Volume 14 | Issue 4 |

 Abstract

Every year, plant diseases cause large losses in crop yield, posing a serious threat to global food security. Effective intervention depends on a prompt and precise diagnosis of the disease, but farmers often lack access to specialists in plant pathology, particularly in rural areas. In order to help with plant disease diagnosis, this paper introduces PlantDocBot, a multimodal AI-driven chatbot that can accept both textual symptom descriptions and leaf images. The system combines Natural Language Processing (NLP) for symptom-based diagnosis and a Convolutional Neural Network (CNN) for image-based disease identification in an intuitive chat interface. The use of transfer learning (MobileNet/ResNet-based models) for effective image classification and a fine-tuned transformer (BERT-based) to understand symptoms reported by farmers are two important innovations. Once a disease has been identified, a treatment recommendation module suggests evidence-based treatments based on a carefully curated knowledge base. With a Streamkit-built frontend prototype that allows image upload and real-time communication, we deployed PlantDocBot using HuggingFace Transformers for natural language processing and TensorFlow for CNN model training. A brief user study reveals the chatbot's responses are pertinent and useful, and evaluation on benchmark datasets demonstrates high disease detection accuracy (up to 95% top-1 accuracy). Small-scale farmers can now access real-time, affordable diagnosis thanks to PlantDocBot's scalable approach to democratizing plant disease expertise. To further close the knowledge gap in agriculture, future work will investigate voice interfaces, multilingual support, and increased coverage of disease.

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 Keywords

PlantDocBot, Plant disease classification, Computer vision in agriculture, BERT, Transfer learning, Smart farming

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid evolution of digital agriculture had necessitated the development of intel-ligent systems that could bridge the in-formation gap for smallholder farmers. This project presents an AI-Based Farmer Query Support and Advisory System, a combined framework designed to provide real-time and data-driven agricultural guidance through advanced computational techniques. To leverage Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), this system ensures that queries related to crop management, pest control, and soil health are answered with high factual accuracy, minimizing the risks of AI hallucinations often found in standard generative models [1], [2], [11]. A core challenge in global agriculture is the linguistic barrier; as a result, this sys-tem implements multilingual and cross-lingual architectures to support low-resource languages, ensuring that farmers can interact in their native dialects while the system retrieves expert knowledge from scientifically validated English cor-pora [3], [8], [21]. To enhance the robust-ness of its recommendations, the frame-work utilizes Multi-Modal Fusion and IoT-based real-time data integration [9], [15]. This allows the "Advisory and Rec-ommendation Engine" to process diverse inputs , ranging from voice and text que-ries to real-time weather forecasts and soil sensor data , to generate site-specific ad-vice [4], [7], [22]. Furthermore, the system incorporates Deep Learning models, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), to provide accu-rate agricultural commodity price fore-casting and yield predictions [5], [12]. This predictive capability empowers farmers with market intelligence and poli-cy scheme optimization, facilitating better financial decision-making [15], [19]. By ensuring accessibility in remote regions with limited connectivity, this architecture includes an Offline Edge AI framework, enabling localized processing of sensor data and immediate advisory delivery [6], [22]. The integration of a Feedback and Learn-ing Module creates a closed-loop system where user interactions and expert-vetted "Golden Q&A" datasets are used to itera-tively improve the model's performance [4], [11]. By combining state-of-the-art AI innovation with practical agricultural ex-tension needs, this system serves as a scalable roadmap for the digital transfor-mation of global agrifood systems, ulti-mately enhancing productivity, sustaina-bility, and food security for smallholder farmers [18], [20], [23].

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 Keywords

Large Language Models (LLMs) [1, 21], Retrieval-Augmented Generation (RAG) [3, 23], Random Forest Classifier [2, 12], Nearest Neighbor [3, 21], Multi-Modal Fusion [15, 17], Agricultural Advisory System [7, 10], Voice-based Farmer Sup-port [8, 15], Smallholder Farmers [4, 11, 20], and Market Intelligence [5, 15].

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The Khumri System Prevalent in the Jaunsar-Bawar Region: A Critical Study

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The Khumri System Prevalent in the Jaunsar-Bawar Region: A Critical Study

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This study explores school-community partnerships to promote inclusive and quality education and to make inclusion more meaningfully successful as successful inclusive education requires mobilizing the cooperation of families, as well as the support of the community, voluntary organizations, and the general public. Leveraging community resources and local partnerships fosters high-quality educational and enrichment opportunities; this is because it broadens the scope of experiences typically offered to all students particularly those with special needs while simultaneously enhancing their access to local expertise (Kaushik, 2019). Parents should also play a role in formulating policies related to inclusion. They should be involved in supporting the changes necessary to develop inclusive schools. Teachers holding senior positions bear a responsibility toward the wider community as well; they should not only strive to improve or develop their own schools but also collaborate with stakeholders outside the school premises (Sharma, 2021). When families and communities stand united with educational institutions, they ensure that inclusion is not merely a policy mandate but a lived reality, providing every child, regardless of ability, with the equal opportunity to learn, grow, and thrive.

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Inclusive education, Inclusive practices, School-community partnerships, Community resources, Family support

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Bereavement, Conversational AI, Retrieval-Augmented Generation, Financial Guidance, Digital Asset Management, Natural Language Processing, India.

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The safety and reliability of highway bridges are critical for modern transportation systems, as structural failures can lead to serious economic losses and safety hazards. This work proposes an advanced monitoring and maintenance framework based on Wireless Sensor Networks (WSNs) to continuously observe the structural health of highway bridges. In the proposed system, multiple sensors such as strain gauges, vibration sensors, temperature sensors, and displacement sensors are strategically installed on different parts of the bridge to collect real-time structural data. The sensed information is transmitted wirelessly to a central monitoring unit where embedded processing and data analysis techniques evaluate the bridge condition and detect abnormal structural behaviour at an early stage. The system enables continuous condition assessment, early fault detection, and predictive maintenance, thereby reducing the risk of sudden structural failure and minimizing maintenance costs. By integrating wireless communication, embedded processing, and real-time monitoring, the proposed approach significantly improves bridge safety, operational efficiency, and long-term infrastructure management.

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Wireless Sensor Networks (WSN), Structural Health Monitoring, Bridge Safety, Embedded Monitoring System, Real-Time Data Acquisition, Vibration Sensors, Strain Measurement, Predictive Maintenance, Infrastructure Monitoring, Remote Bridge Monitoring.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Yuva aur maanasik svaasthya

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Yuva aur maanasik svaasthya

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Creative Commons Attribution 4.0 and The Open Definition

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In the present paper, it has been argued how the law on Geographical Indications (GI) provides the best way of protecting Traditional Knowledge (TK). It has been illustrated with reference to the Indian legislation enacted to provide domestic protection to the GIs and its provisions which recognize GIs as a sui generis form of IPRs. Unlike any other Intellectual Property Right (IPR), the protection granted by GIs to an object rests upon a whole community instead of an individual. The evolution of the law on GIs starting from the Uruguay Round of GATT till the Doha Work Programme has been discussed in brief in order to demonstrate the ongoing conflict between the developed and developing countries over the extent of protection accorded to a GI object under the law. Further, criteria for the protection of TK under the GI have also been analyzed. The discussion is based on several aspects like equity and justice, protection of biological diversity, avoidance of biopiracy, etc. The interrelation between GI and TK has been established with respect to two types of protection, namely, constructive and defensive. Examples have been taken from the state of Assam (India) to illustrate how the registration of certain objects under GIs have not only provided economic incentives to the locals but also facilitated the transmission of traditional skills within generations as well as ensured community identification and self-governance. However, it must be pointed out that while GI provides a label or reputation to a product, it is not able to protect the knowledge base and the skill sets used in manufacturing the goods. Finally, it has been concluded that even though GI may not replace TK completely, the former should be treated as an important complement to the latter in ensuring adequate protection. In fact, GI law must be made more robust in consonance with the overall intellectual property rights regime.

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Geographical Indications, Traditional Knowledge, TRIPS Agreement, Intellectual Property Rights, Biopiracy, Cultural Heritage, GI Act 1999, Muga Silk, Assam, Sui Generis Protection

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Creative Commons Attribution 4.0 and The Open Definition

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This paper examines whether Plato's theory of class, as presented in The Republic, can be used to justify the Indian caste system. Plato divides society into three classes--rulers, auxiliaries, and producers--based on natural ability and the principle of justice, where each person performs the role best suited to their nature. Similarly, the Indian caste system (varna system) also organizes society into hierarchical groups with specific duties. At a superficial level, both systems appear similar because they emphasize division of labour and social order. However, this paper argues that the philosophical foundations of the two are significantly different. Plato's model allows for mobility based on merit and education, whereas the caste system is largely hereditary and rigid. Moreover, Plato's idea of justice is rooted in harmony and efficiency, while the caste system has historically been associated with social inequality and exclusion. By revisiting Plato in the Indian context, this study critically evaluates whether his class theory can genuinely justify caste. It concludes that although there are structural similarities, Plato's theory cannot be used to defend the caste system in its traditional form.

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Key Words: Ambedkar,caste, class, justice, Plato

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Rabindranath Tagore, India's most celebrated literary figure, was far more than a poet and novelist. He was a visionary thinker who championed women's empowerment in colonial India when such advocacy was revolutionary. This paper examines Tagore's philosophy on women's rights and development through an analysis of his writings. The paper traces the introduction of the concept of true liberation for women through education and independence, showing how Tagore worked toward challenging societal norms that confined women to domestic roles. It explores how Tagore envisioned building women's strength and identity beyond traditional expectations. The paper analyzes his concept of the new woman, educated, independent, and socially conscious, as a transformative force for Indian society. Through examination of notable quotes on women, the paper demonstrates how Tagore articulated a vision grounded in voices of strength and autonomy. The paper presents women's empowerment in the words of Rabindranath Tagore, showing his multifaceted approach to gender equality. The discussion section synthesizes Tagore's interconnected ideas about education, economic independence, and intellectual freedom. The paper concludes with suggestions that Tagore's nineteenth-century vision remains deeply relevant to contemporary struggles for women's empowerment.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Rabindranath Tagore, women's empowerment, true liberation, education, challenging societal norms, gender equality.

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Creative Commons Attribution 4.0 and The Open Definition