precision of pest identification. Advanced systems are now able to differentiate between species of pests, thus allowing for targeted treatment rather than broad-spectrum pesticide use. A comparative study of camera- and sensor-based traps showed that integrating sensors with wireless communication technologies significantly improves detection accuracy and operational efficiency in pest management (Passias et al., 2023). Moreover, using multispectral imaging helps in detecting smaller and camouflaged pests, which are often missed by traditional monitoring techniques. By enhancing early pest detection, sensor-based systems reduce crop losses and pesticide reliance, contributing to more sustainable tea cultivation practices.

2.2 Real-time monitoring and data collection

Real-time monitoring enabled by IoT technologies is critical in modern tea plant protection strategies. In tea plantations, IoT-based sensors collect continuous data on environmental conditions, such as humidity, temperature, and soil moisture, as well as pest activity. This data is transmitted wirelessly to cloud platforms, where it is analyzed in real time to provide actionable insights to farmers. For example, in Indonesia’s tea plantations, a smart farming system that utilizes LoRa technology allows for efficient wireless data transmission over long distances, ensuring that farmers in remote areas can still monitor and manage their crops in real-time (Thereza et al., 2020).

Real-time data collection significantly enhances the decision-making process by providing immediate feedback on pest outbreaks, weather conditions, and crop health. Farmers can use this information to take swift action, such as adjusting pesticide application rates or improving irrigation practices, thus preventing extensive crop damage. The ability to access this data remotely, often via mobile applications, adds a layer of convenience and efficiency, enabling farmers to make decisions without being physically present in the fields. By integrating data from multiple sensors, tea plantations can optimize their resource use, reduce labor costs, and ensure timely interventions, ultimately improving both productivity and sustainability (Huang, 2024).

2.3 Integration of IoT with field management practices

Integrating IoT technologies with traditional field management practices offers significant advantages in managing tea plantations. IoT systems not only enhance pest monitoring but also streamline other critical aspects of plantation management, such as irrigation, fertilization, and overall crop health monitoring. By connecting various sensors and devices, such as soil moisture sensors, weather stations, and pest detection systems, IoT enables automated responses to field conditions. For instance, IoT systems can trigger drones to apply pesticides or adjust irrigation levels based on real-time data, reducing manual intervention and improving efficiency (Azfar et al., 2023).

The integration of IoT into field management practices allows tea farmers to practice precision agriculture, where inputs such as water, fertilizers, and pesticides are applied only when and where they are needed. This reduces wastage, lowers costs, and minimizes environmental impact. Additionally, IoT systems can collect historical data, which helps in predictive analytics, allowing farmers to anticipate pest outbreaks or environmental stresses. By integrating IoT with field management, tea plantations can achieve higher yields, better quality crops, and more sustainable operations. This holistic approach to farm management is particularly beneficial for tea, where the quality and timing of interventions can significantly affect the final product.

3 Remote Sensing Technologies in Tea Plant Protection

Remote sensing technologies, particularly satellite and drone-based imaging, have become invaluable tools in modern tea plant protection. By offering a non-invasive, real-time means of monitoring crop health and pest activity, these technologies enable more efficient and sustainable pest management. Through the use of advanced spectral analysis, remote sensing helps in early pest detection and targeted interventions, thus reducing the dependency on chemical pesticides.

3.1 Satellite and drone imaging for pest detection

Satellite and drone imaging technologies have proven to be highly effective in detecting pest infestations in tea plantations. Drones, in particular, offer flexibility and high-resolution imaging, allowing farmers to monitor large areas of crops with great precision. UAVs equipped with multispectral and hyperspectral cameras provide detailed images that can reveal subtle changes in plant health, which may indicate the presence of pests. For instance, a UAV-based imaging system was successfully used to detect early-stage infestations by analyzing the reflectance patterns of tea leaves affected by pests like the tea leaf blight (Hu et al., 2024).

Moreover, satellite imagery, while covering larger areas than drones, can be useful for monitoring tea plantations over time and detecting large-scale pest outbreaks. Satellites can capture images at different spectral bands, which makes it easier to identify areas of stress caused by pests, even from a great distance. A study comparing UAV and satellite imagery highlighted that while UAVs offer higher resolution, satellite images are better suited for covering larger areas, making them ideal for monitoring pest outbreaks across vast tea plantations (Müllerová et al., 2017).

3.2 Multi-spectral and hyper-spectral imaging applications

Multispectral and hyperspectral imaging are essential tools in remote sensing for pest management. Multispectral imaging captures data at a few broad spectral bands, while hyperspectral imaging collects data across hundreds of narrow spectral bands, providing more detailed information. These technologies are particularly useful in identifying specific stress indicators in tea plants caused by pest infestations.

Hyperspectral sensors, mounted on UAVs or satellites, are capable of detecting minute differences in plant reflectance, which can signal the early stages of pest infestations. In vineyards, for example, hyperspectral imaging has been used to monitor pest-related stress with high accuracy, and similar methodologies can be applied to tea plantations (Vanegas et al., 2018). Additionally, hyperspectral imaging has been shown to outperform traditional RGB or NIR sensors by providing more detailed spectral data, making it easier to differentiate between healthy and pest-infested plants. These imaging techniques not only help in pest detection but also assist in precision pesticide application, ensuring that chemicals are applied only where necessary, thus reducing environmental impact and costs (Adão et al., 2017).

3.3 Analyzing vegetation indices to assess pest infestation

Vegetation indices derived from remote sensing data are powerful tools for assessing plant health and detecting pest infestations. Indices such as the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) are commonly used to monitor changes in plant biomass and vigor, which can indicate the presence of pests. For instance, lower NDVI values typically correspond to areas of poor plant health, potentially caused by pest damage.

The use of vegetation indices is particularly effective in large-scale tea plantations, where manual monitoring of every plant is impractical. A study using hyperspectral data to monitor rice plants infested with brown planthoppers demonstrated that specific vegetation indices could accurately reflect the severity of the infestation (Tan et al., 2019). Similarly, in tea plantations, these indices help farmers identify hotspots of pest activity, enabling them to focus their pest control efforts precisely where needed. By analyzing the spectral signatures of healthy versus pest-infested plants, vegetation indices provide an efficient means of monitoring crop health over time. This allows tea growers to implement more proactive pest management strategies, ultimately improving crop yield and quality.

4 Synergistic Use of IoT and Remote Sensing

4.1 Combining data from iot sensors and remote sensing

Combining IoT sensors and remote sensing technologies allows for comprehensive, multi-dimensional data collection in tea plantations, offering real-time insights into environmental conditions and crop health. IoT sensors placed within the fields continuously monitor factors such as soil moisture, temperature, humidity, and light intensity, which are critical for understanding the overall health of the tea plants. These sensors can detect subtle changes in the environment that might suggest the early onset of pest activity, such as prolonged dry conditions that could stress plants and make them more susceptible to pests. Meanwhile, remote sensing technologies, particularly drones and satellites equipped with multispectral or hyperspectral imaging, provide large-scale monitoring of plant health and pest outbreaks. These imaging technologies can detect changes in leaf color, chlorophyll levels, and other physiological indicators that are often early signs of pest infestations.

The integration of these data sources enhances the ability of farmers to monitor their crops with a high degree of precision. For instance, IoT sensors might detect localized changes in soil moisture, while drone imagery could reveal pest hotspots based on changes in vegetation indices, such as the Normalized Difference Vegetation Index (NDVI). This combination of ground-based and aerial data allows for a more accurate diagnosis of potential threats to the tea plants, facilitating early interventions and minimizing crop losses. Additionally, this data fusion allows for continuous monitoring, reducing the reliance on manual inspections and enabling more efficient resource use (Figure 1) (Thereza et al., 2020).

Figure 1 Scheme of monitoring system design (Adopted from Thereza et al., 2020)

4.2 Data-driven decision making in pest control

Data-driven decision-making is a key benefit of integrating IoT and remote sensing technologies in pest control. The real-time data collected from IoT sensors and remote sensing tools enable farmers to make informed decisions based on current conditions in the field, rather than relying on generalized assumptions or outdated information. This approach allows for predictive pest management, where patterns and trends identified in the data can inform decisions before significant damage occurs. For example, if IoT sensors detect rising temperatures and decreasing humidity, combined with remote sensing data showing changes in leaf color indicative of stress, farmers can preemptively apply treatments or adjust irrigation before pest populations proliferate.

4.3 Advantages of integrated technologies

The integration of IoT and remote sensing technologies offers numerous advantages for precision pest management in tea plantations. One of the most significant benefits is the ability to detect pest infestations early and intervene before they cause widespread damage. Remote sensing technologies, such as drones equipped with multispectral cameras, can survey large areas quickly, identifying pest hotspots by detecting changes in plant health. IoT sensors provide complementary ground-level data, such as soil moisture or temperature, which can be crucial for understanding the conditions that may lead to pest outbreaks. This integrated approach allows for more precise and targeted pest control measures, reducing the need for broad-spectrum pesticide applications that can harm the environment and lead to pesticide resistance in pests. UAVs equipped with multispectral cameras offer rapid and accurate monitoring of crop health and pest infestations, enabling more effective pest control while reducing resource wastage and improving the overall efficiency of farm management (Modica et al., 2020).

Moreover, the integration of these technologies leads to cost savings and improved efficiency. Automation plays a key role, as IoT sensors and drones can monitor the plantation autonomously, reducing the need for manual inspections. In one study, the combination of IoT and drone technology in agricultural management reduced labor costs by automating tasks such as pesticide application and field monitoring (Sarangi et al., 2020). The ability to apply pesticides or other treatments only where and when they are needed also reduces resource wastage, lowering input costs and minimizing environmental impact. In addition, the data collected through these technologies can be stored and analyzed over time, helping farmers to identify long-term trends and optimize their pest management strategies for future growing seasons. By integrating IoT and remote sensing, tea plantations can achieve more sustainable, cost-effective, and efficient pest management. The integration of UAV-based hyperspectral imaging with IoT systems allows for precise monitoring of crop health and pest presence, which helps farmers make data-driven decisions and reduces labor costs through automation (Vanegas et al., 2018).

5 Benefits and Challenges of Precision Pest Management in Tea Cultivation

Precision Pest Management (PPM) using IoT and remote sensing technologies offers significant advantages in enhancing tea cultivation practices. However, these benefits come with technological and practical challenges that need to be addressed for broader adoption and successful implementation.

5.1 Increased efficiency in pest management

Precision Pest Management (PPM) significantly enhances the efficiency of pest control in tea cultivation by using data-driven technologies. Traditional pest management often involves the blanket application of pesticides across entire fields, leading to inefficiencies such as overuse or underuse of chemicals, increased labor costs, and potential harm to non-target species. In contrast, PPM employs IoT sensors, remote sensing, and advanced analytics to monitor pest populations and environmental conditions in real time. This allows for precise identification of pest hotspots, enabling targeted interventions rather than treating the entire plantation indiscriminately. For instance, UAVs equipped with multispectral imaging can scan large tea fields to detect stressed plants, which may indicate pest activity. This data, combined with on-the-ground sensor inputs, helps farmers apply pesticides only where needed, drastically reducing chemical usage and labor costs. Precision agriculture uses multispectral and hyperspectral imaging from UAVs to detect crop stress and pest infestations with high accuracy, allowing farmers to apply pesticides only in affected areas, reducing chemical usage and labor costs (Deng et al., 2018). These technologies enhance decision-making by providing detailed, real-time data on crop health.

Research on UAV-based remote sensing for precision pest management in vineyards demonstrated significant improvements in detecting pest hotspots, allowing for timely interventions and reduced chemical input, a practice that can be directly applied to tea plantations (Maes and Steppe, 2019). Furthermore, the real-time nature of IoT sensors allows for quicker responses to pest outbreaks, minimizing crop damage. For example, in a case study using UAV-assisted detection of red spider mites in tea, farmers were able to rapidly assess the severity of infestations and apply treatments accordingly, resulting in both labor savings and improved pest control outcomes (Choudhury et al., 2022). By focusing resources only where needed, PPM improves both the economic efficiency and environmental sustainability of tea farming, ensuring healthier crops and more consistent yields.

5.2 Economic and environmental benefits

The economic and environmental benefits of precision pest management are significant, particularly in the context of tea cultivation. From an economic perspective, PPM reduces the costs associated with pesticide use, labor, and crop losses. Targeted application of pesticides, as facilitated by PPM, means farmers use fewer chemicals, leading to direct cost savings. Furthermore, the efficiency gains from real-time monitoring systems reduce the need for frequent manual inspections and interventions, cutting down on labor costs. Studies in ecological pest management have shown that these methods can enhance both the quantity and quality of tea yield. For example, ecological pest management practices in China led to an increase in economic return of nearly 25% compared to conventional methods, adding roughly $2,000 more per hectare per year (Zheng et al., 2022). Precision farming reduces input costs by minimizing chemical usage and lowering labor requirements, leading to substantial economic benefits. Targeted pesticide applications and real-time monitoring result in direct cost savings for tea farmers, as they can limit pesticide use to specific areas where it is needed. This not only cuts costs but also increases yields by improving crop health (Finger et al., 2019).

A study in Shaoxing, China, showed that replacing chemical fertilizers and reducing pesticide use through ecological pest management increased tea yield and quality while reducing environmental pollution. Proper organic fertilizer and pesticide reduction management helped mitigate soil acidification and nutrient runoff, contributing to both environmental sustainability and higher economic returns (Xie et al., 2018). In terms of environmental benefits, PPM minimizes the negative impacts of chemical use on the ecosystem. By reducing the volume of pesticides applied, PPM helps to decrease soil and water contamination, which is particularly important in sensitive tea-growing regions where runoff can damage local waterways. Additionally, lower pesticide usage helps maintain the biodiversity of beneficial insects, such as natural predators of tea pests, which further supports long-term sustainable pest control. The reduction in chemical inputs also aligns with the growing demand for organic or low-residue tea products in international markets, potentially allowing farmers to achieve higher prices for their crops while meeting environmental sustainability goals.

5.3 Technological and practical challenges

While PPM offers numerous benefits, its adoption in tea cultivation is not without challenges. One of the primary obstacles is the high cost of technology. IoT sensors, drones, and remote sensing equipment represent significant upfront investments, which may be prohibitive for small-scale tea farmers who dominate the industry in countries like India and China. Furthermore, the ongoing costs of maintaining these systems and analyzing the data they produce can also be a barrier, particularly in regions with limited access to technical support and infrastructure. Training and education are crucial in overcoming this challenge, as many farmers may lack the necessary skills to operate these advanced technologies effectively. Without adequate support and training, even well-funded projects may fail to achieve their full potential (Finger et al., 2019).

Another challenge is the variability of environmental conditions and pest behavior across different tea-growing regions. Precision pest management systems must be tailored to the specific conditions of each plantation, which can vary greatly in terms of climate, soil type, and pest populations. This variability complicates the standardization of PPM practices and increases the complexity of implementation. Furthermore, there can be resistance from farmers who are accustomed to traditional methods and may be reluctant to adopt new technologies. Overcoming these challenges will likely require government support, in the form of subsidies for technology adoption, and ongoing research into region-specific solutions. Despite these hurdles, the long-term benefits of PPM, both economic and environmental, make it a promising approach for the future of sustainable tea cultivation.

6 Case Study

6.1 Overview of the selected region/tea plantation

The selected tea plantation for this case study is located in Assam, northeast India, a region renowned for its large-scale tea production. The humid, tropical climate of Assam is conducive to tea cultivation but also creates ideal conditions for pest outbreaks, particularly pests such as the tea mosquito bug (Helopeltis theivora), red spider mites, and aphids. The plantation, spanning approximately 1 500 hectares, has traditionally relied on conventional pesticide applications to control pests. However, growing concerns over pesticide resistance, environmental degradation, and the need for sustainable practices prompted the shift toward precision pest management (PPM) (Filho et al., 2019).

In this plantation, IoT and remote sensing technologies were integrated to offer real-time monitoring and targeted pest control. The management team installed IoT sensors to measure environmental factors such as temperature, humidity, and soil moisture, while drones equipped with multispectral cameras performed periodic flyovers to capture high-resolution imagery of the crops. These technologies allowed the plantation managers to identify pest hotspots more accurately and respond in a timely manner, reducing the need for blanket pesticide applications and contributing to both environmental sustainability and economic efficiency (Figure 2) (Vanegas et al., 2018).

Figure 2 Generated orthomosaic of studied sites; two vineyards in the Yarra valley, Victoria, Australia (Adopted from Vanegas et al., 2018)

6.2 Implementation of IoT and remote sensing for pest control

The implementation of IoT and remote sensing technologies involved a multi-step process. First, environmental monitoring stations equipped with IoT sensors were strategically placed throughout the plantation to continuously track climate conditions and soil moisture, key factors influencing pest behavior. Drones were deployed to capture multispectral images of the plantation at regular intervals, focusing on detecting early signs of pest infestations through vegetation indices such as the Normalized Difference Vegetation Index (NDVI) (Li et al., 2022). These indices revealed areas of stress in the tea plants, which often indicated pest presence.

The data from IoT sensors and drones were integrated into a cloud-based platform that used machine learning algorithms to predict potential pest outbreaks based on historical data and real-time environmental conditions. When the system flagged areas of concern, farm workers were alerted and deployed targeted pesticide applications using UAVs. This approach significantly reduced pesticide usage and improved the precision of pest control efforts. The use of drones for pesticide application, informed by real-time data, ensured that only affected areas were treated, minimizing chemical exposure to the rest of the plantation (Thereza et al., 2020).

6.3 Effectiveness of precision management in pest reduction

The introduction of IoT and remote sensing technologies in this tea plantation led to substantial improvements in pest management. The most significant outcome was a 35% reduction in pesticide use, primarily due to the ability to apply chemicals only in areas where pests were detected, rather than treating the entire plantation. This not only reduced the environmental impact of chemical use but also resulted in cost savings on pesticides. Furthermore, the targeted approach prevented over-application, thus reducing the risk of pests developing resistance to the pesticides (Sarangi et al., 2020).

Crop yields improved by approximately 15%, attributed to healthier plants with less pest damage. Early detection of infestations through drone surveillance allowed for timely interventions, preventing pests from spreading and causing more extensive damage. For example, red spider mite infestations, which typically affected large sections of the plantation, were contained to smaller, manageable areas, reducing crop losses significantly. The combination of IoT and drone technology thus proved to be a successful strategy for improving both pest management and crop productivity in this tea plantation (Filho et al., 2019).

7 Future Directions and Innovations

7.1 Emerging technologies in precision agriculture

Emerging technologies in precision agriculture are revolutionizing pest management by enabling more accurate, efficient, and sustainable farming practices. One of the most promising advancements is the increased use of drones and Unmanned Aerial Vehicles (UAVs) equipped with multispectral and hyperspectral sensors for real-time monitoring of crops and early detection of pest infestations. These technologies can provide detailed imaging and data analytics that allow for targeted interventions, reducing the need for broad pesticide applications and minimizing environmental impact (Sishodia et al., 2020). In addition, the Internet of Things (IoT) and big data analytics are increasingly being integrated into farming operations, enabling continuous data collection from sensors distributed throughout fields. This data is used to optimize irrigation, fertilization, and pest control strategies in real-time.

Blockchain technology is also being explored in agriculture to improve transparency in the supply chain, ensuring that sustainable farming practices are documented and verifiable (Khanna et al., 2022). Additionally, cloud computing platforms allow farmers to store and analyze vast amounts of data efficiently, enabling faster and more informed decision-making. These technological advancements, when combined, form the basis of Agriculture 4.0, where precision farming techniques become even more intelligent and responsive to the specific needs of crops.

7.2 Potential for AI and machine learning integration

Artificial Intelligence (AI) and Machine Learning (ML) are set to play an increasingly important role in the future of precision pest management. AI algorithms are already being used to analyze data collected from IoT devices, drones, and satellite imagery, enabling predictive modeling of pest outbreaks. Machine learning models, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, are particularly effective at analyzing complex datasets and detecting patterns that indicate pest presence or crop stress (Sharma et al., 2021). For example, AI-based systems can predict pest infestations based on historical weather data, plant growth patterns, and environmental conditions, allowing farmers to take preventive measures before pests become a major problem.

Another promising area is the integration of AI with robotics for automated pest control. Drones and ground-based robots can be programmed with AI algorithms to identify and treat specific pest-affected areas, applying pesticides only where necessary. This reduces the overall chemical load on the environment and improves the sustainability of farming practices (Umrani et al., 2021). AI can also optimize the use of biological controls, such as introducing natural predators, by predicting where pest populations are likely to emerge.

7.3 Future prospects for scaling precision pest management

The future prospects for scaling precision pest management are promising, particularly as the costs of technologies such as IoT sensors, drones, and AI systems continue to decrease. One of the main barriers to widespread adoption, particularly in smaller or resource-constrained farms, has been the initial investment required for these advanced tools. However, as technology becomes more affordable and accessible, precision pest management is likely to become the norm rather than the exception. The development of user-friendly, cloud-based platforms for data analysis and decision-making will further facilitate the adoption of these technologies by farmers who may not have technical expertise (Micheni et al., 2022).

Additionally, government incentives and subsidies for sustainable farming practices, along with increased demand for environmentally friendly agricultural products, will likely drive further investment in precision technologies. As the global population grows and the demand for food increases, precision pest management will be critical in ensuring that crops are protected while minimizing environmental impact. With continuous improvements in AI, machine learning, and IoT technologies, precision pest management systems will become more accurate, scalable, and cost-effective, benefiting farmers worldwide (Naresh et al., 2020).

8 Concluding Remarks

This study has demonstrated the effectiveness of integrating Internet of Things (IoT) and remote sensing technologies into Precision Pest Management (PPM) in tea plantations. Through a combination of real-time data from IoT sensors and high-resolution imaging from drones and satellite platforms, farmers are able to monitor environmental conditions and pest activity more accurately, allowing for targeted interventions. The case study showed significant reductions in pesticide use-up to 35%-as well as an improvement in crop yields, with healthier plants and more timely responses to pest infestations. These findings highlight the potential for PPM to transform pest control practices in tea plantations by making them more efficient, cost-effective, and environmentally sustainable. Furthermore, the integration of machine learning and AI has enabled predictive modeling of pest outbreaks, contributing to early intervention and improved outcomes for farmers.

The long-term benefits of implementing precision pest management in tea cultivation are substantial. First, the reduction in pesticide usage leads to less environmental contamination and minimizes the risk of pesticide resistance among pests. This also aligns with growing consumer demand for sustainable and organic products, opening up new markets for tea producers. Additionally, precision technologies, such as AI-driven analytics and IoT-enabled monitoring systems, provide continuous improvements in efficiency, reducing operational costs over time. As the cost of technology decreases, these methods will become increasingly accessible to smallholder farmers, contributing to broader adoption and scalability across the tea industry.

Another long-term benefit is improved soil health and biodiversity in tea plantations. By reducing chemical inputs, PPM contributes to more balanced ecosystems, allowing beneficial insects and microorganisms to thrive. This promotes healthier soil, which is essential for sustaining long-term tea production. As precision farming technologies continue to evolve, the integration of advanced machine learning models, autonomous drones, and IoT devices will enhance pest control further, making it more precise and cost-efficient. Ultimately, the widespread adoption of these technologies has the potential to reshape the tea industry by fostering more resilient, sustainable, and profitable farming systems.

Acknowledgments

We would like to thank two anonymous peer reviewers for their suggestions on my manuscript.

Conflict of Interest Disclosure

The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

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