Journal of the Indian Institute of Science最新文献

{"title":"Editorial: Special Issue on Digital Agriculture","authors":"Lalith Achoth","doi":"10.1007/s41745-025-00494-4","DOIUrl":"10.1007/s41745-025-00494-4","url":null,"abstract":"","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"135 - 137"},"PeriodicalIF":2.3,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in Atomic-Scale Modeling of Materials and Molecules","authors":"Ananth Govind Rajan, Phani Motamarri, Tej S. Choksi, Vikram Gavini, Shaama Mallikarjun Sharada","doi":"10.1007/s41745-026-00510-1","DOIUrl":"10.1007/s41745-026-00510-1","url":null,"abstract":"","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"407 - 408"},"PeriodicalIF":2.3,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review of Soil Sensors for Precision Agriculture","authors":"Nikila Nair,&nbsp;Jose Joseph,&nbsp;G. K. Ananthasuresh","doi":"10.1007/s41745-026-00502-1","DOIUrl":"10.1007/s41745-026-00502-1","url":null,"abstract":"<div><p>Ever since the dawn of human civilization, agriculture has driven scientific, engineering, and technological developments. Today, it is technology’s turn to drive agriculture because of the increasing population of the world and accompanying challenges in food production that include reducing cultivable land, compromised soil health, ever-increasing stressors on crops, detrimental climatic changes, scarcity of water and energy, etc. Technologies developed for human health, wellbeing, and comfort are now being utilized for improving agriculture, and justifiably so. Just as precision medicine is being developed for humans, it is being adapted for precision agriculture. Central to this critical endeavour are the soil sensors. To complement many insightful reviews that are available on this topic, this review article takes a comprehensive view by focusing on only three important soil sensors, namely, pH, nutrients, and moisture. In order to provide a complete view of the topic, this paper begins with a glimpse of the ever-growing list of research publications and proceeds to give an overview of transduction principles, multiple ways of measuring the aforesaid three soil parameters, capabilities and limitations of existing sensor techniques, status of commercially available sensors, integration of sensors into state-of-the-art precision and digital agricultural practices, and finally future perspectives.</p></div>","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"143 - 185"},"PeriodicalIF":2.3,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editor’s Desk","authors":"G. K. Ananthasuresh","doi":"10.1007/s41745-026-00509-8","DOIUrl":"10.1007/s41745-026-00509-8","url":null,"abstract":"","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"405 - 406"},"PeriodicalIF":2.3,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Agriculture: From Data to Decisions, and Decisions to Farmer Welfare","authors":"Lalith Achoth,&nbsp;M. S. Bobji,&nbsp;Jaywant Arakeri,&nbsp;Y. Narahari","doi":"10.1007/s41745-026-00500-3","DOIUrl":"10.1007/s41745-026-00500-3","url":null,"abstract":"<div><p>In this article, we introduce digital agriculture and describe the various building blocks of digital agriculture. We bring out the promise, issues, and challenges. We conclude with specific directions for future research and innovation for transforming the potential of digital agriculture to reality.</p></div>","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"139 - 142"},"PeriodicalIF":2.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editor’s Desk","authors":"G. K. Ananthasuresh","doi":"10.1007/s41745-026-00498-8","DOIUrl":"10.1007/s41745-026-00498-8","url":null,"abstract":"","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"133 - 134"},"PeriodicalIF":2.3,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148434101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unlocking AI’s Potential in Agriculture: The Critical Role of Data","authors":"K. B. Vedamurthy,&nbsp;Manojkumar Patil,&nbsp; Vaishnavi,&nbsp;V. Priyanka,&nbsp;L. Suman,&nbsp; Ajayakumar,&nbsp; Sagar","doi":"10.1007/s41745-026-00505-y","DOIUrl":"10.1007/s41745-026-00505-y","url":null,"abstract":"<div><p>India generates substantial volumes of public agricultural data, yet artificial intelligence (AI) adoption in farming remains limited and largely confined to pilot initiatives. This paper examines this gap by assessing India’s agricultural data infrastructure against the requirements of AI systems deployed at scale. Drawing on a systematic review of major national datasets and digital initiatives—including Soil Health Cards, crop insurance, AgriStack, and selected state platforms—we identify persistent structural constraints, including temporal misalignment between data collection and agricultural decision cycles, spatial fragmentation arising from the absence of common geocodes linking soil, weather, and yield information, limited machine readability due to reliance on static data formats, and unclear governance frameworks that restrict data access and reuse. These deficiencies impede cross-dataset integration and automated decision support, with disproportionate consequences for smallholders, who constitute 86% of India’s farmers and lack the capacity to compensate for weak data infrastructure. Drawing on implementation evidence from India and comparative international experiences, the paper identifies recurring features associated with scalable digital agriculture systems, including incentives linked to data provision, service bundling through local institutions, and sensor-enabled risk management. Building on these insights, we define the core attributes of AI-ready agricultural data—persistent identifiers, machine-accessible formats, temporal alignment, semantic interoperability, and transparent governance—and show that even India’s most advanced initiatives remain only partially aligned with these requirements. The paper’s contribution is primarily diagnostic: it demonstrates that data infrastructure limitations, rather than algorithmic capability, constitute the principal bottleneck to scalable AI adoption in Indian agriculture and proposes an operational framework for assessing agricultural data readiness.</p></div>","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"283 - 304"},"PeriodicalIF":2.3,"publicationDate":"2026-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative AI as an Interface Between Farmers and AI Algorithms: Opportunities and Challenges","authors":"Wenbo Zhang,&nbsp;Amulya Yadav","doi":"10.1007/s41745-025-00490-8","DOIUrl":"10.1007/s41745-025-00490-8","url":null,"abstract":"<div><p>Artificial Intelligence (AI) has transformed industries worldwide, yet its adoption in agriculture remains limited, particularly in low-resource settings. Farmers in developing countries face two chronic challenges: (1) the assumption of reliable Internet access is often an incorrect one to make in low-resource settings in developing countries; and (2) the complexity of AI algorithms often makes it challenging to be used by low-resource farmers, who often lack the required significant technical expertise needed to use these AI algorithms effectively. As a result, many low-resource farmers are excluded from the benefits of AI technologies, thereby exacerbating the global digital divide. This paper explores whether Generative AI, particularly large language models (LLMs) like ChatGPT, Gemini, and LLaMA, can bridge this ever-increasing divide, and make AI tools and algorithms more accessible to farmers in developing countries. LLMs represent a breakthrough in interactivity, allowing users to engage conversationally with AI algorithms. Historically, AI has lacked this interactivity, making it difficult for non-technical users to understand or act on algorithmic outputs. Generative AI models, however, offer a solution by serving as intuitive interfaces that translate complex AI algorithms into actionable insights. These models enable farmers to ask questions and receive answers in natural language, along with enabling them to provide feedback so that the underlying AI algorithms could be adapted to their specific needs. Furthermore, by leveraging non-Internet modalities like voice-based interfaces and SMS, these models can extend AI’s reach to remote areas lacking reliable Internet access. This paper takes the position that Generative AI can indeed serve as a bridge between low-resource farmers and heavy-duty AI algorithms. This paper discusses how LLMs bring a unique level of interactivity to AI. We then describe a vision for how these models can enable farmers to access algorithmic agricultural advice in an accessible and user-friendly manner. Subsequently, we outline the challenges that must be overcome to realize this vision, including technical, infrastructural, and ethical considerations. Finally, we conclude with a discussion on the broader implications of using Generative AI to address the needs of low-resource farming communities. By exploring this intersection of Generative AI and agriculture, this paper aims to contribute to the ongoing dialogue on making AI technologies inclusive and impactful for underserved populations, ensuring that the transformative potential of AI is enjoyed equitably by all sections of society.</p></div>","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"305 - 321"},"PeriodicalIF":2.3,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s41745-025-00490-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temperature as an Indicator of Water Stress in Leaves and Plants: Leaf-Scale to Field-Scale","authors":"Rather Laasani Sanya Shabir,&nbsp;Shivani Chauhan,&nbsp;Navneet Kumar","doi":"10.1007/s41745-026-00497-9","DOIUrl":"10.1007/s41745-026-00497-9","url":null,"abstract":"<div><p>The water uptake status of a plant is directly linked to water stress levels in the leaves. It is important to identify this stress strategically, and if possible in advance. This study reviews current practices and highlights canopy/leaf temperature as a reliable indicator of plant water status and stress. Several indices were developed to quantify this stress out of which the crop water stress index (CWSI), based on temperature (of leaf and reference surfaces) measurement, is the most widely accepted technique despite challenges posed by the environmental variability. Further, experimental results are also presented, carried out using IR imaging to correlate the leaf temperature to different stages such as ‘healthy’, ‘dying/wilted’, and completely ‘dead/dry’. Healthy leaf temperatures are closely aligned with white dry and black wet reference surfaces. Dying leaves exhibited temperatures ranging between yellow and red dry surfaces, while dead leaves aligned between dry green and blue surfaces, with most data clustering near green, indicating its suitability for representing dead leaf conditions. Dying leaves were observed to be 8–10 °C warmer than healthy leaves under similar ambient conditions. These findings helped to formulate a refined and user-friendly CWSI scale using optimal combinations of dry and wet reference surfaces, enabling accurate detection of water stress stages. This work underscores the potential of temperature-based assessments for early stress detection and precision irrigation.</p></div>","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"187 - 210"},"PeriodicalIF":2.3,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling Large Language Models and Agentic Workflows on Legacy GPU Architectures for Building Scalable Farmer Advisory Systems in India","authors":"Janaki Chintalapati,&nbsp;Akhil Rajeev P,&nbsp;Bhaswata Choudhury,&nbsp;Mrunal Bedare,&nbsp;Yogender Kumar","doi":"10.1007/s41745-026-00501-2","DOIUrl":"10.1007/s41745-026-00501-2","url":null,"abstract":"<div><p>Large Language Models (LLMs) and agentic workflows are increasingly being applied and evaluated across multiple domains, including agriculture, where they support intelligent, context-aware advisory services. Such systems can assist farmers across the agricultural lifecycle, including soil preparation, sowing decisions, crop health monitoring, pest identification, irrigation planning, harvesting, and post-harvest management. In the Indian context, where agricultural practices vary significantly across regions and are deeply rooted in local knowledge, Generative AI and Natural Language Processing can be used to deliver timely, localized, and language-accessible agricultural advisories. Adapting LLMs with region-specific domain knowledge enhances their relevance and practical utility, particularly when delivered through chatbot-based interfaces that support local languages. However, training and deploying such systems typically require substantial computational resources, often relying on modern GPU architectures. This paper examines the practical considerations involved in leveraging existing computational infrastructure for Generative AI workloads and proposes an approach for their effective use in resource-constrained settings. The paper further presents a proposed system architecture incorporating agentic AI workflows for agricultural applications, illustrating how task-oriented agents powered by LLMs can support agricultural chatbot functionality. It also discusses key design considerations in integrating model outputs with agent-based workflows and constructing scalable pipeline architectures tailored to agricultural use cases.</p></div>","PeriodicalId":675,"journal":{"name":"Journal of the Indian Institute of Science","volume":"105 :","pages":"323 - 335"},"PeriodicalIF":2.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}