Modelling the Barriers to the Implementation of Drone Technology in Indian Agriculture

In view of the influence of disruptive technologies in Agriculture 4.0, with technologies like IoT, AI, Robotics and Big Data Analytics, face farming in the world changes (Narmilan and Puvanitha 2020; Umme et al., 2020; Konstantina et al., 2021; Spanaki et al., 2022). Said technologies drive smart farming, increasing productivity and lowering costs while being more sustainable because of data-driven decision-making and techniques for precision agriculture. Application of disruptive technologies in agriculture addresses challenges like climate change, population growth and resource scarcity, all the applications ensure efficient manpower use and resources by better crop yield and quality. IoT for monitoring (Balaji and Santhosh, 2021; Dayananda et al., 2024), AI for predicting and robotics for automating-these types of disruptions shall change the agricultural sector toward resilience, productivity and sustainability with productivity and sustainability as part of the new paradigms that they can assure for generations to come (Aggarwal and Sharma, 2022). Since its entrance into the environmental and agricultural sector, drone technology has been on the list of very disruptive technologies in farming (Bryceson, 2019).                             
 
Drones are increasingly being used in agriculture due to their potential benefits in modern farming practices. These technologies include fruit picking, field analysis, disease detection and pest management (Bharambe et al., 2020; Kailashkumar et al., 2023; Rus et al., 2023; Metagar and Walikar, 2024). They are being developed with foldable features, enhancing efficiency and productivity. The integration of drones in agriculture has revolutionized traditional farming, improving sustainability and productivity. This technological revolution is transforming the agricultural sector globally. Similarly, such technologies are also being accepted and embraced in developing economies like India. In India, the use of drones in agriculture is hampered with several issues despite its potential benefit. The first issue lies in the adverse effects that are caused on the farmers due to health risks of manual pesticide spraying (Michael et al., 2022). Similarly, overreliance on a traditional farming system to satisfy the demand for increasing population with limited land component suitable for agriculture further presses the dire need for the improvement of farming methods, where drones certainly hold a possible great solution. Automatic spray via drones for pesticides and fertilizers could replace manual spray technology, lessen the impact on human health and attain efficiency with large coverage of land in agriculture.
       
Overcoming challenges such as assembly errors, sizing issues and weight balancing problems during drone development is crucial to realizing the full potential of drone technology in Indian agriculture. This motivated the authors to undertake this study with following research objectives:
 
Research question 1
 
What are the barriers to the adoption of drone technology by farmers in Indian agriculture?
 
Research question 2
 
Are there interrelationships among the barriers? If yes, what are the interrelationships among the barriers?
 
Review of literature
 
A detailed literature study is undertaken to determine the barriers impeding the wider application of drones. Table 1 shows the gaps founded in the past literature with respect to the use of technological advancement s like Arial technology-drones in agricultural purposes.
 

       
Further, a comprehensive set of challenges as identified through review of literature are represented in Fig 1.
 

This research was conducted by the authors at Symbiosis Institute of Business Management, Nagpur, Symbiosis International (Deemed University) Pune in 2024. A questionnaire-based study was conducted to identify major barriers in the use of drones in agriculture in India. Experts from farming business owners were involved. The study ranked these challenges on a 1-10 scale and used a Failure Mode and Effect Analysis approach. The study also identified interrelationships between the barriers using Interpretive Structural modelling. The data was collected in two phases.
 
Failure mode and effect analysis
 
Barriers prioritization
 
The system or process is identified by recognizing its barriers, assessing the severity of each risk using a scale of 1 to 10, determining the frequency of risk occurrence (O) and the rating of detection (D). The severity of each risk is then rated, ranging from 1 to 10, ensuring a comprehensive risk assessment.        
Table 2 shows the detailed FMEA constructed thorough the inputs from the farm owner experts.
 

 
Analysis of FMEA
 
Based on the RPN of all the challenges identified, top 15 challenges with highest RPN are considered for further analysis, given below.
1. High initial costs.
2. Economy and employment.
3. Intentional hacking, cyberattacks and terrorism.
4. Social anxiety about automation.
5. Liability for drone owners.
6. Congested airspace for manned aircraft.
7. Adverse weather conditions.
8. Violations of rights.
9. Unauthorized usage of data and blackmail.
10. Unauthorized usage of drones.
11. Drone routes.
12. Operator certifications and training.
13. Drones theft.
14. CO₂ emission.
15. Obstacle and collision avoidance.
       
Thus, these above mentioned failure modes become the high level risks in the system.
 
Interpretive structural modelling
 
Interpretative Structural Modelling is a general-purpose method for analysis and a decision-support system that discovers and structures relationships among the key concerns or problems. It provides a structured approach for attending with complex situations. Table 3 shows the first step of constructing the structural self-interaction matrix.
 

       
The contextual relationship between the risks amongst themselves is obtained consulting academic and industrial experts. Based on four symbols, on the relationship between the various factors, the SSIM matrix is prepared The terminologies used to explain any two factors ‘i’ and ‘j’ are in the following words.
V: if factor i influences on factor j.
A: if factor j influences on factor i.
X: if the both factors influence on each other.
O: if the factors are unrelated.
       
The initial reachability matrix is formed as shown in Table 4.
 

       
Transitivity is performed on the initial reachability matrix to identify indirect relationships among the barrier variables as shown in Table 5. Transitivity means when variable A impacts variable B, variable B impact variable C, then the relationship also exists between A and C.
 

 
Partitioning of reachability matrix into different levels or level partitioning
 
The reachability matrix is broken into distinct levels using an algorithm-based level partitioning procedure. This creates a multilevel interpretive structural model based on risk variables. Reachability, antecedent and intersection sets are formed for each barrier, allowing level partitioning.
 
Table 6 shows the result of first iteration as element 4 being the top barriers in the implementation of drones in agriculture.
 

 
Table 7 shows that iteration 2 reveals 5, 6, 7, 8, 11, 12, 14 and 15 are level 2 barriers.
 

       
Table 8 shows the result of third iteration as element 9 and being the next level barriers in the implementation of drones in agriculture.
 

       
Table 9 shows the result of third iteration as element 3 and being the next level barriers in the implementation of drones in agriculture.
 

       
Table 10 shows the result of final iteration as element 2 being next level barrier and the bottom barrier in the system is element 1.
 

 
Reachability matrix expressed in conical form
 
Combinations of the row and column risk variables in rank order from high to low form a conical matrix. Adding up the 1s in the rows and columns turns out correspondingly; what is called the driving power and the dependent power.
 
Reduced conical matrix (CM)
 
Node digraph
 
Combinations of the row and column risk variables in rank order from high to low form a conical matrix as shown in Fig 2, based on the driving and the dependency powers of the barriers as shown in Table 11.
 

 

 
ISM model
 
The nodal digraph is transformed into ISM model by changing the nodes with the barriers associated with the node number as shown in Fig 3.
 

Fig 3 highlights the economic challenges and factors in integrating drone technology into Indian agriculture, highlighting its potential for enhancing efficiency and productivity. However, it also highlights the economic difficulties inherent in integrating drones, as they offer superior gains compared to traditional methods but require a costly investment in technology (Kwon et al., 2017). Drones in logistics require investments in software, sensor packages and surveillance cameras for safety and efficiency. This employment may impact labor markets, potentially increasing social inequality. Security challenges like drone theft, hacking, cyberattacks and terrorism pose significant risks to privacy, security and ethical integrity (Chang et al., 2017 and Osakwe et al., 2021). Therefore, strategies must be comprehensive in light of the above regulatory measures, technology safeguards and increasing awareness. Environmental factors, too, constitute an integral part of the smooth conduction of drone operations. Although drones were nearly always shown as environment-friendly solutions in most literature, this is not the case if the customer is at a distant location from the service depository. This is because drones may produce more CO₂ emissions than trucks (Goodchild and Toy 2017). Weather, routes, CO₂ emission and obstacles are very closely knit factors that efficiently and effectively impact the success of drones. Such combined challenges can be tackled only through a holistic solution where weather issues will be addressed holistically with route reduction, CO₂ emission issues and safety aspects for safely and sustainably deploying unmanned aerial systems (Stolaroff et al., 2018; Kuru, 2021; Kapoor et al., 2021; Toria and Clark, 2021). Responsible governance of drone operations requires balancing factors like owner liability, airspace congested, operator certificates and training. A multidimensional approach is needed to spur innovation while safeguarding safety, privacy and social wellbeing (Sah et al., 2020). Automation fears include job replacement, long-term business overwhelm and equal benefit sharing. An all-encompassing approach, empowering farmers and local communities to adapt and preserve their lifestyles, is crucial for sustainable development and overcoming these anxieties.

High upfront costs in acquiring and maintaining drone technology in agriculture in India are quite prohibitive for small-scale farmers to invest in modern farming practices. Automation could mean that rural employment may get displaced and hence concerns over unemployment and economic instability. Drones are prone to hacking, cyberattacks and theft, which can compromise data security and operational safety. Blackmail and unauthorized use of the drones are other results of the unauthorized misuse of data. Weak regulations and enforcement measures may also result in unsafe and troublesome operations, besides unauthorized or illegal uses. Liability issues on accidents, property damages, or charges on privacy violation haunted the owners of the drones. Congested airspaces for manned aircraft, limited operator certifications and training are other few factors that hinder wide-scale adoption of drones. Other predisposing factors to social anxiety about automation include privacy concerns or bad weather. This kind of concerns demand comprehensive strategies with elements, including clear regulatory frameworks, investment in training and technology and community engagement.

The authors declare that they have no known competing financial interests that could have appeared to influence the work reported in this paper.