The Role of Precision Coffee Farming in Mitigating the Biotic and Abiotic Stresses Related to Climate Change in Saudi Arabia: A Review

Abstract

In Saudi Arabia, coffee (Coffea arabica L.) has been grown for centuries on the mountain terraces of the southwestern regions. Jazan region accounts for about 80% of the total production. The acreage allocated to coffee is comparatively small but it is expanding rapidly thanks to a strong government-supported drive to increase local coffee production. Despite the initial success, the effort is hampered by the limited water supply available for irrigating the new plantings and the increased incidence of pests and diseases. The magnitude of these natural handicaps appears to have increased as of late, apparently due to climate change (CC). This review examines strategies to mitigate the consequences of CC on the coffee sector through the implementation of precision agriculture (PA) techniques, with the focus on addressing the challenges posed by biotic and abiotic stresses. The impact of CC is both direct by rendering present growing regions unsuitable and indirect by amplifying the severity of biotic and abiotic tree stressors. Precision agriculture (PA) techniques can play a key role in tackling these challenges through data-driven tools like sensors, GIS, remote sensing, machine learning and smart equipment. By monitoring soil, climate, and crop conditions, PA enables targeted irrigation, fertilization, and pest control thus improving efficiency and sustainability. This approach reduces costs, conserves resources, and minimizes environmental impact, making PA essential for building climate-resilient and sustainable coffee production systems. The review synthesizes insights from case studies, research papers, and other scientific literature concerned with precision farming practices and their effectiveness in alleviating biotic and abiotic pressures on coffee trees. Additionally, it evaluates technological advances, identifies existing knowledge gaps, and suggests areas for future research. Ultimately, this study seeks to contribute to enhancing the resilience of coffee farming in Saudi Arabia amidst ongoing CC challenges by educating farmers about the potential of PA technologies.

1. Introduction

Coffee is one of the most commercially important crops, ranking as the second most traded commodity after oil [1]. In the production year 2022/2023, world coffee production stood at 168.2 million bags (10.098 million metric tons), with Arabica coffees contributing 94.0 million bags (i.e., 55.9% of the total) and Robustas 74.2 million bags (44.1%) [2]. The apparent stagnation in world total coffee production hides stark differences among the main producing regions, with a significant output increase in the Americas and a decrease in Africa and Asia mainly due to unfavorable climate conditions. The major coffee producing countries are Brazil, Vietnam, and Colombia, which lead global production. Other significant producers include Indonesia, Ethiopia, Honduras, and India. These countries, located in the “Coffee Belt”, i.e., the tropical regions of the world between latitudes 25 degrees North and 30 degrees South, contribute the majority of the world’s coffee supply, which is grown in regions across Latin America, Asia, and Africa [1,2].

Beyond its high export value, coffee holds significant cultural value due to the traditional knowledge, art, and rituals surrounding its preparation and consumption—a cultural heritage value that has grown in recent decades [3,4]. Despite its economic and cultural significance, the sustainability of coffee cultivation amid mounting challenges from climate change (CC) and the spread of difficult-to-control diseases and pests is at great risk due to the narrow genetic base of the species [5,6,7].

Recent research reports indicate that CC poses significant threats to coffee production, with projections suggesting a decline in yield, loss of suitable cultivation areas, and increased prevalence of pests and diseases that directly affect coffee tree growth [6,8,9]. The species Coffea arabica L., in particular, is fragile; a recent intergovernmental panel concluded that CC will significantly reduce coffee yields by 2050 [10]. Thus, a deeper understanding of the impact of CC on coffee agro-ecosystems is urgently needed [11].

In Saudi Arabia, the impacts of CC are already evident [12], manifesting as both biotic and abiotic stresses that jeopardize the sustainability of coffee farming. This review aims to explore how precision farming can mitigate the effects of these biotic and abiotic stressors on coffee production in the context of CC. The paper will assess the current status of technological advancements and practices, identify existing knowledge and research gaps, and propose areas for further investigation to enhance the resilience of coffee farming in the country. The paper also discussed the significance of coffee cultivation in Saudi Arabia and explored how CC affects the growth of Arabica coffee trees, particularly through abiotic stress factors. We also elaborated on the concept of precision farming, illustrating how its application can enhance the quality and yield of annual harvests while addressing the challenges that growers face.

To compile this review, we collected, analyzed, and synthesized existing relevant research publications with the objective of apprising the present situation and providing a foundation for further discussion and inquiry. A systematic review process was utilized to identify the relevant literature; this involved setting clear inclusion and exclusion criteria prior to the literature search. The first criterion was to focus mostly on publications from the last fifteen years (2010–2025) to ensure the information is current. The second criterion was using the following specific keywords in the search: Saudi Arabia, coffee production, remote sensing, abiotic stress, CC and PA. Only English-language publications were considered, except for Ghebris [4] that was in Arabic. The approach ensured that only the works that contributed meaningfully to the review were retained.

2. The Coffee Crop: Origin and Significance

The origin of coffee can be traced back to the Ethiopian highlands and the Boma Mountain in South Sudan, from where it spread to the Arabian Peninsula in the 15th century [13,14]. By most accounts, coffee was first cultivated and consumed as a beverage in the late fifteenth century in what is now Yemen [14]. It was reported that a Muslim Sufi leader from Yemen, Abubaker Bin Abdallah Ashadhili, also known as Al-Idrous, was the first to create a drink from coffee beans in the late fifteenth century [4]. He used this brew as a stimulant to help him stay awake for night prayers and encouraged his disciples to partake as well. By the 16th century, coffee made its way to Constantinople, then Europe and the Americas through Southeast Asia, establishing itself as a global commodity [15].

Being one of the first places where coffee domestication started, Saudi Arabia has a rich history of coffee cultivation dating back more than four centuries [16,17]. Known locally as “Qahwa” or “Gahwa”, Saudi coffee embodies the hospitality and traditional values of Arab culture [17]. It is typically made from lightly roasted Arabica beans infused with spices such as cardamom, saffron, or cloves, and is often served alongside dates and sweets [18].

3. Coffee Production and Trade in Saudi Arabia

Coffee production in Saudi Arabia peaked at 386 tons in 1982 [19]. This figure has since declined, largely due to the introduction of other crops that offer quicker returns and a shorter value chain. Additionally, the oil boom may have contributed to the abandonment of coffee farms, as more lucrative opportunities in other sectors, especially in the cities, became available. However, in the last few years there has been a sort of revival in coffee production in the country. In a 2021 report from the Ministry of Environment, Water and Agriculture (MEWA), it is indicated that there are about 700 thousand coffee trees in Saudi Arabia, cultivated in 2500 farms across the four regions of the southwest, producing about 144 tons of green beans yearly [19,20]. This production represents less than 0.5% of the country’s coffee consumption [21]. However, due to disorganized production and marketing practices, these figures are rough estimates.

Coffee orchards thrive in the regions of Jazan, Asir, Al-Baha, and Najran at elevations ranging from 1100 to 1600 m above sea level, with Jazan accounting for the lion share of the production (Figure 1 and Figure 2) [22,23]. The main coffee acreage is located in Addayer governorate of Jazan region, with about 70% of the total number of trees [16,17]. The regions of Al-Baha, Asir and Najran have much fewer trees, but the numbers are increasing rapidly with significant government support for the growers (Figure 3) [20]. The main coffee varieties are Khawlani, Shadawi, Dawairi, Balady and JU2030 [16,24].

Saudi Arabia’s coffee market size is fairly large; it was evaluated at 2.12 billion US$ in 2022 [21]. Furthermore, the yearly increase in coffee consumption stood at 4% between 2016 and 2021 [25]. Saudi Arabia imported 54,000 tons of coffee in 2021, mainly from Ethiopia but also from Brazil, Colombia and Indonesia [26].

Despite recent increases in farm and tree numbers, coffee cultivation in Saudi Arabia faces several significant challenges, including aging trees, insufficient rainfall, a lack of technical expertise, limited access to institutional financing, and the absence of established farmer organizations or cooperatives. Furthermore, the rocky terrain where coffee is grown adds to the difficulties and cost of coffee farming, necessitating innovative solutions.

The country has implemented many initiatives to develop the local coffee production by providing services to farmers [25]. One of these initiatives was the Sustainable Rural Agricultural Development Program (2019–2025), more known by the acronym “Reef”, which was launched by MEWA with the technical support of the Food and Agriculture Organization (FAO) to help growers in the four southwestern regions expand and modernize their coffee orchards and boost the country’s coffee output [20,26] (Figure 3). Similarly, the Saudi Public Investment Fund (PIF) has established the Saudi Coffee Company with the primary goal of fostering sustainable coffee production in the country and developing the coffee industry (Figure 2). The company plans to invest 1.2 billion SAR over the next decade to enhance the Saudi coffee industry, aiming to increase annual production from 144 tons to 2500 tons by the end of the decade [27]. The vision includes building the nation’s capacity to export premium coffee beans and coffee products to international markets.

4. Climate Change and Its Impact on Coffee Farming

Global warming describes the gradual increase in the Earth’s surface temperature over the long term, beginning around the mid-19th century. This trend is largely caused by human actions, including the burning of fossil fuels, large-scale deforestation, and various industrial activities that release greenhouse gases—such as carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)—into the atmosphere [28]. As a consequence of this greenhouse effect, earth’s average temperature has increased by about 1 °C, with an ongoing rise of over 0.2 °C per decade. This current warming trend, unprecedented in millennia, has been conclusively linked to human activity since the 1950s [29].

A major concern linked to global warming is CC, which involves long-term alterations in typical weather patterns at local, regional, and global levels [28]. These changes have resulted in a range of noticeable impacts often associated with the broader concept of “CC”. Since the mid-1900s, human activities—most notably the combustion of fossil fuels—have been the leading contributors to this phenomenon. However, natural influences such as volcanic eruptions, recurring oceanic cycles, and fluctuations in solar radiation also play a role in driving climate variability [30].

Research from the Intergovernmental Panel on Climate Change (IPCC) and the Regional Initiative for the Assessment of Climate Change Impacts on Water Resources and Socio-Economic Vulnerability in the Arab Region (RICCAR) suggests that certain regions of Saudi Arabia are expected to experience increased climate-related risks in the years ahead [31]. These risks include rising temperatures, increased precipitation variability and extremes (such as droughts and floods), and more frequent strong wind days, potentially intensifying sand and dust storms (Figure 4). Similar findings were reported by Varela et al. [30]. According to Zittis et al. [29], if current emission trends continue unchecked, the Middle East and North Africa (MENA) region could experience record-breaking super- and ultra-extreme heat waves during the second half of the 21st century. These events will feature exceptionally high temperatures (reaching up to 56 °C or higher) and extended durations (lasting several weeks), posing serious, potentially life-threatening risks to human health. The study’s climate projections under a high-emission scenario suggest a shift towards extreme heat wave conditions, with the emergence of unprecedented “super-extreme” events by mid-century, potentially becoming common summer conditions by century’s end. Such prolonged, intense, and frequent events are likely to pose significant risks to human health and societies, with substantial impacts on livestock, agriculture, and biodiversity.

CC poses significant threats to coffee production, affecting both yields and quality [6,7,9]. Both Robusta and Arabica coffee species, which account for 99% of global coffee production, are expected to be adversely impacted by CC [32]. Rising temperatures are forcing farmers to seek higher altitudes for coffee cultivation, where suitable land for agriculture is limited [33]. Current and future climate conditions are projected to challenge sustainable coffee production, leading to reduced yields [34]. Studies from the IPCC predict that by the year 2050, there will be a decline in average global coffee yields and suitable cultivation areas [10]. In Saudi Arabia, the decrease in rainfall and dwindling of ground water reserves are exposing the coffee plantations to extreme water stress which weakens the trees and predispose them to soil-borne diseases (Figure 4). Research indicates that after 2050, conditions favorable for Arabica coffee cultivation are likely to diminish, impacting the regions where it presently thrives [35]. It is becoming increasingly clear that the long-term effects of CC on coffee production will include a reduction in suitable growing areas, lower yields, increased prevalence of pests and diseases, and more frequent extreme weather events [10]. Most models are forecasting significant declines in coffee yields—ranging from 12% to 28%—between 2036 and 2065 in Latin America and Africa due to climate change [36]. Additionally, the diminishing suitability of various regions for coffee cultivation is expected to affect production volumes, price stability, market dynamics, and the livelihoods of millions of smallholder farmers [37].

Coffee productivity in both C. arabica L. (2n = 4x = 44) and C. canephora (2n = 2x = 22) is influenced by a wide range of environmental variables, including temperature, rainfall patterns, and solar radiation, as well as by agronomic practices such as irrigation, pruning, plant density, fertilization, and pest and disease management [38,39]. These factors collectively shape the crop’s vegetative and reproductive phases [11].

Given the anticipated shifts in climate over the next decades, particularly global warming, it is vital to understand how climatic conditions and their variability affect Arabica coffee cultivation [40]. The CC not only threatens yields and bean quality but may also shift the geographic suitability of coffee-growing regions. Such changes could compel farmers to move production areas or implement more sustainable cultivation techniques, which in turn may raise production and water management costs [11]. In this context, adaptation and mitigation strategies become critical to ensuring the resilience of coffee farming under changing environmental conditions [41,42] (

Table 1. Predicted regional shifts in coffee suitability under climate change.

Region/Country	Main Study Sources	Projected Suitability Changes	Altitude or Area Shifts	Main Drivers (Climatic Variables)	Adaptation Recommendations
East Africa (Ethiopia, Kenya, Uganda, Tanzania)	[6,10,11]	- Major decline (40–60%) in arabica suitability in low- and mid-elevations. - Highland zones (1600–2200 m) become climatic refuge.	Upward migration of coffee zones by 300–600 m above current optimal range.	↑Temperature (+1.5–3 °C); ↓ rainfall reliability; ↑ fungal disease risk (leaf rust).	- Conserve wild Coffea arabica gene pools in Ethiopian forests. - Promote shade agroforestry and irrigation buffering. - Introduce climate-resilient cultivars (e.g., C. arabica var. Harari).
Central & South America (Brazil, Colombia, Costa Rica, Nicaragua, Mexico)	[9,10,11]	- Suitable arabica area to decline by 25–50% in Brazil and Central America by 2050. - Some highland expansion possible in Andes (Colombia, Peru). - Potential Robusta expansion in warmer lowlands (Espírito Santo, Rondônia).	Altitude shift +200–400 m; possible southward movement of viable zones.	↑ Temperature; irregular rainfall; longer dry seasons; ↑ pests (berry borer).	- Promote integrated pest management (IPM) and shade systems. - Adjust planting calendars and irrigation. - Breed hybrids tolerant to heat and drought (e.g., Catuaí SH3).
Southeast Asia (Vietnam, Indonesia, Philippines)	[8,9,10,11]	- Vietnam’s Central Highlands to face yield declines of 20–30% due to drought. - Robusta remains viable in wetter highlands; lowland losses expected.	Suitable zones shift 100–300 m upward; reduction in total area by 10–20%.	↓ Soil moisture; ↑ dry-season length; rising night-time temperatures.	- Invest in efficient irrigation (drip/sensor-based). - Diversify with intercrops (pepper, fruit trees). - Expand Arabica in northern highlands (Lâm Đồng province).
West Africa (Côte d’Ivoire, Ghana, Cameroon)	[9,10]	- Significant decline in Robusta suitability (15–40%) due to extreme heat and erratic rainfall. - Northern zones become unsuitable; southern wetter regions remain marginal.	Contraction southward; potential upward migration limited by topography.	↑ Mean temperature > 27 °C; ↓ annual rainfall; ↑ heat stress index.	- Introduce drought-resistant Robusta clones. - Rehabilitate shaded systems and reforest buffer zones. - Improve early-warning climate services.
Arabian Peninsula (Yemen, Saudi Arabia—Jazan Highlands)	[10,11]	- Marginal arabica zones are increasingly heat-stressed but could persist at >1500 m with shade. - Potential for high-value specialty coffee niches.	Upward migration of 200–300 m; reduced area but possible quality improvement in cool pockets.	↑ Temperature; ↓ rainfall; episodic drought; high evapotranspiration.	- Emphasize terraced agroforestry, shading and mulching. - Use local drought-tolerant varieties (e.g., Jazan Premium, JU2030). - Integrate rainwater harvesting and micro-irrigation. - Strengthen integrated pest control and soil conservation. - Expand smallholder adaptation training.
Pacific Islands & Papua New Guinea	[10]	- Moderate decline (10–25%) in arabica suitability; increased pest pressure. - High-altitude micro-zones (>1500 m) remain suitable.	Minor upward shift (< 200 m).	↑ Rainfall variability; ↑ temperature.	- Strengthen integrated pest control and soil conservation. - Expand smallholder adaptation training.

Note: ↑: increase; ↓: decrease.

).

CC poses a multifaceted threat to global coffee cultivation and production. Davis et al. [6] warn that rising temperatures and habitat loss could drive over 60% of wild coffee species toward extinction, undermining the genetic diversity essential for crop resilience and long-term sector sustainability. In Vietnam, droughts—exacerbated by erratic rainfall and higher evapotranspiration—are already reducing yields and threatening farmer livelihoods [8]. Similarly, Bilen et al. [10] synthesized global findings, revealing that shifting temperature and precipitation patterns alter pest and disease dynamics, suitable growing zones, and overall productivity, with Arabica coffee being particularly vulnerable (Table 1) [6,8,9]. However, coffee plants may show greater resilience than previously believed if adaptive agronomic practices, genetic improvement, and shading systems are implemented [11]. Complementing this, Bianco [9] highlights the critical role of corporate social responsibility and adaptation initiatives—such as sustainable sourcing and farmer training—in mitigating climate impacts and promoting equitable resilience across the value chain. Collectively, these works emphasize that climate change is reshaping the geography, sustainability, and socio-economic stability of global coffee systems, demanding urgent adaptation and conservation measures.

To mitigate the impact of CC on coffee production, it is essential to implement adaptation strategies that include breeding stress-tolerant varieties [7,43], selecting suitable shade trees, enhancing soil fertility, improving pest and disease management, and equipping farmers with access to weather information and advanced technology [36]. These measures are crucial for promoting climate-resilient coffee production systems and sustaining the livelihoods of millions worldwide [33,36]. PA tools, in particular, can play a significant role in streamlining and enhancing the effectiveness of these strategies, allowing for targeted interventions and better resource management in coffee farming [44].

5. Precision Coffee Farming Techniques

Smart farming and PA mark a significant shift in modern agricultural methods, utilizing cutting-edge technologies like geostatistics, remote sensing, spatial variability analysis, machine learning, the Internet of Things (IoT), artificial intelligence (AI), and big data analytics to optimize productivity and sustainability [45,46,47] (Table 2). PA combines these techniques with a focus on resource use optimization by accounting for soil variability, tree stand heterogeneity and changes in plant needs with age and physiological status [44,48]. The overriding objective of PA in coffee cultivation is to enable farmers to optimize resource allocation to increase crop yields while improving sustainability and reducing environmental footprint.

This section underscores the significant role that precision farming technologies play in enhancing coffee production amidst challenges such as CC, biotic and abiotic stresses, and water scarcity [47]. Each technological approach contributes uniquely to improving the efficiency and sustainability of coffee cultivation practices [44]. The aim is to reduce the application of pesticides and fertilizers, improve operational efficiency, and ensure sustainable coffee production [49,50]. It is a vision of PA that strives to strike a balance between agricultural sustainability and environmental stewardship [48]. Presently, the use of precision electronic resources and harvesters in coffee cultivation is mostly limited to large producing countries such as Brazil; nevertheless, even in small orchards, manual operation such as spraying and harvesting can help generate useful data to build thematic maps [51].

By analyzing various soil, climate, and plant factors with machine learning algorithms, PA can identify sustainable farming practices to maximize crop production [46,47,52]. This method relies on sophisticated tools like sensors, GPS, and data analytics to oversee and control agricultural operations with remarkable accuracy [53]. For example, soil sensors deliver real-time insights into moisture, nutrient levels, and pH, enabling farmers to use water and fertilizers more effectively. Additionally, weather-related data—such as temperature, humidity, and rainfall trends—can help forecast the best times for planting and harvesting, thereby minimizing the chances of crop loss due to unfavorable weather.

Machine learning algorithms analyze vast amounts of data to identify patterns and make recommendations for improving crop yields while minimizing environmental impact [54]. For example, they can suggest crop rotation schedules that enhance soil health, recommend pest control measures that reduce chemical use, and optimize irrigation systems to conserve water [47]. These techniques contribute to agricultural economies and environmental management, ensuring economic, agricultural, and ecological efficiency [55]. Particularly in the management of nitrogen fertilizers, PA presents opportunities to improve fertilizer use efficiency while minimizing environmental risk [56]. Thus, PA combines technology, data analysis, and policy measures to enhance agricultural productivity and reduce environmental harm by reducing waste, conserving resources, and minimizing the ecological footprint of farming practices [57].

Overall, precision coffee farming techniques present innovative ways to manage crops, enhance sustainability, and boost agricultural profitability while protecting the environment [36,51,57].

Table 2. Precision Farming Techniques in Coffee Production.

Technological Component	Application in Coffee Growing	References
Geostatistics, Remote Sensing, Spatial Variability Analysis	Analyze soil, canopy, and environmental variability to guide resource allocation; detect biotic/abiotic stress and climate-related shifts.	[44,45,46,47]
Artificial Intelligence (AI) & Machine Learning (ML)	Process large datasets to identify yield patterns, optimize irrigation and fertilization, recommend pest control, and predict coffee yield and quality.	[44,45,46,47,52,53,54,55]
Internet of Things (IoT) & Wireless Sensor Networks (WSNs)	Enable real-time data transmission on soil, plant, and weather conditions for informed decision-making; support stress detection and predictive analytics.	[44,45,46,52,53,54,55,56]
Geographic Information Systems (GIS)	Integrate spatial and temporal data (soil, crop, topography) to create detailed farm and soil maps, analyze terroir effects, and monitor environmental impacts.	[51,55,58,59,60]
Satellite Imagery & Remote Sensing	Provide disease mapping, soil quality and plant-health data; detect temporal changes and climate impacts on plantations.	[58]
Variable Rate Technology (VRT)	Apply inputs (fertilizers, water, pesticides, seeds) at variable rates based on field heterogeneity to improve yield, reduce costs, and minimize runoff.	[56,58,59,60,61,62,63]
Sensors (Optical, Thermal, Ultrasonic, Electrochemical)	Measure soil moisture, nutrient levels, canopy temperature, and chlorophyll to enable site-specific management and precision irrigation/fertilization.	[44,45,52]
Drones (UAVs)	Collect high-resolution spatial/temporal imagery for monitoring crop health, irrigation efficiency, pest and disease detection, and precision spraying.	[59,62,63,64,65]
Big Data Analytics & Decision Support Systems	Integrate multiple datasets (climate, soil, yield) for sustainable management, policy planning, and environmental optimization.	[46,47,52,55,57]
Predictive Modeling & Deep Learning	Forecast yield and quality; automate detection of biotic stresses and support targeted treatment strategies.	[45,65,66]

Table 2. Precision Farming Techniques in Coffee Production.

Technological Component	Application in Coffee Growing	References
Geostatistics, Remote Sensing, Spatial Variability Analysis	Analyze soil, canopy, and environmental variability to guide resource allocation; detect biotic/abiotic stress and climate-related shifts.	[44,45,46,47]
Artificial Intelligence (AI) & Machine Learning (ML)	Process large datasets to identify yield patterns, optimize irrigation and fertilization, recommend pest control, and predict coffee yield and quality.	[44,45,46,47,52,53,54,55]
Internet of Things (IoT) & Wireless Sensor Networks (WSNs)	Enable real-time data transmission on soil, plant, and weather conditions for informed decision-making; support stress detection and predictive analytics.	[44,45,46,52,53,54,55,56]
Geographic Information Systems (GIS)	Integrate spatial and temporal data (soil, crop, topography) to create detailed farm and soil maps, analyze terroir effects, and monitor environmental impacts.	[51,55,58,59,60]
Satellite Imagery & Remote Sensing	Provide disease mapping, soil quality and plant-health data; detect temporal changes and climate impacts on plantations.	[58]
Variable Rate Technology (VRT)	Apply inputs (fertilizers, water, pesticides, seeds) at variable rates based on field heterogeneity to improve yield, reduce costs, and minimize runoff.	[56,58,59,60,61,62,63]
Sensors (Optical, Thermal, Ultrasonic, Electrochemical)	Measure soil moisture, nutrient levels, canopy temperature, and chlorophyll to enable site-specific management and precision irrigation/fertilization.	[44,45,52]
Drones (UAVs)	Collect high-resolution spatial/temporal imagery for monitoring crop health, irrigation efficiency, pest and disease detection, and precision spraying.	[59,62,63,64,65]
Big Data Analytics & Decision Support Systems	Integrate multiple datasets (climate, soil, yield) for sustainable management, policy planning, and environmental optimization.	[46,47,52,55,57]
Predictive Modeling & Deep Learning	Forecast yield and quality; automate detection of biotic stresses and support targeted treatment strategies.	[45,65,66]

5.1. Geographic Information Systems (GIS), Remote Sensing, Satellite Imagery, and Variable Rate Technology (VRT)

Satellite imagery and remote sensing have revolutionized coffee plantation monitoring by enabling disease mapping and early detection [58]. These technologies provide detailed information on soil quality, plant health, and other factors affecting coffee production.

GIS applications in coffee production offer a comprehensive framework for analyzing spatial and temporal data. By integrating satellite imagery with GIS, detailed maps can be created to inform farm management decisions. Several studies demonstrated that combining GIS with image analysis capability significantly enhances the accuracy of coffee plantation maps by including physical features and elevation data [58]. For example, by analyzing satellite data time series, one can detect changes across various spatial and temporal scales that may be linked to CC. This approach allows for the identification of trends and anomalies, such as shifts in temperature, tree heath and growth patterns, providing valuable insights into the long-term impacts of CC on the plantations. Additionally, GIS helps understand how terroir influences coffee quality, which is essential for preserving the unique characteristics of different coffee varieties [51]. Furthermore, recent advancements allow for better differentiation of coffee production systems and assessment of their environmental impacts [55,58].

Large volumes of data can be compiled by these monitoring systems. The resulting databases can be easily exploited by researchers in a collaborative manner to gain useful insights into the factors affecting coffee production. The approach consolidates knowledge and resources, creating a cohesive network that outperforms fragmented efforts.

Kushwaha et al. [59] highlighted the critical role of GIS in enhancing PA by allowing farmers to collect, analyze, and interpret spatial data related to their farming activities. GIS combines geographic information—like maps, satellite images, and on-site observations—with descriptive data such as soil composition, crop variety, and yield records to support more informed decision-making. One of its primary uses is in field mapping, where farmers can produce detailed layouts of their land, including boundaries, management zones, and infrastructure like irrigation networks and drainage systems. Soil mapping, in particular, helps identify variations in soil characteristics across a field, such as nutrient imbalances or compaction issues, enabling targeted interventions [60]. Additionally, GIS supports yield monitoring by analyzing data from tools like harvesters equipped with yield sensors. This analysis helps detect patterns and performance differences across the field, guiding future crop management and planning strategies [59,60].

GIS-based prescription mapping enables the precision application of inputs through Variable Rate Technology (VRT), optimizing resource use and maximizing crop yields (Figure 5). With VRT, inputs such as fertilizers, water, pesticides, and seeds can be applied at varying rates across a field, tailored to the unique requirements of each section of a coffee plantation [61]. This approach has been shown to enhance energy efficiency in coffee production without compromising output [56,61]. For instance, adjusting nitrogen fertilizer management according to variable rates can significantly improve both yields, tree health and orchard profitability [58]. Variable Rate Technology is a cornerstone of PA, designed to deliver seeds, fertilizers, pesticides, and water according to the specific needs of different areas within a field. Unlike traditional uniform application methods, VRT ensures crops receive precisely what they need, when and where they need it, optimizing resources, reducing costs, and minimizing environmental impact while improving overall crop yield and quality [56].

At the heart of VRT are several integrated components. GPS (Global Positioning System) provides accurate location data, enabling machinery to navigate fields precisely. Sensors, soil tests, and satellite or drone imagery detect variations in soil fertility, moisture, and crop health, generating detailed maps that highlight zones with differing input requirements [61]. Application equipment—including seed drills, fertilizer spreaders, sprayers, and irrigation systems—then adjusts the rate of input delivery in real time. Control software integrates all these data layers to create “prescription maps,” guiding machinery to apply inputs variably according to the exact field conditions [63].

The VRT process begins with field analysis, either using sensors or historical data, to identify variability in soil and crop conditions (Figure 5). A prescription map is then developed, indicating which areas require more or less of a specific input. As machinery moves through the field, it automatically adjusts application rates according to the map. For example, fertilizers are applied more heavily in nutrient-poor zones and less densely in fertile zones, while seeds are planted more densely in productive areas and more sparsely in weaker ones. Similarly, irrigation and pesticide applications can be adapted to local moisture levels or pest pressure [63].

The advantages of VRT are substantial. Farmers can reduce input costs, minimize chemical runoff, and use resources more efficiently, while crops grow more uniformly and yields increase. By matching inputs to the natural variability of the field, VRT represents a smarter, more sustainable approach to farming. For instance, in a heterogeneous 100-hectare field, fertilizer could be applied at 80 kg/ha in nutrient-poor zones and 40 kg/ha in fertile zones, achieving both economic and environmental benefits [58,61].

Sensor-based monitoring systems are pivotal for collecting real-time data on soil properties, crop health, and environmental conditions [44]. Various sensors—including optical, thermal, ultrasonic, and electrochemical types—are deployed to measure parameters such as soil moisture, nutrient levels, canopy temperature, and chlorophyll content [44,45]. These data enable site-specific management practices that optimize irrigation, fertilization, and pest control, thereby reducing input costs and minimizing ecological impact.

Drones are a cost-effective tool to field monitoring and data collection. They have become an integral component of precision agriculture, offering high-resolution monitoring and efficient management of agricultural resources [62]. Equipped with multispectral, thermal, and optical sensors, drones enable real-time assessment of crop health, soil conditions, and irrigation efficiency. They facilitate early detection of plant stress, pest infestations, and diseases—often before symptoms are visible—thereby supporting timely interventions and improved yield outcomes [63].

By collecting spatial and temporal data throughout the growing season, drones contribute to yield estimation models and assist in mapping and surveying for field planning, planting, and resource allocation [59,64]. Their ability to perform precision spraying of fertilizers, pesticides, and herbicides minimizes chemical use and environmental impact. Overall, the use of drones reduces labor demands, optimizes input application, and increases farm safety by limiting human exposure to hazardous conditions. Consequently, drones represent a cost-effective and sustainable tool for data-driven agricultural decision-making and improved production efficiency [62].

The advent of Internet of Things (IoT) frameworks and wireless sensor networks (WSNs) has further improved data transmission, integration, and decision-making processes [46,54]. Combined with machine learning and remote sensing, these systems can provide predictive insights into crop performance and stress detection. However, challenges remain regarding sensor calibration, energy consumption, data interoperability, and the high costs associated with large-scale deployment [52,62]. Recent research has focused on developing low-power, cost-effective sensors with higher accuracy and durability, suitable for harsh agricultural environments. Overall, sensor-based monitoring represents a cornerstone of modern precision agriculture, enabling data-driven decision-making for sustainable food production. In the context of Saudi Arabia, where water scarcity and resource management are critical challenges [43], VRT represents a practical solution for sustainable coffee production. By optimizing the use of water and other inputs, VRT can help Saudi coffee growers enhance productivity while preserving environmental resources.

5.2. Predictive Modeling and Machine Learning

Predictive modeling and machine learning are at the forefront of agricultural innovation, offering transformative tools for forecasting coffee yields and quality. By leveraging agroclimatic data, machine learning models have demonstrated high accuracy in predicting coffee yields, enabling farmers to make informed planning and decision-making choices [45]. Additionally, the integration of multispectral imaging with machine learning algorithms has proven effective in estimating coffee plant yield, showcasing the potential of these technologies within PA [65].

Research by Esgario et al. [66] highlights the revolutionary impact of deep learning on coffee plant health management. These advanced techniques can automate the detection and severity assessment of biotic stresses, equipping coffee farmers with powerful tools for early intervention and targeted treatment strategies. As technology evolves and datasets grow, the accuracy and applicability of these models are expected to further enhance, potentially transforming global coffee farming practices.

6. Biotic Stress Factors in Precision Coffee Farming

Biotic stressors, including pests, weeds and pathogens, significantly impact coffee production by affecting both the yield and quality of the crop. The integration of deep learning techniques with computer vision can facilitate the prompt and accurate detection of stress-inducing factors, enabling timely interventions [66].

6.1. Pest Management Strategies

Modern pest management strategies in coffee plantations rely on Integrated Pest Management (IPM) techniques that aim to maintain pest populations at tolerable levels in an economically and environmentally responsible manner [67]. These strategies encompass a range of practices, including cultural controls, continuous monitoring, and the use of biological agents such as the fungi Trichoderma spp. and Beauveria bassiana that behave as safe, low-cost, effective and eco-friendly biocontrol agents against a wide range of plant diseases [68]. Given that infestations from insect pests can lead to significant yield losses, effective pest control is crucial for the success of coffee farming [67]. Key pests affecting coffee globally include the coffee berry borer, mealybugs, soft green scale, and white grubs [68,69]. Research efforts have focused on understanding the biology of these pests, improving monitoring techniques, and developing effective management strategies [67].

In Saudi Arabia, the expansion of coffee production and the associated pest risks necessitate a heightened focus on pest management [70]. Innovative practices emphasizing biological control and contaminant management are being developed to suit the region’s unique climate and soil conditions [71]. As pest outbreaks and CC pose increasing challenges, adaptability in pest management strategies becomes vital [72]. This includes utilizing host-plant resistance, natural predators, biopesticides, and synthetic pesticides. Researchers and growers are actively refining IPM techniques to minimize environmental impact while maximizing agricultural yield and profitability; often this entails more precise mapping of pest infestations, variable-rate spraying equipment and GPS-aided monitoring programs [67,69,72].

Ongoing studies indicate a need to reassess pest management approaches and IPM strategies to enhance the resilience of agroecosystems to climatic variability. This includes initiatives like cultivating climate-resilient crops, adjusting planting schedules, utilizing GIS to map pest risks, use of variable-rate delivery equipment and exploring new insecticides with varied modes of action informed by predictive modeling [72].

Image recognition has emerged as a key technology in smart agriculture, enabling rapid, accurate, and non-destructive assessment of leaf health for pest detection and nutrient monitoring [73]. By analyzing visual cues from high-quality images captured via mobile devices, drones, or stationary cameras, computer vision models—particularly deep learning architectures such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and YOLO—can identify pest species, quantify infestation severity, and issue real-time alerts through integrated monitoring systems [74,75]. For example, the study by Andrew et al. [74] achieved a classification accuracy of 99.81% using CNNs for leaf disease detection.

On the nutrient-status side, sensors and image-based models have successfully discriminated N, P and K deficiencies in sugar beet from RGB images using CNNs [75]. Through preprocessing, feature extraction, and classification workflows, these models correlate visual features such as chlorosis, discoloration, and growth anomalies with specific nutritional deficiencies or disease symptoms [76]. The resulting diagnostic outputs provide actionable insights for precision management, reducing reliance on manual inspection and enhancing the speed and consistency of crop-health monitoring.

Pest management is further complicated by the impact of CC on agri-ecosystems. Climate change exerts a profound influence on agricultural insect pests [77]. Its effects are felt both directly on crops and indirectly through the pests that attack them [72,77]. While rising average temperatures is an important factor to take into account, the increasing frequency and unpredictability of extreme weather events pose an even greater challenge to insects and other organisms [78]. Because insects are ectothermic, their survival and behavior depend on external climatic conditions. Over time, they have evolved morphological, physiological, and behavioral traits adapted to relatively stable climatic ranges, making sudden extremes particularly disruptive. These changes in pest behavior force growers and researchers to re-evaluate and adjust control strategies.

Furthermore, shifts in global climate patterns alter species distribution, life cycles, community composition, and overall ecosystem functioning [10]. Evidence shows that CC is already reshaping biodiversity: in a study of more than 1700 wild species, half were reported to be affected by climate shifts. Projections suggest that even under moderate warming scenarios, 15–37% of species could face extinction by 2050 [78].

6.2. Disease Detection and Prevention

Early detection and prevention of diseases in coffee plantations are essential for sustainable production, crop quality and environmental stewardship. Numerous studies have focused on developing methodologies for the early identification of common diseases such as coffee leaf rust (CLR), coffee berry disease (CBD) and Fusarium wilt [79,80,81]. These approaches can leverage AI, neural networks, and deep learning models for precise detection and classification of diseases through advanced visual image processing techniques [82]. These models have demonstrated impressive accuracy rates ranging from 84% to 100% during training and validation.

In recent study conducted in Southwest Saudi Arabia, various Colletotrichum species that cause anthracnose disease in coffee were identified [71,83,84]. The research isolated 27 pure strains of Colletotrichum-like fungi, classifying them within the C. gloeosporioides complex. Pathogenicity tests on leaves and fruit revealed six recognized species, including two novel strains: C. saudianum and C. coffeae-arabicae. Another notable finding relates to CLR, a major destructive disease of Arabica coffee caused by the fungus Hemileia vastatrix. In August 2023, CLR was reported for the first time in the Fayfa district of Saudi Arabia [70]. Additionally, a study investigating the sudden death of coffee trees in Jazan mountains region established a strong relationship between the leaf wilt symptoms and the presence of strains of F. oxysporum and C. musae [84]. Extensive surveys of coffee orchards using GIS can guide the efforts to control these destructive diseases before they reach economic damage thresholds.

7. Precision Coffee Farming Under Abiotic Stress

Abiotic stressors significantly impact coffee production in Saudi Arabia primarily by altering plant growth, development and physiological responses [43]. The main stressors affecting coffee trees are high temperatures, drought, salinity and high irradiance [67]. Coffee plants possess biochemical mechanisms to cope with these challenges. The response starts with alterations of the expression of genes such as those of the DREB subfamily [85]. Studies on the transcriptional activity of specific promoters enhance our understanding of how these genes are regulated in response to abiotic [86]. Research on water-deficit stress has shown changes in metabolites and amino acids, influencing both bean quality and plant longevity [87]. Understanding and managing abiotic stressors is essential for improving coffee yields within PA frameworks [11].

7.1. Water Management Against Drought

Drought poses a critical challenge for coffee growers in Saudi Arabia, necessitating effective water management strategies (Figure 4) [29,43]. Water management in coffee cultivation is pivotal for both productivity and long-term sustainability. Other major coffee-producing regions in the world such as southern Minas Gerais in Brazil and Lam Dong province in Vietnam face similar challenges due to water scarcity and climate variability exacerbated by population growth, deforestation, and unsustainable farming practices [42,88]. A 2025 study by Dinh Nghiep et al. [88] on Vietnamese coffee farming indicated that farmers adapted to changing weather and climate by adopting new practices, such as water management, intercropping with other crops like black pepper, and diversifying crops to improve yields and mitigate risks. To address these issues, various other strategies have been proposed, including optimizing irrigation water usage, adopting environmentally friendly cultivation practices, implementing agroforestry, and protecting forest ecosystems [11,60,89]. Techniques like creating silt pits and employing diverse rainwater management methods can enhance water retention in dryland agriculture. Additionally, modern irrigation technologies such as drip irrigation, overhead sprinkler systems, subterranean irrigation and partial root zone drying can improve water use efficiency (WUE) in coffee orchards [90].

Researchers have also explored the genetic diversity of local coffee populations in Saudi Arabia in search of water stress tolerant cultivars [43]. Furthermore, DNA barcoding was used to identify local coffee genotypes in order to evaluate the diversity of local coffee germplasm that can be used in future breeding programs [24]. A study on arbuscular mycorrhizal fungi (AMF) associated with coffee roots revealed a higher mycorrhizal intensity and spore density at higher-altitude regions, marking the first documentation of AMF presence and composition in Saudi coffee plants [91]. AMF can effectively increase the plant’s capacity to acquire soil water and nutrients as the mycelia of the fungus act like an extension of the root system of the plant [92].

7.2. Mitigation of Temperature and Heat Stress

Mitigating temperature and heat stress in coffee orchards requires a multifaceted approach [11]. Enhancing plant resilience against excessive heat involves integrating efficient irrigation techniques with green infrastructure (GI).

CC has introduced challenges such as rising temperatures and heat stress, negatively impacting coffee production. However, research suggests that elevated carbon dioxide (CO₂) levels can mitigate some adverse effects of heat on coffee plants [93]. Increased CO₂ helps coffee crops endure high temperatures by enhancing the synthesis of protective compounds, boosting antioxidant enzyme activity, and activating genes involved in thermal protection and antioxidant defense [93,94]. Moreover, elevated CO₂ has been shown to improve photosynthesis efficiency and overall biochemical processes in coffee plants under heat stress [95]. These findings underscore one positive consequence of the higher air CO₂ levels at the origin of CC.

In addition to CO₂ management, employing agroforestry systems, mechanized irrigation, drought-resistant varieties, farmer education, rural insurance, and crop nutritional status monitoring can enhance resilience against climate risks [9,40,41].

Research by Baeshen et al. [96] on abiotic stress tolerance in desert plants revealed key pathways and transcription factors that characterize resilient coffee varieties. Marias et al. [97] highlighted that rising temperatures and heat waves threaten suitable habitats for C. arabica. Their study demonstrated the impact of leaf age and heat stress duration on recovery from heat stress. It showed that prolonged exposure leads to reduced photosynthetic efficiency and slower recovery in expanding leaves, thus emphasizing the need for effective heat stress management strategies to prevent crop failure. Artificial intelligence-aided decision-making systems that combine real-time meteorological data with historic climatic trends and crop requirements can greatly assist the growers in managing their plantations [41,66,98].

7.3. Soil Fertility Management

Maintaining a healthy soil is fundamental to improving and safeguarding soil fertility, which is the cornerstone of sustainable agriculture. A fertile well-structured soil is crucial for strong crop development, helping plants better withstand pests and environmental stresses [99]. While many crops remain susceptible to pests even under favorable conditions, effective management of soil, water, and nutrients can reduce pest issues caused by plant stress or nutrient imbalances [100]. Conversely, agricultural practices that harm soil health often result in a growing dependence on external resources like irrigation, synthetic fertilizers, pesticides, and energy-intensive tillage to sustain yields. Within sustainable farming systems, soil is viewed as a living and sensitive component that must be preserved to maintain long-term fertility and stability. Strategies such as incorporating compost and manure, planting cover crops, minimizing tillage, and keeping the soil covered with vegetation or mulch all support soil health. These approaches improve soil structure, enhance nutrient cycling, and guard against erosion and degradation—benefiting both crop yields and the broader environment [100].

In Saudi Arabia, the maintenance of soil fertility in coffee orchards relies chiefly on the use of animal manure and silt transported by rainwater [16,101]. Despite widespread nutrient deficiencies in coffee trees, growers have not adopted the application of synthetic fertilizers as a routine practice [101]. The coffee tree has a large requirement for mineral nutrients [1]. Soil fertility levels in coffee-growing areas often fall below sufficiency thresholds, highlighting the need for nutrient supplementation [98]. Generally, coffee trees respond well to the addition of water-soluble chemical fertilizers [1]. However, organic fertilizers have the advantage of preserving the health of the soil. In fact, research indicates that transitioning from chemical fertilizers to organic or integrated management significantly enhances soil quality and nutrient availability in coffee agroecosystems [102]. Sustainable coffee farming necessitates a balanced use of both inorganic and organic fertilizers, as demonstrated in Ethiopia, where this approach has been shown to boost productivity [103]. Incorporating organic and bio-fertilizers, such as compost, vermicompost and manure, have been shown to improve nutrient availability and enhance tree productivity. Utilizing decomposed coffee husks alongside mineral fertilizers has proven effective in increasing coffee yields [104]. Additionally, employing soil fertility techniques that combine lime, compost, and sand has successfully supported the growth of robust coffee seedlings.

Understanding key soil fertility indicators—including water content, pH, organic matter, and nutrient levels—is vital for evaluating soil health in coffee plantations [42,105]. Future research should explore various organic inputs, their application frequency, and long-term impacts on soil characteristics and coffee production, aiming to develop cost-effective methods for managing soil fertility in coffee-producing regions. GIS tools can make this approach more effective [106,107]. Maintaining a database on soil fertility, tree health and yield in each plot of the orchard can guide fertilization programs giving higher returns on the investment in fertilization [104].

The AMF play a crucial role in enhancing crop productivity and ecosystem sustainability [91]. They improve nutrient uptake and increase plant resilience to abiotic stresses [91]. The presence of these symbiotic fungi has been reported in the coffee orchard soils in Jazan [91]. The phylogenetic analysis identified ten phylotypes, predominantly of the genus Glomus. Commercially available AMF inoculum can be added to the soil to enrich its microbiota and enhance its fertility [91,92]. This is one aspect of PA where the plant’s growing environment is micromanaged to ensure the plant’s wellbeing.

Several PA tools can help the grower build the fertility of his orchard and ensure that his trees receive a balanced nutrition. For instance, building a database of the orchard’s yield, tree performance and health, constructing a digital map of the orchard, using computer applications that identify nutrient deficiencies from leaf symptoms are examples of tools that the growers can use to develop fertilization programs [107].

8. Benefits and Limitations of Precision Coffee Farming

Precision coffee farming offers numerous advantages but also challenges (Figure 6). The benefits include increased productivity, enhanced operational efficiency, and improved product quality [55].

Despite its advantages, the widespread implementation of PA in coffee cultivation faces several hurdles. Limited training and knowledge among farmers impede the adoption of digital technologies [108,109]. Additionally, the absence of reliable electronic tools for coffee harvesters presents challenges for the practical application of PA techniques [51]. To promote the effective use of PA in coffee farming, it is crucial to provide farmers with adequate training and support, thereby enhancing sustainability and productivity.

8.1. Increased Yield and Bean Quality

Various factors contribute to increased yield and quality in coffee plantations. For instance, different forms of fertilizers have different effects on tree growth, yield and coffee liquor attributes. One study demonstrated that using granulated polyhalite, a hydrated sulfate of potassium, calcium and magnesium, as a fertilizer significantly improves both tree yield and key coffee quality parameters such as acidity, body, and balance [110]. Another investigation revealed that applying ethylene at different rates optimizes coffee harvest yield by influencing the proportions of green, ripe, and overripe cherries, as well as the chemical composition and quality of the beverage [111].

Research on split fertilization found that specific fertilization strategies enhanced net photosynthetic rates, bean yield, and various quality attributes, including ash content, total sugar, fat, protein, caffeine, and chlorogenic acid [101,105]. Agronomic management practices, such as planting density, fertilizer type and application frequency, and organic fertilizer application, were identified as key factors influencing yield and quality [112]. Finally, controlled natural fermentation, regulated by temperature and time, was shown to enhance coffee quality, yielding higher scores and special classifications [113].

8.2. Resource Optimization and Sustainability

Resource optimization and sustainability are critical considerations in coffee farming, essential for promoting economic viability within the agricultural sector. Techniques such as linear programming and software analysis can effectively optimize production resources, thereby maximizing benefits [114]. Moreover, integrating energy analysis with process modeling can provide valuable insights into energy flows within agricultural systems, supporting sustainable management practices [115]. Techniques such as geostatistical analysis, remote sensing, and spatial variability mapping contribute to the optimization of sustainable farming practices and effective soil management [50]. By enabling coffee growers to accurately calculate labor costs and develop yield maps, precision coffee farming facilitates informed decision-making [49].

To foster economic stability and growth in coffee farming, efforts should focus on capacity building, product innovation, and the development of sustainable supply chains [116]. Addressing the disproportionately low share of global revenue that smallholders receive in coffee production is vital for ensuring both economic and environmental sustainability. By creating supportive environments and improving livelihoods for smallholders, the overall sustainability of the coffee sector can be enhanced [9,117].

8.3. Economic Viability

Optimizing land use, revenue generation, and production inputs based on both economic and ecological considerations can facilitate sustainable agricultural practices [99]. Numerous studies confirm the economic viability of coffee farming. A financial feasibility analysis of Arabica coffee farming conducted in Indonesia using net present values (NPV), benefit–cost ratios (BCR) and internal rate of return (IRR) metrics demonstrated that coffee cultivation is a financially sound investment, bolstering the country’s economy [118]. Similarly, research on production methods in Ethiopia revealed that coffee cultivation is economically viable with all methods showing positive NPV and BCR values [119]. In Brazil, specialty coffee farmers engaged in short food supply chains (SFSCs) experienced enhanced economic sustainability and profitability through direct sales and premium pricing strategies [106]. Additionally, research on coffee marketing in Indonesia confirmed that coffee businesses are viable with a revenue–cost ratio (R/C) exceeding the figures in [120].

Information technologies made these economic and financial analyses easier further contributing to the sustainability of the sector.

9. Examples of Successful Mitigation Projects

Several case studies highlight successful coffee farming practices. One study examined the dynamics of coffee farming systems in DakLak Province of Vietnam, revealing diverse adaptive capacities among farmers [121]. Other studies from Yemen addressed the socio-economic and environmental challenges facing coffee growers there that led to the decline in production and profitability of their orchards [108,122]. Rainfall irregularities exacerbated by CC, the encroachment of qat cultivation and political unrest were identified as the chief culprits in the decline in coffee production.

A teaching case study showcased the Ninho da Águia Farm in Brazil, where strategic managerial changes facilitated the business’s international expansion [123]. The adoption of agroforestry practices by coffee plantations led to ecosystem preservation while ensuring fair income for coffee producers [34]. A case study from Costa Rica illustrated how a family-run coffee farm successfully implemented agroecological methods, realizing economic benefits through organic coffee processing and innovative product development [124].

10. Successful Implementation of Precision Coffee Farming

Precision coffee farming, which incorporates PA methods into coffee cultivation, has shown promise in enhancing crop management and sustainability. Research has explored various dimensions of precision coffee cultivation, including soil spatial differences and the integration of modern technologies like AI and remote sensing [121]. However, the successful implementation of precision coffee farming rests on overcoming the many challenges related to farmers’ perspectives and their education in digital technology usage [122].

Technologies employed in PA, such as adjusting nutrient and agrochemical application rates and soil humidity sensor-based irrigation, can significantly improve resource use efficiency, reduce crop yield losses, and mitigate environmental risks [125]. Additionally, using digital terrain models derived from unmanned aerial vehicles (UAVs) facilitates streamlined data collection for topographic analysis, enhancing precision coffee initiatives [49,54,62]. Evaluating the time required and labor costs associated with manual execution of agricultural tasks such as fertilization, irrigation and harvest can further support the adoption of precision coffee farming practices [124]. Overall, precision coffee farming offers innovative methods for managing coffee production, enhancing sustainability, and increasing farm revenue, all while protecting the environment [60].

11. Future Directions and Recommendations for Coffee Cultivation in the Kingdom of Saudi Arabia

Precision coffee farming represents an evolving frontier in agricultural technology, harnessing tools like remote sensing, geostatistical analysis, and machine learning to enhance crop management and sustainability in coffee production. The integration of these technologies enables precise monitoring and appraisal of spatial variability in meteorological, soil and plant factors thus facilitating the development of tailored orchard management systems. By addressing CC impacts, improving farming techniques, and optimizing natural resource use, precision coffee farming can play a crucial role in promoting sustainable agricultural practices.

Looking ahead, several future directions for precision coffee production should be considered; these include:

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Data analytics: Utilizing advanced analytical tools allows farmers to interpret complex data and make well-informed decisions based on current, real-time field conditions.

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Sustainable farming practices: Emphasizing eco-friendly methodologies that conserve resources while maintaining productivity.

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Technological innovation: Continuous investment in and adaptation of new technologies will further enhance efficiency and effectiveness in coffee farming.

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Legislative interventions: Advocating for policies that support PA initiatives can create a conducive environment for innovation and investment.

Additionally, the adoption of autonomous systems, on-farm experimentation, and open-source data to gather localized knowledge about agro-economic performance at the subfield level will be vital for future advancements. By focusing on these areas, Saudi Arabia can enhance its coffee cultivation practices and ensure the industry’s long-term viability.

12. Perspectives

Precision coffee farming has the potential to significantly mitigate the adverse effects of CC and other stresses on coffee production by implementing targeted strategies that leverage current data and location-specific insights. To refine methods across diverse regions and cultivation types, further research is essential, particularly in the development of advanced sensors and tailored techniques such as precise delivery systems. Successful implementation of precision coffee farming will require collaboration among researchers, technology developers, government agencies and farmers. By fostering resilience and sustainability, PA can secure the future of coffee farming, benefiting both producers and consumers alike.

In conclusion, emphasis should be placed on addressing climate challenges, evaluating the effectiveness of innovative farming techniques, and recognizing the economic and environmental advantages of PA. Highlighting the limitations of existing studies and providing recommendations for future research will further underscore the potential of precision coffee farming to tackle CC issues and ensure the sustainability of coffee production in Saudi Arabia.