Agricultural drone services as a source of income and employment generation in Andhra Pradesh

Introduction

Agriculture is increasingly adopting precision-based technologies to improve the efficiency and timeliness of farm operations. Among these technologies, agricultural drones have emerged as useful tools for crop spraying, crop monitoring, pest and disease management and precision application of agricultural inputs. Their ability to cover large areas within a short period and reduce dependence on manual labour has increased their relevance, particularly under conditions of labour scarcity and rising labour costs. Dutta and Goswami (2020) [2] highlighted the potential of drones to improve resource-use efficiency and support various agricultural operations. Similarly, Yadachi et al. (2023) [12] emphasized their contribution to reducing labour, time and input requirements. The increasing use of drones has also created opportunities for service-based agricultural enterprises. Farmers can access drone services without bearing the high cost of purchasing and maintaining the equipment themselves. This service-based model provides opportunities for trained individuals to operate drones as service providers and generate income by serving multiple farmers. Simula (2021) [10] identified agricultural drone services as an emerging business opportunity, while Parmaksiz and Cinar (2023) [8] found that farmers showed greater willingness to rent UAV services rather than purchase the technology. These findings indicate the potential for custom-hiring and service-based drone operations in agriculture. Evidence from India further demonstrates the economic potential of drone applications. Meera et al. (2023) [7] reported that crop spraying was the major application of agricultural drones and identified labour savings, cost reduction and improved operational efficiency as important benefits. Yazhini et al. (2024) [13] also found that droneassisted cultivation could reduce cultivation costs and improve net returns. In Andhra Pradesh, Ravi Goud et al. (2024) [9] identified considerable potential for drone spraying, although inadequate maintenance facilities and limited availability of spare parts were reported as important constraints. Wider adoption of such services can therefore create increasing demand for trained drone operators and provide opportunities for rural service enterprises. The livelihood and employment dimensions of drone technology have received increasing attention in recent years. Belton et al. (2025) [1] noted that the expansion of agricultural drone services can help address labour shortages while creating new forms of agricultural service provision and rural livelihood opportunities. In India, initiatives such as the Namo Drone Didi Scheme have further encouraged the use of drones as a source of income and employment by supporting Self-Help Groups in providing drone services to farmers (Government of India, 2024, 2025) [4,5] . Sri Lekha et al. (2025) [11] also reported substantial income gains and the emergence of skilled employment among women involved in drone operations. However, most existing studies have focused mainly on farmer adoption, perceptions, productivity, input savings and cost reduction. Comparatively less attention has been given www.extensionjournal.com to drone pilots themselves as emerging rural entrepreneurs, particularly with regard to changes in their overall income, income distribution and employment generation. These aspects are important for assessing whether drone services can provide sustainable livelihood opportunities for operators. Against this background, the present study examines the income and employment opportunities generated through agricultural drone services among drone pilots in Andhra Pradesh. It assesses changes in annual income, the effect of drone income on income inequality, and employment generated through drone operations. The study thus provides a service-provider perspective on the livelihood potential of agricultural drone technology.

Methodology Study area and sampling:

The empirical analysis was based on primary data collected from 60 agricultural drone pilots operating in Andhra Pradesh. The sampled pilots were engaged in providing drone-based agricultural services, particularly spraying operations. Information was collected on income from farming, non-farm activities and drone services, working days, helpers employed, number of farmers served and area covered. The analysis used the pilot as the unit of observation and compared annual income before and after inclusion of drone-service income.

Income comparison: Annual income without drone services was defined as the sum of farm and non-farm income. Annual income with drone services was calculated by adding income generated from the drone unit. The percentage increase was calculated as: Increase (%) = [(Income with drone -Income without drone) / Income without drone] × 100.

Significance testing: A paired t-test was used to test whether the mean difference between annual income before and after inclusion of drone income was statistically different from zero. Because paired income data may not satisfy normality assumptions, the Wilcoxon signed-rank test was also used as a non-parametric robustness check. Cohen's d for paired observations was used to indicate the magnitude of the income change.

Income inequality: Income inequality was measured using the Gini coefficient and Lorenz curve. The Gini coefficient ranges from 0, representing complete equality, to 1, representing complete inequality. The change in the Gini coefficient was assessed by comparing the distribution of total annual income without drone income with the distribution after adding drone income.

Income-source decomposition:

To identify the role of each income source in overall inequality, the Gini decomposition framework associated with Lerman and Yitzhaki was used 12 . For income source k, the contribution depends on its income share (Sₖ), concentration coefficient (Cₖ) and the resulting product SₖCₖ. The percentage contribution to total inequality was derived from the source-specific contribution relative to the overall Gini coefficient. A source with a concentration coefficient below the overall Gini can have an inequalityreducing effect.

Employment and outreach: Employment generated was estimated as the number of pilots and helpers engaged multiplied by the mean number of working days per person per year. Service outreach was assessed from the total number of farmers served and the total area covered by the sampled drone operators.

Results and Discussion

Income generated through drone services

The results demonstrate a substantial contribution of drone services to the earnings of the sampled pilots. Before income from the drone unit was considered, mean annual income was ₹7,42,594, consisting of ₹6,03,394 from farming and ₹1,39,200 from non-farm sources. Drone services generated an additional ₹7,03,030 per pilot per year, raising mean annual income to ₹14,45,624. Thus, the inclusion of droneservice income increased average earnings by approximately 95 per cent. The paired t-test produced a value of 18.94 with p < 0.001, indicating a statistically significant difference between income before and after inclusion of drone income. The Wilcoxon signed-rank test also yielded p < 0.001, providing consistent evidence without relying on the normality assumption of the paired t-test. The paired Cohen's d of 2.45 represents a very large effect. The magnitude and statistical consistency of these results suggest that drone services were not a marginal supplementary activity for the sampled pilots; rather, they represented a major component of their annual earning capacity. The distributional results indicate that the benefits of drone services extended beyond the increase in mean income. Median income increased from ₹5,43,700 to ₹12,85,936, while the minimum income increased from ₹97,200 to www.extensionjournal.com ₹4,59,366. The 25th percentile rose from ₹3,17,000 to ₹9,77,623, indicating a marked improvement in the lowerincome segment of the sample. The Gini coefficient declined from 0.44 before inclusion of drone income to 0.26 after its inclusion. This represents a reduction in the Gini coefficient of about 41.3 per cent, suggesting that the additional income source was relatively more evenly distributed across the pilots than their pre-drone income sources. The Lorenz-curve interpretation is consistent with this result. After inclusion of drone income, the curve moves closer to the line of perfect equality, reflecting a more balanced distribution of income. Thus, drone services appear to have had a dual livelihood effect: they raised average earnings and reduced relative income inequality within the sampled group. The decomposition analysis provides an explanation for the reduction in overall income inequality. Drone income constituted 48.60 per cent of total income but contributed only 25.60 per cent of total inequality. Its concentration coefficient of 0.137 was substantially below the overall income inequality level, indicating that drone income was comparatively more evenly distributed among the pilots. In contrast, farm income accounted for 41.70 per cent of total income but contributed 68.60 per cent of inequality, reflecting its higher concentration coefficient of 0.426. Nonfarm income made a relatively small contribution of 5.90 per cent. These results indicate that drone income partly offset the unequal distribution associated with farm income. Agricultural drone services generated self-employment for all 60 pilots and additional employment for 42 helpers. Based on the reported average of 124 working days, the activity generated an estimated 12,648 person-days of employment per year across the sample. This demonstrates that the livelihood effect of drone technology was not limited to the owners of the equipment. The activity also created work opportunities for persons assisting with field operations, transport, battery handling and other service-related tasks. The outreach of the service was considerable. The pilots collectively served 26,930 farmers, with an average of 441 farmers served per pilot, and the average area covered was 1,495 ha per year. These figures show that drone service enterprises simultaneously functioned as income-generating businesses for the operators and as mechanization-service providers for a large number of farmers.

Effect of drone income on income distribution

Decomposition of income inequality by source

Employment opportunities generated through drone services

Conclusion

The study established that agricultural drone services made a substantial contribution to the income and employment opportunities of the sampled drone pilots in Andhra Pradesh. Importantly, the income effect was accompanied by an improvement in income distribution, with the Gini coefficient declining from 0.44 to 0.26. Drone services also generated self-employment for all 60 pilots and additional employment for 42 helpers, resulting in an estimated 12,648 person-days of employment annually. The pilots collectively served 26,930 farmers and covered substantial agricultural area, demonstrating the wider service-delivery role of drone enterprises. Overall, the findings suggest that agricultural drone services can function as a viable rural enterprise while strengthening individual incomes, creating employment and expanding access to mechanized farm services. Policies that support appropriate training, affordable finance, technical assistance and reliable service ecosystems could help sustain these livelihood and employment benefits.

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