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By Shalini Gakhar and Steve Klassen (International Rice Research Institute)
The impact of drone technology is growing across various sectors, and agriculture is no exception. Globally, the market for agricultural drones is worth US $32.4 billion, compared to US $127 billion for the total value of drone-powered solutions.
For smallholder farmers in countries such as India and the Philippines, drones have the capability to function as an integrated solution for precision agriculture, enabling automated seeding, real-time crop health monitoring, precision spraying, and other critical farming operations. Their versatility lies in the multitude of sensors (Table 1) that can be mounted to gather information over large areas.
To realize this potential, the International Rice Research Institute (IRRI) is working to integrate drones with other technologies to create comprehensive solutions for farmers. These include internet of things (IoT) devices, information and communication technology (ICT) advancements, and artificial intelligence (AI).
As rice farming is a cornerstone of agriculture in both India and the Philippines, each country exhibits distinct characteristics in terms of scale, challenges, and farmer demographics. Though both geographies are embracing drone technology to enhance rice cultivation, the application and impact of these technologies differ significantly due to distinct agricultural landscapes, socio-economic conditions, and policy frameworks. India, being one of the largest rice producers globally, has over 145 million farmers, with approximately 45 million hectares dedicated to rice cultivation. However, Indian rice farmers face challenges such as water scarcity, outdated farming practices, and pest infestations.
In contrast, the Philippines, with around 2.5 million rice farmers and about 4.8 million hectares under rice cultivation, contends with issues like frequent typhoons, flooding, and limited access to modern farming technologies. These unique challenges and varying scales of operation influence how each country integrates drone technology into their agricultural practices to enhance productivity and sustainability. In India, the focus has been on large-scale adoption to optimize vast rice-growing areas, whereas the Philippines has been pioneering innovative uses of drones to tackle the unique challenges posed by its diverse topography and climate variability.
Drones offer various applications for precision agriculture. Photo credit: IRRI
Drone technology in Indian agriculture has been steadily progressing, driven by efforts to scale its adoption through government support and private sector participation. Initiatives like the Digital Agriculture Mission and Sub-Mission on Agricultural Mechanization (SMAM) have promoted the use of drones by offering subsidies and encouraging farmer producer organizations (FPOs) to incorporate drone services. Scalable drone solutions tailored for Indian agriculture have emerged in the private sector, including rental models that make the technology more accessible to smallholders. States like Punjab, Haryana, Andhra Pradesh, and Tamil Nadu have been at the forefront of adopting drone technology, leveraging it for large-scale trials and pilot projects aimed at improving rice productivity.
There have been several successful implementations of drones in Indian agriculture. For instance, a study by IRRI demonstrated the effectiveness of drones in monitoring nitrogen levels in rice fields through multispectral imaging, allowing farmers to optimize fertilizer use. The work was carried out as part of the CGIAR Initiative on Digital Innovation.
Despite its benefits, the adoption of drones in Indian agriculture faces multiple challenges. Near-real-time solutions are difficult to achieve, as data processing can be time-consuming and digital penetration in rural areas remains low. Maintenance costs are high, and even when farmers can identify crop-soil characteristics using drones, they often lack the resources to act on this data effectively. Rice cultivation poses additional challenges, as crops are frequently submerged, making it difficult for sensors to capture accurate data due to interference from water. The costs of drone solutions are also a barrier for smallholder farmers, who often have fragmented landholdings of less than one hectare.
Uptake of drones for agriculture is growing in the Philippines, but remains short of widespread adoption primarily due to high costs, lack of technical expertise, and fragmented landholdings that limit large-scale use. However, guiding standard operating practices for drone-based data collection and analysis, particularly in rice cultivation, are being developed through collaborations between research institutes like the IRRI and the Philippine Rice Research Institute (PhilRice) through the Drone4Rice project. These guidelines help standardize processes such as vegetation index mapping and nutrient assessment, making data analysis more accurate and accessible for farmers.
IRRI and PhilRice have also been at the forefront of integrating advanced sensors into drone technology for rice cultivation. For instance, IRRI has used multispectral sensors to monitor chlorophyll levels, which are indicators of nitrogen sufficiency in rice fields, enabling more precise fertilizer application. Thermal imaging sensors have also been employed to assess water stress and soil moisture levels, crucial for managing irrigation in the Philippines’ variable climate.
The country’s rugged terrain, with many rice fields located on slopes or in hard-to-reach areas, can make drone navigation difficult. Additionally, the tropical climate poses challenges as high humidity and unpredictable weather patterns can affect drone performance and sensor accuracy. Persistent rain and strong winds, especially during the monsoon season, limit the window for drone flights, making it difficult to gather continuous, real-time data.
The government of the Philippines, through initiatives such as the Rice Competitiveness Enhancement Fund (RCEF), has been supportive of integrating advanced technologies like drones into agriculture. However, there is still a need for more structured programs to provide subsidies, training, and technical support specifically for drone operations.
Drawing on their unique experiences and capacities, countries like India and the Philippines can collaborate to share insights, accelerating adoption for both smallholder farmers and large enterprises.
Multispectral and hyperspectral sensors help monitor crop health, soil moisture, and nutrient levels, while AI models analyze this data to optimize irrigation, fertilization, and pest control. Drones can play a vital role in managing greenhouse gases (GHG) by providing accurate baseline data and reducing human intervention, minimizing errors across multiple locations. Big data can further aggregate this information, enabling more sustainable farming practices.
For smallholders, drones complement traditional practices, offering data-driven insights to improve yields and incomes. Large enterprises benefit from streamlined operations and cost efficiencies. Incentives like subsidies and affordable services can drive adoption, while collaboration between countries can create jobs for marginalized communities, especially women and youth, through training in drone operation and data analysis.
Despite these efforts, the adoption rate is still slow, as many farmers view drones as a costly technology that may not provide immediate ROI. For drones to be seen as a viable solution, there needs to be more affordable service models, increased awareness of the technology’s benefits, and continuous support from both the government and private sector to address challenges in training and infrastructure.
Scaling technologies like variable rate technology (VRTs) will require infrastructure, collaboration, and local expertise. Scientists can bridge research and practice by piloting use cases such as precision spraying, pest monitoring, and soil health assessments. Drones, combined with localized advisories, can make farming more efficient and data-driven, ultimately improving productivity and incomes.
Table 1. Types of drone-mounted sensors and their agricultural applications
Operates in visible range of electromagnetic spectrum
Used for basic level of monitoring such as crop growth assessment
Very less processing time needed to do the post data acquisition analysis.
Used for intermediate level of monitoring such as crop and soil health analysis
Helps in deriving some crop/soil-based indices such as normalized difference vegetative index (NDVI)
Acquires images across hundreds of narrow, contiguous spectral bands
Provides detailed information about the chemical and physical properties of crop and soil
High accuracy in terms of soil-crop condition assessment
High computational cost and resource intensive
Each parameter is identified using unique spectral signatures based on reflectance values acquired by the sensors.
Thermal maps generated aids in getting insights for temperature variations throughout the landscape.
Data interpretation is pretty crucial
Used for topographic mapping and accessing vegetation structure.
High precision and penetration capability
Relatively expensive as compared to other sensors
Calibration and sensitivity is important to detect low concentration of gasses too.
Environmental interference is a challenge
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