Submit

Full Research Article

Bhartiya Krishi Anusandhan Patrika, volume 39 issue 3-4 (september-december 2024) : 288-293

Utilisation of Artificial Intelligence-related Technology for Agricultural Extension  Services among Extension Professionals in India

Maulika Patel1, P.B. Khodifad1, Mukesh Chaudhary2,*
1N.M College of Agriculture, Navsari Agriculture University, Navsari- 396 450, Gujarat, India.
2Department of Agricultural Economics and Extension, College of Agriculture, Lovely Professional University, Phagwara-144 411, Punjab, India.

  • Submitted24-11-2023|

  • Accepted19-10-2024|

  • First Online 23-12-2024|

  • doi 10.18805/BKAP695

Cite article:- Patel Maulika, Khodifad P.B., Chaudhary Mukesh (2024). Utilisation of Artificial Intelligence-related Technology for Agricultural Extension Services among Extension Professionals in India . Bhartiya Krishi Anusandhan Patrika. 39(3): 288-293. doi: 10.18805/BKAP695.

ABSTRACT

Background: The research examined the current understanding and utilization of AI-based digital technology in agricultural extension services. It assessed the level of adoption and identified the advantages and disadvantages of incorporating such technology.

Methods: To collect data, a structured online questionnaire was employed and responses were gathered from 131 extension professionals based in India. The data were analyzed using percentage and mean.

Result: The findings revealed that approximately 75.25% of the respondents had used AI-based digital technology at least onceand 61.48% were aware of its potential applications in agriculture extension services. Among those who used AI-based technology, about 45% were able to propagate innovations, while 33.60% showcased innovations and technologies. One of the major benefits, as reported by 80.25% of the participants, was the technology’s capacity to reach the target audience universally and at any time. However, 72.50% of respondents identified the high costs associated with its digital enablers as the primary drawback. The study highlighted a significant level of awareness among agricultural extension specialists regarding AI-based digital technologies. However, the actual utilization of these technologies in their services remained relatively low. To address this gap, the research recommends organizing on-the-job capacity building initiatives for current professionals. These programs aim to promote the adoption of AI-based digital technologies in agricultural extension services throughout India.

INTRODUCTION

The livelihoods of millions are supported by agriculture, which also makes a sizable contribution to the GDP of India. In this agrarian environment, it is crucial to effectively disseminate agricultural knowledge, best practisesand cutting-edge technologies to increase production, sustainabilityand farmers’ general well-being. A crucial link between research institutes and the farming community, agricultural extension services enable the transfer of knowledge and expertise from professionals to farmers.
 
The integration of cutting-edge technologies to handle numerous issues has sparked a technological revolution in the agriculture sector over time. Artificial intelligence (AI) has become a game-changer in several industries, including agricultureand is one such revolutionary force. Agricultural extension services could undergo a transformation thanks to AI-based technology, which would enable a more effective and data-driven strategy to engage farmers, answer their issuesand promote agricultural growth (Aharwal et al., 2023).
 
This study’s main goal is to investigate how Indian extension specialists are using AI-based technologies for agricultural extension services. These experts, such as agricultural officers, consultantsand field agents, serve as essential middlemen, connecting farmers with important knowledge and techniques that can have a big impact on their agricultural practices (Na et al., 2024).
 
The application of AI to agriculture has expanded the possibilities for extension services. Agricultural systems can be made smart and linked by combining AI with other cutting-edge technologies like remote sensing, the Internet of Things (IoT)and big data analytics. Large-scale data processing, intricate pattern analysisand important insight extraction are all capabilities of AI algorithms. These capabilities can help with resource optimisation, risk managementand decision-making.
 
This study will examine the present level of AI acceptance among extension professionals with the goal of evaluating their knowledge with, awareness ofand readiness for AI-based technologies. Designing efficient strategies for broad implementation requires an understanding of the elements that affect the acceptance of AI in extension services.
 
AI in agriculture has a variety of advantages. Using AI-driven solutions, extension specialists can customise their advisory services for farmers by making recommendations that are specific to each farm’s features and the surrounding environment. Additionally, AI-powered predictive models can offer early warnings regarding pest outbreaks, the frequency of diseasesand weather patterns, assisting farmers in the proactive planning of their agricultural activities.
 
Despite the enormous promise of AI in agriculture, there are obstacles that extension specialists may face while using it. These can include issues with data ownership and privacy, the necessity for specialised training and restricted access  to  AI infrastructure (Mohan et al., 2023). To ensure the seamless and effective integration of AI in agricultural extension services, these issues must be resolved.
 
Due to the continuous global population growth, the utilization of AI-based technology in agriculture has become increasingly significant. As projected by the World Population Data Sheet (2020), the world’s population is expected to reach 9.9 billion by 2050, up from 7.8 billion in 2020, putting additional strain on the agricultural system (Liakos et al., 2018). Meeting the rising food demand by 2050 would require a 60-70% increase in agricultural production from its current level (Silva, 2018). This necessity for enhanced food production calls for a more efficient and advanced approach to farming, making digital agricultural transformation crucial. Sophisticated Artificial Intelligence (AI), digital technologiesand innovations, according to Trendov et al., (2019), will bring about significant changes in the agrifood system in the next decade.
 
AI-based tools and technologies have the potential to digitize farming systems, leading to improved production, consumptionand data collection and dissemination. To establish a sustainable food security strategy, the integration of information and communication technologies (ICTs) in agriculture is essential (Miiro et al., 2020; Ridley et al., 2019). Rakhra et al., (2022) emphasize that artificial intelligence-based technologies have proven beneficial in addressing various agricultural aspects, such as crop yield, irrigation, soil content sensing, crop monitoring, weedingand crop establishment, resulting in increased productivity across all industries, including agriculture. Singh and Jain (2022) further add that artificial intelligence technologies enhance multiple agriculture-related tasks throughout the food supply chain, including harvesting, processingand marketing. They also contribute to healthier crops, effective pest managementand soil monitoring (Kim and AlZubi, 2024).
 
Before the COVID-19 crisis, most agricultural extension services primarily relied on traditional “on-the-field” techniques, such as farm tours, group trainingand demonstration plots, as highlighted by Olagunju et al., (2021). However, the pandemic and physical distance-related restrictions prompted a shift toward utilizing modern tools to provide services to farmers by agricultural employees using smartphones (Maertens et al., 2020). These digital instruments, such as smartphones, enable broader access to agricultural extension activities, providing farmers with market information, pricing detailsand hotlines for technical agricultural advice (Danso-Abbeam  et al., 2018).
 
Recognizing the potential of AI-based digital solutions to enhance extension service delivery and the lack of sufficient data on the adoption of these technologies by those involved in agricultural extension, the study focused on assessing respondents’ knowledge regarding the current utilization of AI-based digital technology in agricultural extension services within the research area. The study aimed to address the extent of AI-based technology use in agricultural extension services and its implications.
 
1. Calculate the level of knowledge and adoption of AI-based digital technology in services for improving agriculture.
2. Enumerate the extent to which AI-based digital technology is used in agriculture among responders, extension services.
3. Find out the benefits and drawbacks of employing digital technology based on AI in among the respondents in the study area.

MATERIALS AND METHODS

The research was conducted at Navsari Agricultural University, Navsari, Gujarat, India, A country with a land area of approximately 3.287 million square kilometers and a population of about 1.4 billion (UNFPA, 2021). The study included extension agents from both public and private sectors, lecturers, researchersand contact farmers (farmer-led extension workers) as respondents. To ensure standardization, a structured survey on a Google form was created and pretested among nine experts in rural sociology and agricultural extension. This research was conducted over a 3 year period from 2021 to 2023. The online questionnaire was distributed to 186 extension professionals through email addresses obtained from relevant professional associations, such as the Indian Society of Agricultural Extension, Indian Society of Extension Educationand the National Institute of Agricultural Extension Management. Additionally, 82 unregistered extension professionals in India were approached individually. Eventually, 131 extension professionals completed and submitted the online questionnaire within a month. The data were processed using Version 21 of the IBM-SPSS statistics programand statistical measures such as percentage and mean were utilized to characterize and summarize the data.
 
The research defined the concept of using artificial intelligence (AI)-based technology for agricultural extension as the awareness and utilization of AI (involving human and expert-based intelligence) in communication, sourcing and disseminating information, farm and weather-related data and innovationsand capacity and soft skill enhancement. It also covered extension services in farm management, such as on-farm innovation and technology adoption, diseases management, on-farm monitoringand inputs.
 
To determine the level of awareness, respondents were asked whether they had heard of AI-based technology and provided a “yes” or “no” response. The first stage of “utilization” was ascertained through this awareness assessment. For evaluating the utilization, respondents were asked if they had ever used AI-based digital technology for extension servicesand their responses were scored with one mark for a “yes” and zero for a “no” response. The extent of utilization for each stated conventional extension activity was also assessed, with a maximum score of ten (10) and a minimum score of zero (0).
 
The overall utilization score was calculated by summing up the separate scores for awareness, useand the areas of utilization. To categorize the level of utilization, the equal interval method proposed by Adisa et al., (2022) was employedand responses were divided into high and low levels of utilization, based on an average score of 5 marks. Scores below the average were considered low, while scores above were classified as having a high degree of utilization. Additionally, respondents were asked to provide their opinions on the advantages and disadvantages of employing digital AI technology for agricultural extension services. Positive responses were awarded a “1” mark, while negative responses received a “0” mark.

RESULTS AND DISCUSSION

Awareness of AI digital technology
 
According to Fig 1, a significant proportion of respondents (61.48%) demonstrated awareness regarding the application of digital technology based on artificial intelligence in agricultural extension services. This high level of awareness among extension experts indicates their familiarity with the technology, potentially influencing its utilization. In the process of adopting and accepting any technology, awareness plays a crucial role as it sparks interest in the innovation adoption process.

Fig 1: Awareness of AI digital technology in agricultural extension services in India.


 
Use of AI digital technology for agricultural extension services
 
Fig -2 illustrates that approximately 38.52% of respondents reported never having used digital AI technology for agricultural extension services, while roughly 61.48% of respondents indicated that they had employed such technology. This finding suggests that in India, more than half of the extension specialists were already utilizing AI-based digital technologies to support agricultural extension activities. This could imply that these specialists recognize the advantages of AI-based digital technologies compared to traditional face-to-face extension approaches. Especially during the Covid-19 pandemic and in the post-Covid-19 era, the adoption of digital platforms has emerged as an alternative solution (Olagunju et al., 2021), offering a novel approach to address the challenge of a low extension-farmers ratio. Beyond the limitations of traditional face-to-face methods, AI-enabled agricultural extension and decision support tools have the potential to significantly enhance interactions with farmers effectively.

​

Fig 2: Respondents using AI digital technology in agricultural extension services.


 
Areas of utilisation of AI digital technology in agricultural extension services
 
Table 1 displays the applications of AI in agricultural extension services. Slightly over 45% of the respondents reported using AI-based digital tools to disseminate technologies, while 34% utilized AI-based technology for demonstrating innovations and technologies. Additionally, respondents mentioned employing AI digital technology for livestock disease control (16%), crop disease management (19.8%) and consultation with subject matter experts (25.2%). These findings align with the research by Sarker et al., (2019) and Olagunju et al. (2021), which also indicated that AI-based digital technology can be effectively used for marketing, consultationand transmitting information related to crop and livestock disease control. However, it is notable that the respondents used AI-based digital technology primarily as communication tools rather than fully integrating them into extension service tools for farm management and monitoring. This suggests that there is room for greater utilization of AI-based technology in agricultural extension services.

​

Table 1: Level of utilisation of AI digital technology for agricultural extension service.


 
As shown in Fig 3, a significant majority of respondents (79.25%) reported using digital AI technology at a low level, while only a small percentage (20.75%) utilized it at a high level. This finding indicates that, despite the potential benefits of AI-based digital technology for agricultural extension services, the majority of extension practitioners are not fully harnessing its capabilities. One possible reason for this underutilization could be the lack of capacity building among extension specialists and their limited experience in using AI-based digital technology for agricultural extension services.

​

Fig 3: Level of utilisation for AI digital technology for agricultural extension services.


 
Benefits considered of using digital technology based on AI for extension services
 
Table 2 illustrates that a majority of extension professionals acknowledged several benefits of using AI-based digital technology in agricultural extension services. These benefits include the ability to reach the target audience universally and at any time (80.25%), providing extension services without physical contact (66.15%), promoting agricultural digitalization (45.62%)and simplifying, optimizingand making agriculture more precise (18.25%). These findings suggest that AI-based digital technology has the potential to address various challenges, such as a shortage of extension agents, a gap between research and development, limited funding, inappropriate adoption of innovations and technologies, outdated and inaccurate farm data, as well as issues related to monitoring and managementand bridging the gap between extension and farmers. These outcomes clearly demonstrate that extension specialists were aware of the vast possibilities that AI-based digital technologies offer for enhancing efficiency in agricultural extension services in India.

​

Table 2: Perceived merits of using AI-based digital technology for agricultural extension service.


 
Perceived drawbacks of using digital technology based on AI for extension services
 
Based on the data presented in Table 3, the primary drawbacks cited by the majority of respondents regarding the use of AI-based digital technology for extension services include the high costs associated with acquiring and maintaining smartphones and other infrastructure facilities (72.50%), low literacy rates among farmers (56.27%), the need for technical and financial expertise to operate AI technologies (42.50%)and insufficient digital skills and education among extension agents (40.80%). This suggests that extension professionals in India, who typically earn average incomes, may encounter challenges in adopting technology due to its high costs, particularly advanced Artificial Intelligence technology. The findings align with the observations made by Farinde et al., (2022), indicating that the adoption of AI-based digital tools for extension practices in India is hindered by factors such as high costs, low levels of literacyand a lack of technical expertise among extension personnel and farmers.

​

Table 3: Perceived drawbacks of using digital technology based on AI for extension services.

CONCLUSION AND RECOMMENDATION

The utilization of AI-based digital technologies for extension services among extension specialists in India is currently at a low level. To enhance the adoption and effective use of AI-based digital technology for extension services, especially in rural areas, several measures can be taken by the government and other stakeholders.

Firstly, the government and relevant stakeholders should provide digital enablers, such as affordable smartphones, digital infrastructureand reliable internet connections, to facilitate access to digital technology in remote regions. Lowering the price of internet data by internet service providers would make internet access more accessible to all consumers, including extension professionals and farmers.
 
Secondly, the Ministry of Agriculture and Rural Development, in collaboration with the Ministry of Communication and Digital Economy, should regularly organize and facilitate accessible programs aimed at building the capacity of agricultural extension professionals in digital technology usage. These capacity-building programs will empower extension specialists with the necessary skills and knowledge to leverage AI-based digital technologies effectively in their extension services.
 
Furthermore, to ensure long-term sustainability and promote scientific validity, subjects related to AI-based digital technology should be integrated into the university agricultural curriculum, specifically in agricultural extension programs. By incorporating these subjects into the curriculum, future extension professionals will be well-equipped with the expertise required to utilize digital technology optimally in their agricultural extension practices.
 
Through these initiatives, there is a greater likelihood of enhancing the use of AI-based digital technology for agricultural extension services and ultimately improving the overall extension services provided to farmers across India.

REFERENCES


  1. Adisa, B.O., Famakinwa. M., Adeloye, K.A. and Adigun, A.O. (2022). Crop farmers’ copingstrategies for mitigating conflicts with cattle herders: Evidence from Osun State, Nigeria. Agricultura Tropica et Subtropica, 55 OV, 191- 201.

  2. Aharwal, B., Roy, B., Meshram, S. and Yadav, A. (2023). Worth of artificial intelligence in the epoch of modern livestock farming: A Review. Agricultural Science Digest. 43(1): 01-09. doi: 10.18805/ag.D-5355.

  3. Danso-Abbeam, G., Ehiakpor, D. S., and  Aidoo, R. (2018). Agricultural extension and its effects on farm productivity and income: insight from northern Ghana. Agriculture & Food Security. 7(1): 1-10.

  4. Farinde, A. J., Ojo, T. F. and Famakinwa, M. (2022). Virtual and artificial intelligence tools for extension practices in nigeria in agricultural extension in nigeria. 3 rd edition, Agricultural Extension Society of Nigeria. pg.165-180.

  5. Kim, T.H. and AlZubi, A.A. (2024). AI- enhanced precision irrigation in legume farming: Optimizing Water Use Efficiency. Legume Research. DOI: 10.18805/LRF-791.

  6. Liakos, K. G., Busato, P., Moshou, D., Pearson, S. and D. Bochtis (2018). Machine learning in agriculture: A Review. Sensors. 18(8): 2674.

  7. Maertens, A., Michelson, H., & Nourani, V. (2020). How do farmers learn from extension services? evidence from Malawi. American Journal of Agricultural Economics, 103(2): 569- 595.

  8. Miiro R., Luzobe, B., Mangheni, M.and Asiimwe, A. (2020). Responding to the COVID-19 Lockdown, Agricultural Extension Agents’ Experiences in Uganda: A Survey by the Uganda Forum for Agricultural Advisory Services (UFAAS). Food and Agriculture Organization (FAO) Webinar series, June 2020.

  9. Mohan, S.S., Venkat, R., Rahaman, S., Vinayak, M. and Hari Babu, B. (2023). Role of AI in Agriculture: Applications, Limitations and Challenges: A Review. Agricultural Reviews. 44(2): 231-237. doi: 10.18805/ag.R-2215.

  10. Na, I.S., Lee, S., Alamri, A.M. and AlQahtani, S.A. (2024). Remote sensing and AI-based monitoring of legume crop health and growth. Legume Research. 47(7): 1179-1184. doi: 10.18805/LRF-795.

  11. Olagunju, O., Adetarami, O., Koledoye G., Olumoyegun, A and Nabara, I. (2021). Digitization of agricultural extension system for effective management of emergency in Nigeria. Journal of Agricultural Extension, 25 (4): 81-91.

  12. Rakhra, M., Sumaya, S., Quadri, N.N.,Verma, N., Ray, S., and Asenso, E (2022).Implementing machine learning for smart farming to forecast farmers’ interest in hiring equipment. Hindawi Journal of Food Quality, Volume 2022, Article ID 4721547, 17 pages Accessed on 21 February 2022 from https://doi.org/10.1155/2022/ 472-1547.

  13. Ridley, M., G. Rao, P. Vikram, F. Schilbach. (2019). Poverty and Mental Illness: Causal Evidence. https://economics. mit.edu/files/18694

  14. Sarker, M.N. I, Islam M.S, Ali. M.A, Islam, M.S, Salam. M.A, and Mahmud, S.M.H. (2019). Promoting digital agriculture through big data for sustainable farm management. International Journal of Innovation and Applied Studies. 25(4): 1235-1240.

  15. Silva, G. (2018). Feeding the World in 2050 and Beyond - Part 1: Productivity Challenges. Michigan State University Extension - December 3.

  16. Singh, S., and Jain, P. (2022). Applications of Artificial Intelligence for the Development of Sustainable Agriculture. In:  Agro-biodiversity and Agri-ecosystem Management. [Kumar, P., Tomar, R.S., Bhat, J.A., Dobriyal, M., Rani, M. (eds)] Springer, Singapore. https://doi.org/10.1007/ 978-981-19-0928-3-16.

  17. Trendov, N.M., Varas, S. and Zeng, M. (2019). Digital technologies in agriculture and rural areas - Status report. Rome. Accessed on December 28th, 2022 from http://www. fao.org/3/ca4985en/ca498. 

  18. UNFPA (2021). Delivering on the Transformative Results, Annual Report.
Reviewed By

Download Article
In this Article
    Contact us
    Follow us

    Editorial Board

    View all (0)