AI-Driven Regenerative Agriculture for Climate Resilience: A Review

Introduction

Climate change is now a major threat considered to be the greatest challenge for agriculture worldwide. Droughts and floods are becoming more common, as are heat waves and changing patterns of pests, all of which are impacting world agricultural output and food security. Direct non-CO2 GHG emissions from agriculture, including land use change and deforestation for agricultural expansion, play a vital role in the global environment and are related to conventional farming practice, which involves high rates of tillage, monoculture, and overapplication of synthetic fertilizers and pesticides (Kabato et al., 2025; Basheer et al., 2024; Yuan et al., 2024) [26, 10, 63] . Regenerative agriculture is a comprehensive farming system that attempts to enhance ecosystem services without compromising agricultural productivity. Key principles are reduction in soil disturbance, maintenance of soil cover, diversification of crops, incorporation of livestock, and increasing biological activity. These activities favor soil organic carbon (SOC), water infiltration, nutrient cycling and biodiversity (Rai et al., 2025; Khangura et al., 2023; Jayasinghe et al., 2023) [47,29,24] . Recent progresses in Artificial Intelligence (AI) provide unprecedented chances to enhance the regenerative agriculture. AI algorithms can analyze enormous datasets from satellites, drones, sensors, weather stations and farm records in order to assist decisionmaking in real time. The Intersection of AI and regenerative agriculture has the potential to produce climate smart production systems that maintain productivity as climate variability increases (Ali et al., 2025; Sajjad et al., 2026) [5,49] .

Concept of Regenerative Agriculture 2.1 Minimize Soil Disturbance

Conservation tillage and no-till farming are essential regenerative ag practices that reduce or avoid soil disturbance along with the number of times the soil in a field is tilled. These practices contribute to moisture conservation in the soil, help reduce erosion, increase water penetration into the soil and stimulate activity of the good types of organisms in the soil. Through residue retention and minimal soil disturbance, they conserve soil aggregation and soil pore space, [52,66,41] .

Permanent Soil Cover

Cover crops, crop residue retention, and mulching are regenerative agricultural practices that preserve soil cover, prevent erosion, retain moisture, and mitigate soil temperature (Lal, 2015) [68] . These practices contribute to the build-up of soil organic matter, stimulate microbial activity, suppress weeds, improve water-use efficiency, and enhance the resilience of agricultural systems toward climate change (Zhang et al., 2023; Gomide et al., 2026) [64,19] .

Crop Diversification

Crop rotations, intercropping and agroforestry systems are sustainable land management practices that improve soil fertility, enhance nutrient cycling, suppress pests and diseases, increase biodiversity, and promote efficient use of natural resources, contributing to higher agricultural productivity and environmental sustainability (Zheng et al., 2024; Al-Musawi et al., 2025; Toker et al., 2024) [67,6,56] .

Livestock Integration

Sustainable grazing and nutrient cycling, coupled with better interactions among ecosystem functions, improve soil fertility, nutrient availability, and eco-environmental stability, beneficially affecting biodiversity and resource use efficiency within sustainable agriculture. These managements enhance soil quality and water productivity, they contribute to ecosystem resilience and they reduce reliance on off-farm inputs and on degradation of the environment (Morales-Reyes et al., 2025; Mabhaudhi et al., 2023) [38,34] .

Soil Biological Enhancement

Organic amendments, application of compost and biofertilizers or microbial inoculants enhance the physical, chemical and biological characteristics of soils by increasing organic matter content, nutrient availability, microbial population, and overall soil structure. These practices enable sustainable nutri-ent management, sustainable crop productivity and sustainable soil health and environmental quality (dos Reis et al., 2024; Tomar et al., 2026) [16,58] .

Source: https://www.shutterstock.com/image-vector/regenerative-agriculture-principles-diagramdo-not-2660453793

Climate Resilience in Agriculture

Climate resilience is the ability of agricultural systems to survive, adapt to, and recover from climate related stresses and disturbances while still maintaining its basic functions of production and provision of ecosystem services. It includes adaptive capacity (the ability to change management practices in response to climate variability), resistance (the ability to maintain productivity during unfavourable conditions), recovery capacity (the ability to recover productivity after disturbances), and transformative capacity (the ability to undergo long-term transformations according to changing climatic conditions). Regenerative agriculture practices improve climate resilience by building soil organic matter, enhancing water holding capacity and soil water infiltration, supporting biodiversity, bolstering ecosystem functions, and reducing reliance on external inputs, resulting in farming systems that are more able to withstand climaterelated stressors (Sharma et al., 2025) [51] .

Artificial Intelligence in Agriculture

AI is currently being developed as a computational system that can learn by itself from the data, reason about things, make predictions, and potentially-even support an agent in making autonomous decisions. AI in agriculture is a combination of multiple high-end technology to bring revolution in productivity, efficiency and sustainability. ML algorithms examine massive data collections to support decisions relating to yield prediction, soil classification, and climate forecasting (Jabed and Murad, 2024) [23] . Deep Learning (DL), a branch of AI, employs intricate neural networks, among other things, to achieve remarkable accuracy in disease detection, weed detection, and image-based crop monitoring (Kumar et al., 2026; Pai et al., 2024) [31,43] . Applications of Computer Vision techniques to analyze and interpret digital images (2D or 3D) for canopy analysis, detecting nutrient deficiencies, and crop phenotyping are now well established. The convergence of AI and Internet of Things (IoT) enables sensing environmental, soil and plant parameters in the real-time, automated irrigation scheduling, and environmental monitoring using connected sensors and devices (Abdelmoneim et al., 2025) [1] . Moreover, Remote Sensing (RS) and GIS offer spatial data on vegetation mapping, soil moisture estimation, and ter-restrial carbon monitoring, which contribute to the implementation of sitespecific management of agricultural resources (Li et al., 2024) [33] . All of these AI-enabled technologies are transforming the way we grow food, building more resource-use efficient, riskreduced and climate-resilient farming systems to feed the future (Madhav, 2025) [35] .

Integration of AI with Regenerative Agriculture 5.1 AI-Based Soil Health Monitoring Soil Organic Carbon

Soil organic carbon (SOC) is commonly used as an indicator of soil fertility and soil quality since it represents the amount of organic matter in soil. Its level is monitored to evaluate carbon sequestration potential and the efficacy of regenerative management practices (Patil et al., 2025) [45] .

Soil Moisture

Soil moisture is the amount of water contained in the soil, which is a key factor affecting plant growth and nutrient absorption. Continuous monitoring of soil moisture contributes to the optimization of irrigation scheduling and the enhancement of water use efficiency (Tripathi et al., 2025) [59] .

Microbial Activity

Microbial activity is the measure of the population and the activity of beneficial soil microbes that participate in nutrient cycling and the decomposition of organic materials. Greater microbial activity is representative of healthy, biologically active soil for plant production (Badagliacca et al., 2024) [8] .

Nutrient Availability

Nutrient availability is the extent to which essential nutrients are accessible for plant uptake. Nutritional status evaluation is useful for accurate fertilization and to avoid nutrient deficiency or toxicity (Bhatia and Sindhu, 2024) [11] .

Continuous Monitoring

Monitoring continuously supplies data on soil states and ecosystem activities in real-time. It facilitates decision-making at the right time and early identification of soil health problems (Colombi et al., 2025) [16] .

Site-Specific Interventions

Site-specific management is the application of management practices to portions of a field, rather than the entire field, to address the specific needs of each area (Sharma, 1996) [69] . This leads to greater efficiency of utilization of inputs and better farm level productivity (Thakur and Subba, 2026) [55] .

Improved Soil Restoration Strategies

Precise information on soils enables scientists to develop efficient soil restoration measures adapted to the local conditions. These practices contribute to restoring soil fertility, enhancing ecosystem services, and ensuring agricultural sustainability in the long run (Patil et al., 2025) [45] .

Precision Nutrient Management Variable-Rate Fertilizer Application

Variable-rate fertilization is one of the precision agriculture techniques which consist of fertilizing at different rates within a field according to the conditions of soil fertility and crop needs. This leads to nutrient supply at the right place and right time (where in field in field or farm) resulting in increased fertilizer use efficiency and reduction of wastage (Pawase et al., 2023; Kabato et al., 2025) [46,26] .

Nutrient Demand Forecasting

Forecasting of nutrient demand uses information derived from soil testing, plant growth stages, environmental condition, and from model based on AI to estimate future nutrient needs. This provides more precise prediction of the timing, and rates of N application for improved crop production (Tomar et al., 2026) [58] .

Nutrient-Use Efficiency Improvement

Enhancement of nutrient-use efficiency is based on the principle to utilize maximum nutrient among the applied nutrient under crop root environment. Balanced nutrition, precision nutrient management, and use of biofertilizers are some of the approaches which reduce nutrient losses and enhance productivity (Singh et al., 2025) [54] .

Reduced Fertilizer Losses

Precision nutrient management decreases nutrient losses owing to leaching, runoff, volatilization and fixation. The fertilizer 35 efficiency is increased with less environmental pollution (Bhatia and Sindhu, 2024) [11] .

Lower Greenhouse Gas Emissions

Efficient fertilization reduces emissions of greenhouse gases, especially nitrous oxide (N2O), from the soil under agricultural use. This leads to climate change abatement and improved sustainable agricultural practices (Patil et al., 2025) [45] .

Enhanced Soil Biological Activity

The result of balanced nutrient management is a thriving community of beneficial soil microbes, actively engaged in the cycling of nutrients and decomposition of organic matter. High microbial activities can enhance soil physical properties, fertility and sustainable productivity of soils (Tomar et al., 2026) [58] .

Smart Water Management Weather Forecasts

Expectations about rainfall, temperature, humidity, and other weather conditions are given by weather forecasters. Such forecasts enable the farmers to better organize their irrigation and field work, and mitigate the risk of weather variability (Wang et al., 2024) [62] .

Soil Moisture Sensors

Soil moisture sensors continuously monitor soil moisture and give real-time information of the availability of water to crops. This allows a control-led irrigation management, by applying water only when needed and avoiding both underand overirrigation (Urdanigo-Zambrano et al., 2025) [60] .

Crop Growth Models

Cropping growth stages use weather, soil, and crop information to represent plant development and water needs at different growing periods of the crop. These models enable better decisions in irrigation and crop management (Tolomio and Casa, 2020) [57] .

Improved Water-Use Efficiency

Weather forecasts, soil moisture sensors and crop growth models can be integrated to enable more precise water application in relation to crop requirements. This is to enhance the efficiency of water use and curtail wasteful consumption of water (NK and Ga, 2024) [40] .

Reduced Drought Vulnerability

Current and predicted soil moisture status and weather conditions enable farmers to take a proactive stance on water shortages. As a consequence, crops gain in drought stress tolerance and yield loss is minimized (Islam et al., 2025) [22] .

Better Groundwater Conservation

Precision irrigation practices mitigate over-exploitation of groundwater through optimization of water application rates and timing. This contributes to groundwater resource conservation and sustainable water management in agriculture. ) [65,39] . [32,9] . [4] .

Biodiversity Monitoring

Heat Stress Management

Flood Risk Reduction

Analysis of landscape hydrology, runoff estimation, and water retention optimization will greatly contribute to sustainable land and water resources management. Landscape hydrology analyses the transport, fate, and storage of water across agricultural landscapes, while runoff prediction provides estimates of the volume and flow rate of water exiting fields during precipitation events. These evaluations contribute to the development of water retention optimization strategies, such as better drainage, conservation structures and soil management, thereby increasing water supply, reducing soil losses, minimizing nutrient losses, and boosting the adaptive capacity of agricultural systems to climatic variability (Sene, 2024) [50] .

Pest and Disease Resilience

Early warning systems, predictive modeling of diseases and reduction of pesticide dependency are important elements of intelligent crop protection. Early warning systems are based on realtime weather, crop, and environmental data to detect conditions that may promote pest and disease outbreaks prior to causing significant damage. Predictive disease models use the data to predict when and how severe a potential infection could be, allowing producers to intervene in a timely and targeted manner. This enables farmers to use preventive and precision management practices for a reduced pesticide dependence, less production costs, less environmental impacts, and a more sustainable agriculture system (Biswal et al., 2025) [12] .

Emerging Technologies Supporting Regenerative Agriculture Digital Twins

Digital Twins are digital representations of farms, fields, or agricultural systems that incorporate live data from sensor networks, weather stations, and management records. They provide the opportunity to simulate alternative management scenarios, evaluate climate and production risks, and even assist in the optimization of farming decisions prior to field application (Manivasagam, 2025) [36] .

Explainable Ai

Explainable AI (XAI) is a class of AI systems that provide their recommendations and predictions in a way that is accessible and understandable to users. By enhancing transparency and interpretability, the confidence of farmers is increased, decision-making may be more informed, and the diffusion of AI-based agricultural technologies is facilitated (Chettri et al., 2026) [15] .

Edge Computing

Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, such as on-site with a sensor or an instrument, on a drone, or a farm machinery, rather than sending all the data to cloud servers. This allows for real-time processing and decision-making, as well as reducing the lag time associated with data transmission and dependence on the internet (Khan et al., 2026) [28] .

Blockchain Integration

Blockchain technology allows for a safe, transparent, and immutable digital record of farm activities and transactions. In regenerative farming, it enables tracking of sustainable agricultural practices, soil carbon sequestration, and carbon credit trading, promoting accountability and market trust (Parhamfar et al., 2024) [44] .

Autonomous Robotics

Autonomous robotics is the use of automated machines with sensors, AI, GPS navigation, and other technologies to carry out farming operations with little human intervention. Robots can now perform precision planting, selective weeding, crop monitoring and field scouting, enhancing efficiency, minimizing labour needs and enabling sustainable crop management (Botta et al., 2022) [13] .

Source: KhetiBuddy. https://khetibuddy.com/blogs/innovative-tools-and-technologies supporting-regenerative-agriculture/?utm

Challenges and Limitations Technical Challenges

Data limitations, inadequate interoperability, and model uncertainty are significant impediments in applying advanced digital technology and artificial intelligence in agriculture. Data scarcity is related to the fact that high quality, standardized and long term datasets are scarcely available to develop accurate models and make sound decisions. Bad interoperability is the existence of problems related to data and communication interoperability in different sensors, platforms, software systems and agriculture technologies within which each side wants exchange information to its own peculiar format. In 38 addition, model uncertainty is induced by environmental conditions, quality of data and assumptions made in predictive models that may cause errors in predictions and recommendations. It will be important to overcome these challenges to enhance the reliability, scale and impact of digital agriculture solutions (Hossen et al., 2025; Challinor et al., 2018) [21,14] .

Economic Constraints

High capital expenditure, burden of infrastructure and low affordability for small farms constitute major impediments for enhancement of adoption level of high-tech agriculture. Various digital tools, precision farming machinery, sensors, drones, and automated systems demand significant initial investment and supporting infrastructure such as dependable electricity, internet access, and technical assistance. These costs may be prohibitive for small-scale and resource-poor farmers, limiting their access to cutting-edge technologies and producing disparities in technology adoption. [17,25,37] .

Social Challenges

Research Gaps

Future work on AI-enabled regenerative agriculture should prioritise long-term field-based validation of AI-enabled regenerative systems, to test effectiveness in a range of agricultural environments and over time. The development of AI models for smallholder systems, which are specific to their resource-constrained environment and management practices is required. Developing a standard set of indicators for the assessment of regenerative agriculture will provide a baseline against which soil health, biodiversity, carbon sequestration and sustainability can be regularly quantified. There is a call for research focusing on so-called techno-socio-environmental data integration to enable 'holistic' decision-making that serve environmental and livelihood outcomes. In addition, the advancement of interpretable and transparent AI models is critical for the enhancement of user trust, accountability, and acceptance. Lastly, development of regional climate resilience models will enable farmers to tailor regenerative principles to local climates, increasing the relevance of, and success in implementing, climate-smart agricultural practices (Omotayo et al., 2025; Rajbongshi et al., 2026) [42,48] . [20] .

Conclusion

AI-enabled digital twins build virtual farm models that interact with live data to simulate management strategies, forecast results, and enhance decision-making. Climate prediction products employ sophisticated analytics and AI, yielding weather and climate predictions that inform farmers on managing risk and adapting to evolving environmental conditions. Drones and robots autonomously execute a variety of agricultural activities including crop monitoring, precision spraying, seeding, and weeding, with little to no human supervision, optimizing productivity while minimizing labor needs. Platforms for carbon accounting facilitate measuring, monitoring, and verifying sequestration of carbon and greenhouse gas emissions, contributing to carbon credit systems and sustainability reporting. Heterogeneous sensor networks aggregate information from soil sensors, weather stations, drones, satellites, and farm equipment to deliver integrated realtime data about crop and environmental conditions. Generative AI-based advisory systems process extensive, heterogeneous datasets, including crop management, resource optimization, and sustainable agriculture best practices, to provide tailored recommendations that can increase productivity, resilience, and environmental sustainability.

References

1. Ali Z, Muhammad A, Lee N, Waqar M, Lee SW. Artificial intelligence for sustainable agriculture: A comprehensive review of AI-driven technologies in crop production. Sustainability. 2025;17(5):2281
2. Al-Musawi ZK, Vona V, Kulmány IM. Utilizing different crop rotation systems for agricultural and environmental sustainability: A review. Agronomy. 2025;15(8):1966
3. Atapattu AJ, Perera LK, Nuwarapaksha TD, Udumann SS, Dissanayaka NS. Challenges in achieving artificial intelligence in agriculture. In: Artificial intelligence techniques in smart agriculture. Singapore: Springer Nature Singapore; 2024:7-34
4. Badagliacca G, Testa G, La Malfa SG, Cafaro V, Lo Presti E, Monti M. Organic fertilizers and bio-waste for sustainable soil management to support crops and control greenhouse gas emissions in mediterranean agroecosystems: A Review. Horticulturae. 2024;10(5):427
5. Balamurali D, Chakankar S, Sharma G, Pagey AP, Natarajan M, Shaik S, et al. A solar-powered, internet of things (IoT)-controlled water irrigation system supported by rainfall forecasts utilizing aerosols: a review. Environment, Development and Sustainability. 2025:1-40
6. Basheer S, Wang X, Farooque AA, Nawaz RA, Pang T, Neokye EO. A review of greenhouse gas emissions from agricultural soil. Sustainability. 2024;16(11):4789
7. Bhatia T, Sindhu SS. Sustainable management of organic agricultural wastes: contributions in nutrients availability, pollution mitigation and crop production. Discover Agriculture. 2024;2(1):130
8. Biswal KA, Das S, Jana S. Emerging Plant Disease Prediction Through Forewarning Model and Artificial Intelligence (AI) Under Climate Change Scenario. In: Climate Resilient and Sustainable Agriculture: Volume 2: Social and Transformative Strategies. Cham: Springer Nature Switzerland; 2025:155-196
9. Botta A, Cavallone P, Baglieri L, Colucci G, Tagliavini L, Quaglia G. A review of robots, perception, and tasks in precision agriculture. applied mechanics. 2022;3(3):830854
10. Challinor AJ, Müller C, Asseng S, Deva C, Nicklin KJ, Wallach D, et al. Improving the use of crop models for risk assessment and climate change adaptation. Agricultural systems. 2018;159:296-306
11. Chettri K, Sen B, Ghosal P. Deep learning for precision agriculture: a systematic review of methods, challenges, and future directions. Knowledge and Information Systems. 2026;68(1):35
12. Colombi G, Martani E, Fornara D. Regenerative organic agriculture and soil ecosystem service delivery: A literature review. Ecosystem Services. 2025;73:101721.
13. dos Reis GA, Martínez-Burgos WJ, Pozzan R, Pastrana Puche Y, Ocán-Torres D, de Queiroz Fonseca Mota P, et al. Comprehensive review of microbial inoculants: agricultural applications, technology trends in patents, and regulatory frameworks. Sustainability. 2024;16(19):8720.
14. Erike A, Ikerionwu C, Azubogu A, Obodoagwu V. Is AI for illiterate farmers? A systematic literature review of AI and machine learning applications and challenges for precision agriculture. Discover Artificial Intelligence. 2025;5(1):204.
15. Gardezi M, Joshi B, Rizzo DM, Ryan M, Prutzer E, Brugler S, et al. Artificial intelligence in farming: Challenges and opportunities for building trust. Agronomy Journal. 2024;116(3):1217-1228.
16. Gomide CS, de Paula Medeiros F, Matheus A, Oliveira VS, Oliveira LC, de Moura LHG, et al. The Potential of Remineralizers and Bioinputs as a Strategy for Sovereignty in Tropical Agriculture. In: Soil Remineralizers and Silicate Fertilizers: Regional Solutions to a Healthy Agriculture. Cham: Springer Nature Switzerland; 2026.
17. Gund R, Badgujar CM, Samiappan S, Jagadamma S. Application of Digital Twin Technology in Smart Agriculture: A Bibliometric Review. Agriculture. 2025;15(17):1799.
18. Hossen MI, Awrangjeb M, Pan S, Mamun AA. Transfer learning in agriculture: a review. Artificial Intelligence Review. 2025;58(4):97.
19. Islam MB, Guerrieri A, Gravina R, Delaney DT, Fortino G. From Traditional Machine Learning to Fine-Tuning Large Language Models: A Review for Sensors-Based Soil Moisture Forecasting. Sensors. 2025;25(22):6903.
20. Jabed MA, Murad MAA. Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability. Heliyon. 2024;10(24).
21. Jayasinghe SL, Thomas DT, Anderson JP, Chen C, Macdonald BC. Global application of regenerative agriculture: a review of definitions and assessment approaches. Sustainability. 2023;15(22):15941.
22. John D, Hussin N, Shahibi MS, Ahmad M, Hashim H, Ametefe DS. A systematic review on the factors governing precision agriculture adoption among small-scale farmers. Outlook on Agriculture. 2023;52(4):469-485.
23. Kabato W, Getnet GT, Sinore T, Nemeth A, Molnár Z. Towards climate-smart agriculture: Strategies for sustainable agricultural production, food security, and greenhouse gas reduction. Agronomy. 2025;15(3):565.
24. Katumo DM, Liang H, Ochola AC, Lv M, Wang QF, Yang CF. Pollinator diversity benefits natural and agricultural ecosystems, environmental health, and human welfare. Plant Diversity. 2022;44(5):429-435.
25. Khan RT, Akhtar N, Nazeer S, Hassan A, Ishfaq B, Khan MT, et al. Climate Smart Agricultural Systems: Integration of Internet of Things (IoT), Convolutional Neural Networks (CNN) and Edge Computing for Climate Adaptive and Sustainable Food Production. Research Consortium Archive. 2026;4(2):2514-2528.
26. Khangura R, Ferris D, Wagg C, Bowyer J. Regenerative agriculture-A literature review on the practices and mechanisms used to improve soil health. Sustainability. 2023;15(3):2338.
27. Kumar A, Meena NK, Mazumder C, Rath P, Inamdar F, Singh A, et al. Application of artificial intelligence and machine learning in fruit crop management: A review. Plant Archives. 2026;26(1):823-830
28. Lakhiar IA, Yan H, Zhang C, Wang G, He B, Hao B, et al. A review of precision irrigation water-saving technology under changing climate for enhancing water use efficiency, crop yield, and environmental footprints. Agriculture. 2024;14(7):1141
29. Li T, Cui L, Kuhnert M, McLaren TI, Pandey R, Liu H, et al. A comprehensive review of soil organic carbon estimates: Integrating remote sensing and machine learning technologies. Journal of Soils and Sediments. 2024;24(11):3556-3571
30. Mabhaudhi T, Chibarabada TP, Sikka A. Status of integrated crop-livestock research in the mixed farming 40 systems of the Global South: a scoping study. Frontiers in Sustainable Food Systems. 2023;7:1241675
31. Madhav N. Harnessing Transformative AI Technologies: Pathway to Transformation in Digital Agriculture. African Food Systems: Rethinking Prospects for Continental Sustainable Transformation. Cham: Springer Nature Switzerland; 2025.
32. Manivasagam VS. From bytes to farm: Transferability of industrial digital twins in agricultural systems. Journal of Biosystems Engineering. 2025;50(1):130-144
33. Mizik T. How can precision farming work on a small scale? A systematic literature review. Precision agriculture. 2023;24(1):384-406
34. Morales-Reyes Z, Barbosa JM, Sanchez-Zapata JA, PerezIbarra I. Farmer perceptions of the vulnerabilities of traditional livestock farming systems under global change. Ambio. 2025;54(8):1353-1371
35. Munna MNH, Lal R. Impacts of cover cropping and organic amendments on soil physical quality under temperate climate. Cogent Food & Agriculture. 2025;11(1):2467452
36. NK S, Ga D. Coupled weather and crop simulation modeling for smart irrigation planning: a review. Water Supply. 2024;24(8):2844-2865
37. O'Donoghue T, Minasny B, McBratney A. Regenerative agriculture and its potential to improve farmscape function. Sustainability. 2022;14(10):5815
38. Omotayo AO, Adediran SA, Omotoso AB, Olagunju KO, Omotayo OP. Artificial intelligence in agriculture: ethics, impact possibilities, and pathways for policy. Computers and Electronics in Agriculture. 2025;239:110927
39. Pai DG, Kamath R, Balachandra M. Deep learning techniques for weed detection in agricultural environments: A comprehensive review. IEEE Access. 2024;12:113193113214
40. Parhamfar M, Sadeghkhani I, Adeli AM. Towards the net zero carbon future: A review of blockchain-enabled peerto-peer carbon trading. Energy Science & Engineering. 2024;12(3):1242-1264
41. Patil M, Perumal C, Choudhari P, Pasumarthi R, Sawargaonkar G, Singh R. Differential impacts of regenerative agriculture practices on soil organic carbon: a metaanalysis of studies from India. Scientific Reports. 2025;15(1):33470
42. Pawase PP, Chang YK, Kim YJ. Variable-rate fertilizer application technology for nutrient management: A review. International Journal of Agricultural and Biological Engineering. 2023;16(4):1-12.
43. Rai P, Godfrey SS, Storer CE, Behrendt K, Ip RH, Nordblom TL. Unravelling regenerative agriculture's sustainability benefits and outcomes: a scoping review. Sustainability. 2025;17(3):981.
44. Rajbongshi A, Johora FT, Hossain A, Sarker MS, Rahman MH, Rahman MW, et al. Leveraging explainable AI for sustainable agriculture: a comprehensive review of recent advances. Artificial Intelligence Review. 2026.
45. Sajjad M, Ijaz B, Hur M. Climate smart soil quality management strategies for resilient agriculture. Sustainable Plant Nutrition and Soil Quality Management. 2026;199. 50. Sene K. Climate Change Prediction. In: Hydrometeorology: Forecasting and Applications. Cham: Springer Nature Switzerland; 2024:473-499.
46. Sharma RK, Nwosu N, Singh L, Kramer A, Singh H, Bheemanahalli R, et al. Agricultural sustainability under unpredicted atmospheric changes-strategies to enhance crop resilience and system efficiency: a narrative review. Discover Agriculture. 2025;3(1):124.
47. Sher A, Li H, Ullah A, Hamid Y, Nasir B, Zhang J. Importance of regenerative agriculture: Climate, soil health, biodiversity and its socioecological impact. Discover Sustainability. 2024;5(1):462.
48. Shidayaichenbi D, Singh S, Thokchom S, Singh AK, Singh SK, Ningombam A, et al. Precision Nutrient Management for Reducing Environmental Impacts Due to Nonjudicial Use of Inputs. In: Climate-Smart Agricultural Technologies: Approaches for Field Crops Production Systems. Singapore: Springer Nature Singapore; 2025:117135.
49. Singh M, Jha S, Pathak D, Maisnam G. Advancing biofertilizers: the evolution from single-strain formulations to synthetic microbial communities (SynCom) for sustainable agriculture. Discover Plants. 2025;2(1):226.
50. Thakur M, Subba A. Integration of Robotics in Soilless Cultivation Systems for Medicinal Plant Production. In: Transforming Medicinal Plant Agriculture: Implementing Soilless Systems and AI Innovations for Smart Farming. Singapore: Springer Nature Singapore; 2026:105-130.
51. Toker P, Canci H, Turhan I, Isci A, Scherzinger M, Kordrostami M, et al. The advantages of intercropping to improve productivity in food and forage production-a review. Plant production science. 2024;27(3):155-169.
52. Tolomio M, Casa R. Dynamic crop models and remote sensing irrigation decision support systems: A review of water stress concepts for improved estimation of water requirements. Remote Sensing. 2020;12(23):3945.
53. Tomar A, Kumar A, Estrada RRG, Parmar K, eds. Microbial Consortia in Plant Science and Sustainable Agriculture. John Wiley & Sons; 2026.
54. Tripathi P, Jha AK, Mahapatra C, Ansari MA, Mishra DK. Smart Farming System Using IoT, Embedded Electronics, and Wireless Sensor Networks. In: 2025 International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI); 2025:877-880. IEEE.
55. Urdanigo-Zambrano J, Torres B, De-Pablos-Heredero C, Herrera-Feijoo R, García A. Open Data Reuse in Agricultural, Livestock, and Environmental Systems: A Global Scoping Review with a Case Analysis of Ecuador. Land. 2025;15(1):13.
56. Vedamurthy KB, Patil M, Vaishnavi PV, Suman L, Ajayakumar, Sagar. Unlocking AI's Potential in Agriculture: The Critical Role of Data: KB Vedamurthy et al. Journal of the Indian Institute of Science. 2026:1-22.
57. Wang J, Wang Y, Qi Z. Remote sensing data assimilation in crop growth modeling from an agricultural perspective: new insights on challenges and prospects. Agronomy. 2024;14(9):1920.
58. Yuan X, Li S, Chen J, Yu H, Yang T, Wang C, et al. Impacts of global climate change on agricultural production: a comprehensive review. Agronomy. 2024;14(7):1360.
59. Zhang H, Ghahramani A, Ali A, Erbacher A. Cover cropping impacts on soil water and carbon in dryland cropping system. PLoS One. 2023;18(6):e0286748.
60. Zhang K, Liu Z, McCarl BA, Fei CJ. Enhancing agricultural soil carbon sequestration: a review with some research needs. Climate. 2024;12(10):151.
61. Zhao J, Ramli NN, Nawi NM, Seng KWK, Sharifuddin J. Bridging the gap between natural and social sciences: a bibliometric review of climate-smart agriculturechallenges and opportunities. Environment, Development and Sustainability. 2025:1-30.
62. Zheng H, Ma W, He Q. Climate-smart agricultural practices for enhanced farm productivity, income, resilience, and greenhouse gas mitigation: a comprehensive review. Mitigation and Adaptation Strategies for Global Change. 2024;29(4):28.
63. Lal R. Cover cropping and the "4 per Thousand" proposal. Journal of Soil and Water Conservation. 2015;70(6).https://doi.org/10.2489/jswc.70.6.141a
64. Sharma R, Dubin H. Effect of wheat cultivar mixtures on spot blotch (Bipolaris sorokiniana) and grain yield. Field Crops Research. 1996;48(2-3):95101.https://doi.org/10.1016/s0378-4290(96)01031-3