Water and soil are vital resources needed for food production and sustaining life and the effects of climate change and urbanisation are tightening these resources
(Hatfield et al., 2017). It is anticipated that 9 billion people will need to be fed by 2050 under limited conditions of available arable land, as well as natural resources
(Lal et al., 2017). However, the world economy is expected to face increasing demand for food due to the rising population and standards of living and precision agriculture evolves as a new technology, which will heavily influence the ability to increase global agricultural productivity (
FAO, 2009). Meanwhile, traditional farming methods often lead to environmental degradation, soil depletion and resource inefficiency and in response to these challenges, sustainable agriculture practices combined with precision farming are emerging as a promising solution to ensure food and nutritional security (
Singh, 2022). This is a strategy that involves using tools such as farm management information systems, remote sensing, geographical information systems global navigation satellite systems and spatial statistics in crop production
(Mani et al., 2021). This leads to increased agricultural productivity and profitability, in precision agriculture with a potential to meet the global demand for fuel, fiber and food (
Liu and Khosla, 2010).
Moreover, it is essential to note that precision farming fosters proper utilization of available resources at the plot level, promotes high output efficiency in fields, maintains food security and ensures environmental integrity (
Gebbers and Adamchuk, 2010). Although the power of onboard computers is reducing rapidly due to the reduction in prices with technology becoming better by the day and such include recent robotics, sensors, geo information systems, global navigation satellite system (GNSS) and geomapping, as well as new data processing tools
(Picchio et al., 2019). Meanwhile, scientists and modern farmers have been assessing crop health and performance with various sensing devices such as
in-situ sensors, spectrum radiometer, multi-spectral and hyper-spectral remote sensing and satellite photograph (
Gebbbers and Adamchuk, 2010). The use of these technologies seek the increase of agriculture production for the improvement of the farm’s productivity, profitability and the general sustainability of their farming activities. Such sensing instruments are very important in site specific management and also help to evaluate crops biomass, weed competition, nutritional status and soil characteristics
(Velten et al., 2015).
However, newly designed advanced modern field equipment is capable of collecting enhanced data of high resolution leading to improved and accurate crop management and development
(Lowenberg-DeBoer et al., 2021). Meanwhile, sustainable development goals in agriculture are indexed in Fig 1. This illustration provides a schematic sketch for the overview of precision agriculture technology. Sustainable soil and crop management enhances the long-term sustainability of Agriculture, so it is a must
(Shaheb et al., 2022). There are many factors that result in variabilities, such as field topography, soil properties, nutrients, crop characteristics, water contents, meteorologies, pests,
etc.
(Lee et al., 2021). For instance, some scholars advocate for the use of climate-smart farming, integrated soil management, sustainable intensification, precision farming, among other methodologies, in achieving sustainable agricultural production
(Garibaldi et al., 2019). However, these goals can be accomplished if all the best management practices for agroecosystems are used consecutively which makes use of resources, preserves soil and gives both current and future social and environmental benefits without violating anyone of those
(Shaheb et al., 2022).
Climate-smart agriculture (CSA) is an approach to ensuring food security by directing entire agricultural systems towards resilient practices, sustainable development and climate change adaptable strategies (
Lipper and Zilberman, 2018). A key element of CSA is climate-smart pest management (CSPM), which provides advantages for preserving agricultural systems by lowering chemical use
(Challinor et al., 2022). This analysis focuses on CSPM and phytosanitary issues related to climate change, examining areas that require more effective interventions (
Alexander, 2019). It investigates how to modify pest management in response to weather events to support the long-term viability of agricultural systems and discusses the challenges related to the implementation and use of CSPM
(Heeb et al., 2019). Precision agriculture (PA) emerges as a crucial strategy for sustainable agriculture in the twenty-first century (
Lipper and Zilberman, 2018). It involves in sophisticated information, communication and data analysis techniques, aiming to minimize environmental effects, reduce water and fertilizer losses and enhance agricultural yields
(Heeb et al., 2019).
However, the fourth agricultural revolution, driven by advancements in information and communication technology, emphasizes the role of technologies like artificial intelligence (
AI), big data analysis, remote sensing,
GPS,
GIS and the Internet of Things (
IoT) (
Velásquez et al., 2018). These technologies are instrumental in optimizing agricultural operations, reducing losses and increasing productivity
(Ristaino et al., 2021). IoT technology systems, such as cloud computing and wireless sensor networks, are integral to smart farming operations, enabling disease and pest monitoring and automated irrigation systems
(Li et al., 2021). AI techniques, including machine learning, facilitate precise and automated application of pesticides, fertilizers, water and herbicides by predicting crop yield and monitoring soil moisture (
Velásquez et al., 2018). However, remote sensing instruments generate big amounts of data used in precision agriculture, processed through big data analysis, machine learning and intelligent automation (
Roßmann et al., 2018). Moreover, cloud computing platforms play a crucial role in processing, distributing and utilizing large datasets for various applications in precision agriculture
(Choudhary et al., 2016). This review explores the transformative synergy between sustainable agriculture and precision farming, envisioning a paradigm that addresses global challenges of environmental degradation, resource scarcity and increasing food demand. Moreover, by integrating eco-friendly farming practices with advanced precision technologies, this review highlights the potential for creating a resilient and efficient agricultural system.
Precision agriculture
One of the modern farming approaches is precision agriculture that uses measurement of crop growth combined with modern technologies
(Shafi et al., 2019). Researchers have also adopted different names for it,
i.e., “Site-specific farming”, “Spatially variable crop production”, “Grid Farming” and “Technology-based agriculture”
(Sishodia et al., 2020). However, some scientist gives an all-inclusive explanation that defines precision agriculture simply as “making use of IT information to enhance decisions relating to agricultural production and marketing, financing as well as people”
(Karunathilake et al., 2023). Essentially, precision agriculture maps out in-field variability using
GIS in order to maximize crop yield through efficient resource utilization
(Su et al., 2023). However, the sensor-IT based technologies which include wireless
GPS technology, provides a framework for precision agriculture to manage unforeseen uncertainty, which in turn results into informed decision making that is aimed at ensuring maximal productivity within the agricultural system
(Saranya et al., 2023). For the precision agriculture to occur, accurate data must be collected timely. Nevertheless, it should be analysed appropriately, translated correctly and managed correctly, at the right scales and frequencies
(Gokool et al., 2023). Moreover, a timeline of precision agriculture is indexed in Table 1.
Importance of precision agriculture
Precision Agriculture is an information technology-based farming method that enhances agricultural output, profitability and efficiency by optimizing resource use and crop management
(Andersen et al., 2023). Continuous technological advancements, including autonomous tractors, agricultural drones,
GIS and sensors empower farmers to operate with unprecedented efficiency
(Abdullah et al., 2024). However, according to researchers, precision agriculture represents an information revolution driven by new technologies, resulting in a more accurate farm management system
(Huo et al., 2024). Farmers must consider within-field variability, as conventional farming practices often involve uniform application of inputs across the entire field without accounting for spatial changes in soil characteristics such as types, electrical conductivity (EC), moisture content (MC), pH and nutrient availability and factors like land topography, soil texture and historical management practices can contribute to spatial variability in soil (
Ghadirnezhad Shiade et al., 2024). In fact, in England, crop yields showed considerable spatial variation even at managed fields reflecting the influence of soil variability, rainfall and field operations
(Godwin et al., 2003). However, farm productivity and profitability is maximized through accurate crop input management (
Bramley, 2009).
Climate smart agriculture
In 2009, Climate-Smart Agriculture (CSA) was introduced to promote sustainable agricultural systems by integrating efforts to mitigate climate change and ensure food security (
Lipper and Zilberman, 2018). The Food and Agriculture Organization (FAO) officially introduced the CSA concept in 2010
(Scherr et al., 2012). However, CSA aims to establish globally relevant agricultural management principles addressing climate change, with strategic goals including reducing greenhouse gas emissions, adapting to climate change, enhancing household resilience and increasing agricultural output sustainably
(Lipper et al., 2014). CSA incorporates various sustainable practices to help farming communities and mitigate the effects of climate change
(Azadi et al., 2021). While international organizations like the World Bank and FAO embraced CSA, disputes arose in policy discussions on sustainability and climate change (
Taylor, 2018). International initiatives now focus on improving CSA adoption, considering varying country contexts and addressing the dual challenges of climate change and poverty
(Huo et al., 2024). Current efforts in least developed nations primarily target food, energy and water, though plant protection remains crucial for sustainability (
Taylor, 2018).
The FAO has been covering CSA to a large extent, but a few works have referred to “Climate-Smart Pest Management” (CSPM), which is referred to as integrated pest management is the approach employed in CSA
(Heeb et al., 2019). Integrated Pest Management (IPM) is an approach that considers all pest management options and combines them with measures that discourage pests so they don’t develop
(Sekabira et al., 2022). The latest information provided in the study of CSPM has resulted in optimum response dynamics controlling pests during growing seasons hence reducing losses due to pea aphids’
(Du et al., 2022). This approach integrates ecological, sustainable and adaptive measures to mitigate the risks associated with pests in agriculture and other ecosystems
(Murage et al., 2015). Climate-smart pest management focuses on utilizing environmentally friendly and low-impact interventions, such as integrated pest management (IPM) practices, biological control methods and precision agriculture technologies
(Bouri et al., 2023). By incorporating climate data and predictive modeling, farmers can anticipate pest outbreaks, optimize resource use and enhance resilience to climate-related challenges, ultimately contributing to more sustainable and resilient agricultural systems in the face of a changing climate (
Roy, 2022). However, the climate smart pest management is indexed in Fig 2.
Application of precision agriculture
Recent agricultural practices, notably precision agriculture is mostly based on three concepts of efficiency, economy and environment
(Sekabira et al., 2022). The use of a best management practices method in agricultural field crop is the basis for this
(Huo et al., 2024). However, precision agriculture technologies are embedded with technical skills and knowledge that facilitate the uptake of safe and eco-friendly soil and crop management approaches.
Geospatial applications
“Geospatial technologies” encompass a variety of instruments utilized for geographical mapping and Earth’s surface analysis, including Remote Sensing, geographic information systems, global navigation satellite systems (GNSS) and internet mapping technologies
(Scherr et al., 2012). Automated field machinery in modern agriculture relies on positional data,
GIS and
GNSS guidance for accurate field operations
(Andersen et al., 2023). Remote sensing technology, with a resolution of just over one meter, collects detailed imagery of Earth’s surface, transforming how geographic data is visualized and shared
(Picchio et al., 2019). Remote sensing, combined with
GIS and
GNSS, is essential for precision agriculture, helping segment cropland into small management zones based on factors like soil types,
pH,
EC,
MC and crop characteristics
(Velten et al., 2015). However, GIS databases store geo-referenced observations from remote sensing, forming the foundation of PA technologies
(Azadi et al., 2021). For instance, remote sensing imaging links chlorophyll content to crop growth and productivity
(Abdullah et al., 2024).
Remote sensing
Remote sensing technology makes use of image data derived from airborne cameras and sensor platforms
(Shaheb et al., 2022). Lately, there has been an observed increase in airborne remote sensing platforms such as remote sensors on aeroplanes and satellite
(Challinor et al., 2022). These technologies are fundamental in determining soil properties based on combined sensors’ spectral data as well as integrated geo-referenced soil/crop fields data (
Alexander, 2019). Compared to other techniques in this way is more accurate, requires less investment and takes shorter duration
(Heeb et al., 2019). Drones of airborne and ground nature have utility for agricultural planting, pesticides, monitoring crop growth, irrigation, soil and field studies and health assessment (
Roßmann et al., 2018). The sensors of these platforms are able to provide data associated with crop parameters like growth rate, leaf area index, leaf temperature, infestation of the pests or diseases as well as soil moisture content, nutrient status (fertility), compaction and temperature
(Choudhary et al., 2016).
Site-specific soil and crop management
Site-specific soil and crop management is a strategy that adjusts farming procedures based on data collected from an appropriate source so as to compensate for the variable soil conditions in each site
(Kumar et al., 2024). Deep tillage is a precision technique that has got a lot of promise as well as reduced tire pressure and control traffic farming (
De Caires et al., 2024). Some of the precision agriculture technology such as remote sensing helps manage soil compaction
(Xiao et al., 2024).
Yield monitoring and mapping
In the contemporary agricultural landscape, modern combine harvesters come equipped with integrated yield monitors, serving as invaluable tools for grain production
(Hatfield et al., 2017). These monitors empower farmers to evaluate and delineate the impact of weather, soil properties and management practices on grain production
(Lal et al., 2017). However, the accuracy of these devices relies on proper installation, calibration and operation (
Singh, 2022). To gain detailed insights into soil health, farmers often employ soil sampling followed by laboratory analyses, complemented by the use of yield monitors to comprehend spatial variability in crop yield
(Mani et al., 2021). The yield monitoring system facilitates the collection of geo-referenced yield data, enabling the creation of yield maps that visualize crop performance variability (
Liu and Khosla, 2010).
Agricultural robots
The landscape of agriculture has been radically transformed by the integration of robotics and applications of robotics in agriculture, forestry and horticulture are constantly evolving
(Wang et al., 2024). To achieve precision in crop production management at the individual plant level, the adoption of autonomous and robotic technologies is becoming increasingly crucial
(Paul et al., 2024). However, emerging technologies such as autonomous tractors, drones, crop harvesting robots, seeding machines and robotic weeding are revolutionizing precision agriculture (
Kappagantula, 2024). These autonomous platforms can handle tasks from field preparation to crop harvesting, offering advantages that surpass traditional machinery
(Bale et al., 2024). Recently, there is growing confidence in the potential of robotic weeding, scouting and the application of crop production inputs through
UAV/drone technology, particularly in the Midwest United States
(Balyan et al., 2024). However, advance application in precision agriculture are indexed in Fig 3.
