Integrating Drone Technology in Agricultural Practices: Evidence from Tamil Nadu’s Farmer Producer Organizations | Chapter 3 | Food Science and Agriculture: Research Highlights Vol. 1

The advent of drone technology has revolutionized various industries, with agriculture being one of the most significantly impacted sectors. The integration of drones into agricultural practices contributes to environmental sustainability by promoting more efficient use of resources. Indian agriculture makes a substantial contribution to the nation's GDP, employment, and food security. It directly employs over half of the country's workforce, supporting the livelihoods of rural communities. Historically, Indian agriculture has been labour-intensive and reliant on traditional practices, resulting in inefficient resource utilization. To address the numerous challenges facing the Indian agriculture sector, the adoption of emerging technologies, such as drones, is imperative. Drones have the capacity to significantly enhance agricultural practices, increasing productivity and reducing resource wastage. This study explores the economic dynamics of drone technology in agriculture, addressing a gap in past research amid the growing use of Artificial Intelligence in the sector. This study integrated the Partial budgeting approach as it allows for assessing the impact of a change in the production system on a farmer’s net income without knowing all costs of production. This study was conducted in two districts of Tamil Nadu using a multistage sampling technique to collect the data. Conducted in the paddy cultivation regions of Thanjavur and Madurai districts in Tamil Nadu, the study involves a sample of 80 for UAV technology and 120 for conventional methods. The findings reveal significant cost savings and higher profitability with drone-assisted farming, where total expenses decrease from ₹27,723.20 for conventional farming to ₹22,857.50 for drones, primarily due to reduced pesticide and herbicide use and improved application efficiency. While both methods yield similar gross returns of ₹39,100 for conventional and ₹40,640 for drone-assisted—the net returns are markedly higher for drones at ₹17,782.50 versus ₹11,376.80 for conventional practices. A per-acre comparison shows substantial reductions in labor costs, with human labor decreasing from ₹11,077 to ₹5,628 and pesticide costs falling from ₹2,032 to ₹950. Although machine labor costs rise slightly with UAVs, overall savings enhance the financial viability of drone-assisted farming. The partial budget analysis indicates a net profit increase of ₹7,331, underscoring the economic advantages of adopting drone technology in agriculture. Finally, it is concluded that the integration of drone technology into Indian agriculture, particularly in paddy cultivation in Tamil Nadu's Thanjavur and Madurai districts, signifies a transformative shift towards more efficient and profitable farming practices.

 

Author (s) Details

 

Malaisamy A
Department of Agricultural Economics, Agricultural College and Research Institute, Madurai, Tamil Nadu-625104, India and International Livestock Research Institute, India.

Yazhini A
Department of Agricultural Economics, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu-641003, India.

 

Raswanthkrishna M
Department of Computer Science and Engineering (AI), Amrita University, Coimbatore- 641 112, Tamil Nadu, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/fsarh/v1/4322

Laser Sensing for Monitoring and Elimination of Oil Pollution at Sea| Chapter 9 | Techniques and Innovation in Engineering Research Vol. 2

 

The spectral characteristics of laser-induced fluorescence (LIF) for oil products in various states are covered in this article (solutions in seawater and thin slicks). This study was carried out to assess the effectiveness of LIF in the quantification of bilge water-related ocean pollution and the identification of oil products. It was found that depending on the state of the oil product, the LIF spectral distribution's form changed (pure fuel, slick or solution). The most popular types of heavy and light marine fuels were used to calibrate the LIF technique at solution concentrations, and limit of detection (LoD) values were created for each type. The time dynamics of the solution spectra were studied and time change characteristics were found. To examine the LIF for marine oil pollution, a small-scale LIF sensor for unmanned aerial vehicles (UAVs) is described. The effectiveness of using dispersants to remove oil films from the sea surface and the effects of oil pollution and dispersants in removing oil films on the state of phytoplankton communities can both be studied using the LIF approach, according to experimental evidence.

Author(s) Details:

Oleg Bukin,
Maritime State University named after Admiral G.I. Nevelskoy, Maritime Technopark, 690003 Vladivostok, Russia.

Dmitry Proschenko
Maritime State University named after Admiral G.I. Nevelskoy, Maritime Technopark, 690003 Vladivostok, Russia and Laboratory of Analytical Laser Spectroscopy, Far Eastern Federal University, 690091 Vladivostok, Russia.

Chekhlenok Alexey,
Maritime State University named after Admiral G.I. Nevelskoy, Maritime Technopark, 690003 Vladivostok, Russia.

Denis Korovetskiy,
Maritime State University named after Admiral G.I. Nevelskoy, Maritime Technopark, 690003 Vladivostok, Russia.

Ilya Bukin,
Maritime State University named after Admiral G.I. Nevelskoy, Maritime Technopark, 690003 Vladivostok, Russia.

Viktoria Yurchik,
Maritime State University named after Admiral G.I. Nevelskoy, Maritime Technopark, 690003 Vladivostok, Russia.

Irina Sokolova,
Maritime State University named after Admiral G.I. Nevelskoy, Maritime Technopark,
690003 Vladivostok, Russia.

Andrey Nadezhkin,
Maritime State University named after Admiral G.I. Nevelskoy, Maritime Technopark, 690003 Vladivostok, Russia.

Please see the link here: https://stm.bookpi.org/TAIER-V2/article/view/8277