**6. Research ethical review**

Ethical considerations are perhaps more profoundly manifest in research than in most other aspect of medicine. Medical research especially those involved with human embryo and stem cell often bring to bear the demands and challenges posed by different bioethical orientations. Deontological bioethicists for their firm adherence to an orientation of natural laws and reasons are unlikely for instance to condone research targeted at exposing life threatening foetal abnormalities that would ordinarily require termination, in a pregnant subject. In the same vein, consequentialist and feminist bioethicists will employ macroethical reasoning to justify the conduct of research on a few human embryo and stem cells for the purpose of obtaining information that will lead to the development of therapy that will benefit many more sick patient (Stephens and Brynner, 2001). Ethical conflicts arise

Bioethics in Obstetrics 303

women, and also incorporate same into the curriculum of medical education; and advocacy into two key areas of sexual and reproductive right failings of women in each country. The project was conducted in each of the countries by a multidisciplinary steering committee under the auspices of the national FIGO – member society. Ethical guideline developed by each of the participating countries were eventually employed in the development of FIGO professional and ethical responsibility guideline concerning sexual and reproductive rights, at the XVII FIGO World Congress in Santiago Chile in 2006 (Adinma, 2003; FIGO, 2004).

a. **Professional Competence** – which enjoins the health professional to uphold the highest standard of professional practice in the care of his patient, avoid inappropriate relationship with patients or his family members, carry out prompt referral of patients when the expertise to care is lacking or in situation of conscientious objection to care of the patient. The health professional is also obligated to avoid ethical and human right violation in the care of their patient, develop appropriate interpersonal relationship with their patients and others while upholding the highest standard of integrity with colleagues and patients. They should also continuously update their professional

b. **Women's Autonomy and Confidentiality** - which obligates health professionals to inform and educate their patient to facilitate informed consent, ensure their right to privacy and confidentiality and avoid all forms of discrimination. The health professionals will also have an obligation to guide adolescents towards making ethics

c. **Responsibility to the Community** - which obligates health professionals to advocate for the right of their community members to information, education and means to make appropriate sexual and reproductive rights decision, advocate for the provision of resources and care that will enable women benefit from scientific progress and also promote reproductive health education of community members that will enable them participate in dialogue on health policy decisions concerning them. They should also discourage the patronage of quacks by community members, encourage traditional healers to refer patient, and show compassion to patients with medical emergencies especially with respect to payment of deposit and during industrial actions (FIGO, 2006;

An unbalance relationship exists between the medical practitioner and his female patient borne out of differences between them in social, cultural and economic circumstances together with inequality in knowledge of medicine. The woman seeking health care is therefore posited on a pedestal of vulnerability. The medical practitioner should address this vulnerability by giving clear information on every available treatment option that will enable the patient decide on her appropriate treatment choice. The woman's autonomy is thereby respected. This situation is no less appropriate to the obstetrics patient than is considered to other patient and this respect for her right to informed decision and consent should be sustained throughout the duration of her pregnancy. Every obstetrics patient has the right to the highest standard of obstetrics care and benefit of scientific progress and should under no circumstances be allowed to go through unnecessary or avoidable pain

These professional ethical guidelines are contained in three basic groupings.

knowledge through continuing medical education.

based reproductive health decisions.

Adinma, 2003; FIGO, 2004).

**8. Obstetrics care**

as to the propriety of employing human subject to carry out scientific studies to reveal vital information that will contribute to the successful management of a vast number of patients as was the case with the Tuskegee Syphilis Research which not only unveiled the long term manifestations of syphilis but also heralded the genesis of the developmental framework for the principles of modern bioethics from the Belmont Report. However it has been established that the well-being of human subject should be given priority consideration to the interest of science and the society (WMA, 2000).

It became necessary at a point to develop an acceptable course in bio-medical research involving not only humans but also animals and the environment. Two landmark guidelines emerged over the years concerning the conduct of research on human subjects – the Nuremberg Code of Ethics developed in 1947 following the trial of 23 German Physicians and Administrators for inhuman experimentations on human subjects during the 2nd world war and the Helsinki Declaration of the 18th General Assembly of the World Medical Association which developed recommendations and later ethical principles for medical research involving human subjects (WMA, 2008).

Helsinki consists of 35 principles, the first 30 of which relate to medical research, while the last 5 concern clinical practice. Between 1964 and 2008 Helsinki has been revised 9 times, the 9th of which was at the 59th General Assembly of the World Medical Association in Seoul South Korea in 2008. Helsinki Declaration recognizes the safety, autonomy, confidentiality, and the dignity of human subject in research. It also recognizes that potential benefits that should accrue to research subjects, and that research should be discontinued if the risk outweighs its benefits. Helsinki Declaration further requires that the protocol of the subject under study be submitted in advance to a Research Ethics Committee that is independent of the investigator or sponsor of the study, for scrutiny, consideration, comments, and ultimate approval or rejection (WMA, 2008). Ethical committee members are multidisciplinary and include not only medical professionals but also a lawyer, a clergy, an ethicists and other notable member of the community all of whom should be familiar with scientific basis of proposals together with the laws and regulations of the country in which the research experiment is performed.


Adopting precision agriculture technologies may be a natural course for Indian agriculture, which will help small farmers by boosting their output and revenue. A data-driven approach to farm management known as "precision agriculture" can boost output and productivity while also raising overall farm profitability [6, 7]. Small farmers in India can take advantage of developments in precision agriculture such as consolidated plots, plantation crops, cash crops, cooperative farming, online sensors, image processing, remote sensing, and integrated PA methods, to name a few [8]. However, precision farming has not yet become widely used in India [9]. India's government and other organisations offer support to small farmers who want to practise precision farming. The government should provide financial aid, training, and instruction to small farmers to encourage them to use precision agriculture methods. Companies can also provide small farmers with access to precision agriculture tools and training on how to use them effectively.

A mixed-methods strategy will be used for this study, combining qualitative and quantitative research techniques. A review of the literature will be conducted as the study's first step to determine what research has already been done on sustainable AI solutions for agriculture, particularly in India. An examination of India's agricultural productivity and carbon impact will also be included in the literature study.

#### **2.1 Artificial intelligence and carbon footprints**

Agriculture's carbon impact could be decreased with the use of artificial intelligence (AI). The following are some important findings from the search results:

Artificial intelligence in the agricultural sector has the potential to reduce carbon emissions from agrarian activities and revitalise the entire sector [10]. AI-based carbon footprint prediction systems analyse data from a range of sources, including weather forecasts, soil conditions, and crop yields, using machine learning (ML) algorithms. The device can calculate farms' carbon footprints accurately by analysing these data [11]. AI can help farming businesses with their knowledge demands, enhancing their capacity to detect diseases, track irrigation, minimise human labour, and raise crop yields [12]. A bibliometric examination of the works carried out in this sector between 2000 and 2021 gives evidence for the function of AI in sustainable agriculture. The study demonstrates the advancements made in the application of AI to sustainable agricultural practices and suggests a framework for streamlining future research on the use of AI technology in sustainable agriculture [13].

In general, the application of AI in agriculture has the potential to lessen carbon footprints by giving farmers precise estimates of their carbon emissions and enhancing the effectiveness of farming techniques. The results indicate that AI can help farming enterprises with their knowledge requirements and improve their capacity to detect diseases, track irrigation, save on labour costs, and raise crop yields.

#### **2.2 AI for agriculture sustainability in India**

The sustainability of Indian agriculture is currently being significantly improved by AI. The following are some important findings from the search results:

India's agriculture is vital to the rest of the world as well as the 500 million people who depend on it for their daily survival. India is integrating AI technologies to develop a more sustainable agricultural system [14]. By combining machine learning (ML) and AI, AI is enhancing the sustainability of agriculture. It aids farmers in making better decisions by providing them with intelligent information on weather, soil, and crop data [15]. Due to recent advancements in technology, the idea of sustainable agriculture has attracted more attention. Evidence from the works carried out between 2000 and 2021 on this topic has been provided by a bibliometric analysis of AI in sustainable agriculture. A framework for future research on the application of AI technology to sustainable agriculture is proposed in the study [13]. Two Google teams, AnthroKrishi, and Google Partner Innovation, are using AI to address the issues India's agricultural industry is facing. To help India's 1.4 billion inhabitants, they are utilising AI to recognise field boundaries and water bodies to enable sustainable agricultural methods, increase food yields, and support the country's 1.4 billion farmers [16]. The AI for Agriculture Innovation initiative is pushing the use of artificial intelligence, which is changing the Indian agriculture industry. To sustainably boost output, Indian farmers are employing smartphones and data collection on the ground. AI is being used to develop a resilient food system and launch a new era of farming [17].

In a nutshell, AI is assisting Indian farmers in improved decision-making and increasing crop yields, which is crucial for feeding India's 1.4 billion inhabitants. The use of AI in sustainable agriculture is reshaping India's agricultural industry and building a robust food chain.

*Sustainable Artificial Intelligence Solutions for Agricultural Efficiency and Carbon Footprint… DOI: http://dx.doi.org/10.5772/intechopen.112996*

#### **2.3 Challenges facing AI adoption in agri-food supply chain**

The adoption of AI in the agri-food supply chain is hampered by:

Insufficient knowledge and comprehension: The potential advantages of AI technology are not widely known among farmers and other participants in the agri-food supply chain. They might not comprehend how AI might be utilised to boost productivity, lower costs, and increase efficiency in the agri-food supply chain.

Expensive to implement: Small and medium-sized businesses (SMEs) in the agri-food supply chain may find it difficult to use AI technology because of the high implementation costs. These businesses may need to make a large investment in hardware, software, and training.

Data availability and quality: The effectiveness of AI technology depends on the availability and quantity of high-quality data. In the agri-food supply chain, however, data availability and quality might be problematic. Data may be lacking, erroneous, or not available in a manner that AI algorithms can readily use.

Resistance to change: Any industry faces resistance to change on a regular basis, and the agri-food supply chain is no different. A lack of trust in the technology or worries about job security may prevent some stakeholders from implementing new technologies, including AI.

Ethical and legal concerns: Data privacy, ownership, and liability are just a few of the ethical and legal issues that the use of AI in the agri-food supply chain brings up. To secure the ethical and legal applications of AI technology in the agri-food supply chain, these issues must be addressed.

In short, overcoming these obstacles is crucial for the effective implementation of AI technology in the agri-food supply chain. The literature reveals the potential advantages of AI technology in the agri-food supply chain and emphasises the need for additional study in this field.

#### **2.4 Interplay between AI and agri-food industry**

An area of significant interest is how AI and the agri-food sector interact. The following are some important findings from the search results:

The emergence of cutting-edge technology like artificial intelligence has modernised economic sectors, and the agri-food sector is no exception. AI can meet the knowledge requirements of agricultural enterprises, enhancing their capacity to detect illnesses, track irrigation, minimise human labour, and more [18, 19]. Artificial intelligence (AI) has the potential to increase sustainability, decrease waste, and increase productivity in the agri-food sector. The review points out how recent advancements in AI technology have revolutionised the agri-food industry [20]. Similar demands are being put on agri-food supply chains to integrate technology, including the Internet of Things (IoT), robotics, and AI. AI may be used to streamline logistics, cut waste, and increase the sustainability of the agri-food supply chain [21]. The interaction between AI and the agri-food sector has been studied using bibliometric analysis to determine the current state of the art and emerging trends. The review gives insights into research trends and advancements in the use of AI in the agri-food sector. Agriculture has made extensive use of machine learning, a kind of artificial intelligence. It is capable of analysing data from numerous sources, including sensors, drones, and satellites, to offer insights into crop health, soil moisture, and other topics.

In general, interest in the interaction between AI and the agri-food sector is rising. Artificial intelligence (AI) has the potential to increase sustainability, decrease waste,

and increase productivity in the agri-food sector. The literature underlines the need for additional study in this field and offers insights into the prospective uses of AI in the agri-food sector.

Based on the literature review, the study will then proceed with qualitative interviews and focus group discussions with farmers, agricultural experts, and technology providers. The studies have used various tools and techniques to analyse the effectiveness of AI but the approach to forecasting the same in the long run seems missing. So, for that, this study uses LCA and forecasting analysis using Exponential Smoothing Model.

Agriculture is an important sector that contributes to the economy of many countries. In recent years, the use of forecasting analysis and life cycle assessment (LCA) in agriculture has gained attention as a means to improve sustainability and productivity.

Statistical models and data are used in forecasting analysis to create predictions about upcoming occurrences, such as weather patterns, agricultural yields, and market prices. This strategy can assist farmers and other stakeholders in making wise choices regarding the planting, harvesting, and marketing of their crops.

Life cycle assessment (LCA) is a technique used to evaluate a process's influence on the environment from the extraction of raw materials to final disposal. LCA can be used in agriculture to assess the environmental effects of various agricultural methods, including conventional versus organic farming and the use of various fertilisers or pesticides. Farmers can choose the best practices to reduce their environmental impact while maintaining productivity by evaluating the environmental effects of various methods. The environmental effects of paddy farming in India are assessed using LCA. A study predicts the energy output and environmental effects of paddy cultivation by combining artificial intelligence techniques and LCA. The findings demonstrate that the environmental effects of paddy cultivation are significantly influenced by in-farm emissions. AI in agriculture can reenergise the entire agricultural system and cut carbon emissions from agrarian activities. A report offers a road plan for Indian agriculture to use AI technologies to lower carbon footprints. AI can promote sustainable farming methods, increase food yields, and serve India's 1.4 billion inhabitants by detecting field boundaries and bodies of water. Energyenvironmental indicators in various wheat production systems can be predicted using LCA and modelling methodologies. In general, the application of LCA and AI technologies can be utilised to assess the environmental effects of agricultural production and lower carbon footprints in the industry. Additionally, the use of AI in agriculture can facilitate sustainable agricultural methods, increase crop yields, and support India's 1.4 billion people.

In India's agricultural industry, Exponential Smoothing Models have been used to predict prices and build an ideal crop portfolio. Following are some salient details from the search results. The Indian agricultural sector has been improving, and it has been employing straightforward exponential smoothing to develop an ideal crop portfolio and boost returns for Indian farmers. In a one-period projection, exponential smoothing is helpful, and further projections for additional periods can be made using pattern prediction. It has been suggested to use the exponential moving average model to create an intelligent, sensible agricultural system. By combining machine learning (ML) and AI, AI is enhancing the sustainability of agriculture in India. It aids farmers in making better decisions by providing them with intelligent information on weather, soil, and crop data.

Both forecasting analysis and LCA can aid in making judgements about agricultural practices by farmers and other stakeholders. These instruments help farmers make the

*Sustainable Artificial Intelligence Solutions for Agricultural Efficiency and Carbon Footprint… DOI: http://dx.doi.org/10.5772/intechopen.112996*

most efficient use of their resources, cut down on waste, and have a smaller negative impact on the environment, resulting in more sustainable and effective farming.

#### **3. Results and discussion**

#### **3.1 Life cycle assessment (LCA) analysis**

The LCA analysis was conducted to assess the environmental impact of sustainable AI solutions for agricultural efficiency and carbon footprint reduction in India. According to the analysis, using sustainable AI solutions to replace conventional farming methods can cut greenhouse gas emissions by up to 25%. Examples of such technologies include precision farming and smart irrigation systems. Furthermore, sustainable AI solutions can reduce water consumption by up to 40% and improve crop yields by up to 30% (**Figure 1**).

The outcomes of the LCA analysis and exponential smoothing model shed important light on the possibility of sustainable AI solutions for improving agricultural productivity and lowering India's carbon footprint. The results of the LCA analysis are in line with other research that has emphasised the advantages of sustainable agricultural techniques, such as precise farming and intelligent irrigation systems, in lowering greenhouse gas emissions and enhancing crop yields.

#### **3.2 Exponential smoothing model**

See **Table 1.** As the exponential smoothing model uses a base year for results, for the study it has been taken as 2012. Although, based on the literature no specific information is available on the year of adoption of AI in agriculture in India. However, the articles suggest that AI is transforming the agricultural sector in India by promoting the use of artificial intelligence and other technologies [22–25]. The Indian agri-tech market is presently valued at US dollars (USD) 204 million and is expected to undergo exponential transformation owing to the adoption of technologies like artificial intelligence and supportive government policies. In India, more than 7000 farmers utilise AI technology to examine their soil, monitor crop quality, and monitor the health of their crops. Other uses for artificial intelligence (AI) in agriculture include robotics, predictive analytics, and precision farming. Because of this, it is obvious that AI is becoming more and more significant in India's efforts to change its agricultural sector, even though it is impossible to determine the precise year that AI was first implemented in Indian agriculture (**Figure 2**).

The above development could be proven by looking at **Tables 2** and **3** that depict the before and after of the predictions made by the model to show the use of AI in agriculture. **Table 2** shows the most important agricultural production activities (crop and livestock) with agricultural production value in India, which clearly show the blue region and have less AI involved as only 1% farmers today in India use precision farming but if the use of AI increases as shown in **Table 3**, the agricultural production value will increase by the year 2030.

Using the exponential smoothing model, it is possible to estimate how sustainable AI technology will affect Indian agriculture. The model predicts that adoption of sustainable AI solutions would increase by 20% yearly over the following 5 years, resulting in a 50% reduction in greenhouse gas emissions and a 60% increase in food yields between 2025 and 2030—the year when the SDGs are to be realised. The carbon

**Figure 1.**

*Life cycle assessment (LCA) framework for agricultural sector in India.*

*Sustainable Artificial Intelligence Solutions for Agricultural Efficiency and Carbon Footprint… DOI: http://dx.doi.org/10.5772/intechopen.112996*

#### **Table 1.**

*Forecasting by exponential smoothing model for the carbon footprint created by AI in Indian agriculture.*

#### **Figure 2.**

*Forecasting by exponential smoothing model for the carbon footprint created by AI in Indian agriculture.*

impact will be lessened as AI eventually finds use in Indian agriculture. Aside from perhaps having a significant impact on agricultural production and greenhouse gas emissions, this increase in adoption will likely increase the sustainability and efficiency of India's agriculture.

#### **Table 2.**

*The most important agricultural production activities (crop and livestock) with agricultural production value in India until 2018.*

#### **Table 3.**

*The most important agricultural production activities (crop and livestock) with predicted agricultural production value in India by 2030.*

#### **4. Conclusions**

In conclusion, this study emphasises the promise of sustainable AI solutions for improving agricultural productivity and lowering India's carbon footprint. Precision farming and intelligent irrigation systems can drastically cut greenhouse gas emissions while increasing crop yields, resulting in more efficient and sustainable agricultural practices in India. The findings from the life cycle assessment and exponential smoothing model offer insightful information about the potential effects of sustainable AI solutions in Indian agriculture.

To ensure that sustainable AI solutions are successfully implemented in Indian agriculture, a number of issues must be resolved. The lack of knowledge and technical proficiency among farmers and agricultural specialists is a major problem. By educating farmers on the advantages and application of sustainable AI solutions, this *Sustainable Artificial Intelligence Solutions for Agricultural Efficiency and Carbon Footprint… DOI: http://dx.doi.org/10.5772/intechopen.112996*

problem can be solved through targeted training programmes and informational campaigns. The price of implementing sustainable AI solutions, which might be prohibitively expensive for many small-scale farms, is another difficulty. Innovative funding mechanisms, such as public-private partnerships and microfinance programmes, which offer inexpensive access to long-term AI solutions can help solve this problem.

The findings of this study are in line with the United Nations Sustainable Development Goals (SDGs), particularly SDG 7: Affordable and Clean Energy, SDG 2: Zero Hunger, SDG 9: Industry, Innovation, and Infrastructure, SDG 13: Climate Action, SDG 14: Life Below Water, and SDG 15: Life on Land. The use of renewable energy in relation to carbon footprint is viewed as one of the solutions and hence the use of renewable energy sources could be done in line with the following SDGs.

#### **4.1 SDG 7: affordable and clean energy**

The use of renewable energy sources in agriculture, such as solar, wind, biomass, and biogas, supports the advancement of clean and sustainable energy solutions. In addition to lowering greenhouse gas emissions and air pollution, renewable energy technologies offer competitively priced, environmentally beneficial alternatives to traditional energy sources.

#### **4.2 SDG 2: zero hunger**

Food security and sustainable agriculture can be attained with the use of renewable energy. It can run irrigation systems, water pumps, and other agricultural equipment, guaranteeing dependable water supply for irrigation and raising crop output. Cold storage facilities powered by renewable energy can also aid in lowering post-harvest losses and enhancing food preservation.

#### **4.3 SDG 9: industry, innovation, and infrastructure**

Infrastructure development is facilitated by the use of renewable energy technology in agriculture, which also stimulates innovation. It promotes the use of decentralised energy systems, empowering rural communities, and fostering the growth of local renewable energy entrepreneurship.

#### **4.4 SDG 13: climate action**

Better resource management: AI may provide farmers with real-time data on soil moisture, nutrient levels, and pest populations to assist them in optimising the use of resources like water, fertiliser, and pesticides. SDG 13 may be furthered by more effective resource management and less environmental impact as a result of this.

Reduced greenhouse gas emissions: AI can be used to improve agricultural methods and cut greenhouse gas emissions, which are a major cause of climate change. AI can contribute to a decrease in the need of synthetic fertilisers and pesticides, which can lead to a decrease in nitrous oxide and other greenhouse gas emissions.

Agriculture that is "climate-smart": Artificial intelligence (AI) can be used to promote climate-smart agricultural practices, which aim to boost output and resilience while lowering greenhouse gas emissions and other harmful environmental effects. AI can help farmers make informed decisions about planting, harvesting, and crop management based on weather data, soil analysis, and other factors.

Using renewable energy in agriculture helps reduce the effects of climate change. It lowers greenhouse gas emissions and thus the carbon footprint of agricultural activities by substituting fossil fuel-based energy sources. This is in line with India's objective of lowering its carbon intensity and raising the proportion of renewable energy in the country's overall energy mix.

#### **4.5 SDG 14: life below water**

Aquaculture that is sustainable: By enhancing fish feeding and keeping an eye on water quality, AI can promote sustainable aquaculture practices. By enhancing fish health and productivity and reducing waste, these actions can support SDG 14.

Improved marine ecosystem monitoring is possible because of AI, which may be used to spot problem regions like coral bleaching or overfishing. In order to safeguard marine ecosystems and encourage sustainable fishing methods, this can aid academics and politicians in developing sound plans.

#### **4.6 SDG 15: life on land**

Animal conservation: AI may be used to track animal populations and spot concerns like poaching. This could support the preservation of endangered species and wildlife.

Forest health monitoring and the detection of regions at danger of deforestation or degradation are two ways AI can be utilised to promote sustainable forestry practices. This can aid in the development of effective initiatives by stakeholders and policymakers to support sustainable forestry practices and safeguard biodiversity.

Agriculture that uses renewable energy encourages sustainable land-use techniques. It can support agroforestry systems, which increase land productivity and conserve biodiversity by powering irrigation systems for tree plantations with renewable energy sources. Solutions based on renewable energy help protect forest ecosystems by reducing deforestation and the use of conventional biomass for heating and cooking.

As a result, AI has the potential to support sustainable agriculture and help achieve SDGs 2, 9, 7, 13, 14, and 15 by enhancing resource management, lowering greenhouse gas emissions, promoting climate-smart agriculture methods, assisting in sustainable aquaculture and forestry, monitoring marine ecosystems and wildlife populations, and promoting biodiversity preservation. However, it is crucial to make sure AI is applied in a responsible and moral manner, taking into account concerns like prejudice, data privacy, and transparency with a combination of already available renewable energy sources.

By enhancing productivity and yields and consequently raising the overall profitability of farming, precision agriculture has the potential to work effectively within the Indian agricultural system [26]. Small farmers in India can increase their production and income by implementing precision agricultural technology like consolidated plots, plantation crops, cash crops, cooperative farming, online sensors, image processing, remote sensing, and integrated PA approaches. Technology production is an important factor for technology transfer. It is a must for technology transfer. In addition, technology production capacity affects human development value and thus, leads to an environment of healthy agricultural practices among small Indian farmers [27]. The environmental effect of farming can be reduced using precision agriculture to reduce the demand for inputs like

#### *Sustainable Artificial Intelligence Solutions for Agricultural Efficiency and Carbon Footprint… DOI: http://dx.doi.org/10.5772/intechopen.112996*

water, synthetic fertilisers, and pesticides. Precision farming can enable small farmers in India, where it is still in its infancy, to enhance their output and revenue [28]. As a result, precision agriculture may complement the Indian agricultural system by giving small farmers access to cutting-edge tools and training on how to utilise them efficiently, which can help them enhance their farming methods and boost their income. Following the steps outlined below will enable small farmers in India to transition from conventional to precision agriculture, sustainable agriculture, and natural agricultural practices. This will increase their output and revenue while lowering their impact on the environment (**Figure 3**).

#### **Figure 3.**

*Pathway for adopting precision agriculture to sustainable agriculture to natural agriculture in India with the point of view of small farmers.*

Since reducing the carbon footprint of AI in agriculture is a difficult problem that calls for a multifaceted strategy, some policy recommendations would be as follows: Encourage the creation of AI technologies that use less energy. The adoption of AI by small Indian farmers should be done in a manner of full utilisation of innovation rather than just adopting the innovation for which a mix of policies will be required. The development of AI systems that are energy-efficient could be funded by governments. This might entail promoting the creation of lowpower technology and the use of renewable energy sources to power AI systems. Implement energy consumption standards and regulations: Governments may decide to implement energy consumption standards and rules for AI systems used in agriculture. This might make sure AI systems are built with energy efficiency in mind and run within predetermined energy consumption restrictions. Encourage the adoption of sustainable agricultural methods: This, together with the application of AI technologies, could help lower the overall carbon footprint of agriculture. Reduced use of synthetic fertilisers and pesticides, crop rotation, and the use of conservation tillage are a few examples of sustainable practices. Promote the usage of data centres powered by renewable energy sources because these facilities are essential to AI systems and use a lot of energy. Governments might promote the usage of data centres that run on renewable energy sources like wind and solar energy. Create carbon offset programmes for AI in agriculture: By aiding initiatives to lower greenhouse gas emissions, carbon offset programmes could assist in minimising the carbon footprint of AI in agricultural sector. Governments may collaborate with agricultural stakeholders to create carbon offset plans that are customised specifically to the agriculture industry. Encourage responsible data management: Governments may set rules and standards for responsible data management in agriculture. This can entail making sure that data are gathered and stored in a way that uses the least amount of energy, as well as that data are used in a way that maximises the environmental advantages of AI in agriculture. All things considered, lowering the carbon footprint of AI in agriculture will necessitate a confluence of technology advancement, environmentally friendly farming methods, and ethical policymaking. Governments, industry stakeholders, and other actors can make sure AI is used in a way that supports sustainable agriculture and lowers the sector's overall carbon footprint by cooperating.

#### **Acknowledgements**

I would like to take this opportunity to express my profound gratitude and deep regard to my Research Guide and co-author, Ms. Pooja Mehra, for her exemplary guidance, valuable feedback, and constant encouragement throughout the duration of the project. Her valuable suggestions were of immense help throughout my research work. Her perceptive criticism kept me working to make this research paper in a much better way. Working under her was an extremely knowledgeable experience for me.

#### **Appendices**

See **Table 4.** Supplementary data to this paper can be found online at *ghgplatformindia.org*.

*Sustainable Artificial Intelligence Solutions for Agricultural Efficiency and Carbon Footprint… DOI: http://dx.doi.org/10.5772/intechopen.112996*

#### **Table 4.**

*Consolidated data of carbon footprint in different sectors in India.*

#### **Author details**

Simran Ahuja and Pooja Mehra\* Amity University, Noida, India

\*Address all correspondence to<EMAIL_ADDRESS>
© 2023 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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[28] Gunnamreddy DS. The State of Precision Farming Agriculture in India. India: India.com; 2022

### *Edited by Orhan Özçatalbaş*

As countries develop economically, the relative importance of agriculture decreases. However, this does not mean that the importance of agriculture has decreased overall. Agriculture has been of vital importance for humanity in every period and will remain important from now on. Agricultural economics is a branch of science that examines the allocation, distribution, and use of resources and goods produced in agriculture. Events in recent years, including climate change, environmental pollution, global warming, and the COVID-19 pandemic, have clearly demonstrated the importance of food safety and security. This book discusses the theory and practice of sustainable agriculture as well as the importance of agricultural economics and agri-food business in combating the effects of threats such as climate change and meeting the world's food demand.

> *Usha Iyer-Raniga, Sustainable Development Series Editor*

Published in London, UK © 2024 IntechOpen © Andre2013 / iStock

Agricultural Economics and Agri-Food Business

IntechOpen Series

Sustainable Development, Volume 16

Agricultural Economics and

Agri-Food Business

*Edited by Orhan Özçatalbaş*


**2.1 Sonoporation**

When ultrasound is irradiated locally with certain energy, the cavitation nuclei, such as ultrasound contrast agents and bubbles, could alternately occur expansion, contraction, splitting, fusion, and even rupture. This physical process is called cavitation effect. Accompanied by the cavitation effect, acoustic microstreaming, micro-jet, high temperature, and shockwave will occur in the medium, resulting in the formation of some temporary, reversible pores on the cell membrane, which is sonoporation [6, 7]. Generally, it is an accepted notion that the sonoporation from cavitation effect allows genes and drugs to enter cells [8].

There are a large number of studies, which have confirmed that sonoporation can increase the efficiency of gene delivery through enhancing the permeability of the cell membrane [9–12]. The number of pores, having a high impact on the gene delivery efficiency, can be affected by a lot of factors, such as acoustic pressure, irradiation duration time, and pulse repetition frequency [13–15]. Sonoporation pores trend to be larger along with the increase of acoustic pressure and irradiation time, which also enhance gene transfection efficiency [16]. However, excessive acoustic pressure or ultrasonic duration may reduce cell viability and even cause cell death, vascular rupture, and other side effects [17–19]. Therefore, to achieve a

**101**

*Recent Advances about Local Gene Delivery by Ultrasound*

high gene transfection efficiency and remain a cell viability as much as possible, it is important to optimize ultrasound irradiation parameters during gene transfection.

In addition to sonoporation, cavitation effect can change the cell membrane structure through microstreaming and shear force. The mechanical force may cause cytoskeleton rearrangement and regulate various downstream cellular signaling pathways, helping the endocytosis of genetic cargo [20, 21]. Generally, there are three forms of endocytosis, including macropinocytosis, clathrin-mediated endocytosis, and caveolae-mediated endocytosis [22]. After ultrasound irradiation, the reactive oxygen species are produced to stimulate the calcium influx and induce the occurrence of endocytosis [23]. In addition, cavitation effect and shear force induced by ultrasound can change cell structure and influence endocytosis through mechanosensors and signaling cascade [24]. Meijering et al. demonstrated that endocytosis was involved in the uptake of the macromolecular substances, while small molecules enter cells mainly through the pores of the membrane surface [25].

Recently, Cock et al. put forward a new viewpoint on the mechanism of ultrasound-mediated gene delivery [26]. By using the real-time scanning confocal microscopy, they found that nanoparticle-loaded microbubbles could deposit the nanoparticles in patches onto the cell membrane during ultrasound irradiation and promote the particles that enter cell through the fluidity of the membrane. In their opinion, this method, termed sonoprinting, is neither the traditional sonoporation nor the material swallowing. The underlying mechanisms still need to be explored.

Genes administrated by the intravenous route are easily be degraded. Conventionally, genes such as plasmids, mRNA, siRNA, and miRNA need to be protected from degradation by extracellular and intracellular barriers **Figure 2**. The ideal gene vectors should have the following characteristics: (1) safe and nontoxic, long cycle time in vivo, protecting the nucleic acid molecules from being destroyed by extracellular nucleic acid enzymes; (2) possessing the characteristics of a targeting ability and delivering the gene to target tissue or target cells; (3) high gene-carrying capacity; (4) promoting the gene to enter cytoplasmic or nucleus and stable expressing; (5) ensuring the controllability of gene function; and (6) noninvasive evaluation of gene delivery effectiveness. In the field of ultrasoundmediated gene delivery, many ultrasound contrast agents, including microbubbles, nanobubbles, nanodroplets, and some nanoparticles, are being developed into gene

The gene vector may help them to avoid degradation by extracellular and intracellular barriers, including serum endonucleases, immune detection, and endosome

Microbubbles are small, gas-filled microspheres with the particle size of 1–3 μm.

As gene vectors, they not only can protect the genes from nucleic acid enzyme degradation and from reticuloendothelial system clearance but may also enhance

**3. Type of ultrasound contrast agents as gene vector**

vectors in gene delivery mediated by ultrasound.

(Quoted from: Yin et al. [2]).

**3.1 Microbubbles**

*DOI: http://dx.doi.org/10.5772/intechopen.80036*

**2.2 Endocytosis**

**2.3 Sonoprinting**

high gene transfection efficiency and remain a cell viability as much as possible, it is important to optimize ultrasound irradiation parameters during gene transfection.


*Supporting open minds since 2005*

Bio-Inspired Technology http://dx.doi.org/10.5772/intechopen.73400 Edited by Ruby Srivastava

### Contributors

Simone Sprio, Lorenzo Preti, Barbara Lambiase, Elisabetta Campodoni, Monica Sandri, Andrea Ruffini, Nicola Pugno, Anna Tampieri, Soumya K. Srivastava, Ezekiel O. Adekanmbi, Yang Yi, Kangjun Bai, Ruby Srivastava

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