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.
Risk Priority Number (RPN)=S*O*D.
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. CO
2 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.