Augmentation on Satellite Imagery with Information Integrated Farming

P, Vijaya Kumar B; N.K, Mahadev Mohit; Ranjith, M S Pawan; Nadig, Narendranath D; P, Nikita Menon K.

doi:10.1109/icecct.2019.8869021

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…Giudice et al[8] observed many Mobility aids or Electronic Travel Aids for blind people. They described 4 important factors important for implementation of technology aiding Blind Navigation : Sensory Translation Rules, Selection of Information, Device Operation, Form and Function.In[9] Vijaya Kumar B.P et al discusses extent of the tasks scope in horticulture business, stock administration offices and other farming partners. In[10] Naresh et al describes the primary accentuation of this work is to decide the ideal purpose of testing and impediments faced while choosing an ideal point for a test.…”

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confidence: 99%

A Low cost Augmented Reality system for Wide Area Indoor Navigation

Dosaya

Varshney

Parameshwarappa

et al. 2020

2020 International Conference on Decision Aid Sciences and Application (DASA)

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In today's world, there are a lot of outdoor navigation apps for visually challenged people, but there are none that can precisely tell a user's location inside a large structure. Indoor navigation is a complex task for the visually challenged as well as for the general public, especially in large structures like malls, airports, museums, factories, etc. Present solutions and technologies are not cost-effective as well as complex. Hence, we are proposing a low-cost model that uses Augmented Reality to place virtual anchors across a structure, so a person can navigate from one location to another with the help of these anchors. The model doesn't use technologies like GPS. Machine Learning, and Artificial Intelligence but here, the anchors placed are pervasive and persistent across the indoor environment for smooth navigation. Once placed, these virtual anchors remain at their location and can be used at any time by any person registered on our app. This model can be extended for the general public in any indoor space and also can be enhanced by gamification for better user interaction and retention. This model can also be extended to collaborate with the Aarogya Setu app, which can help us identify routes that go through spaces through which covid positive patients have passed which in turn helps us avoid those routes in real-time navigation.

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mentioning

confidence: 99%

A Low cost Augmented Reality system for Wide Area Indoor Navigation

Dosaya

Varshney

Parameshwarappa

et al. 2020

2020 International Conference on Decision Aid Sciences and Application (DASA)

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Irrigation with Artificial Intelligence: Problems, Premises, Promises

Wei,

Xu,

Kang

et al. 2024

Hum-Cent Intell Syst

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Protagonists allege that artificial intelligence (AI) is revolutionising contemporaneous mindscapes. Here, we authoritatively review the status quo of AI and machine learning application in irrigated agriculture, evaluating the potential of, and challenges associated with, a wide range of existential AI approaches. We contend that aspiring developers of AI irrigation systems may benefit from human-centred AI, a nascent algorithm that captures diverse end-user views, behaviours and actions, potentially facilitating refinement of proposed systems through iterative stakeholder feedback. AI-guided human–machine collaboration can streamline integration of user needs, allowing customisation towards situational farm management adaptation. Presentation of big data in intuitive, legible and actionable forms for specialists and laypeople also urgently requires attention: here, AI-explainable interpretability may help harness human expertise, enabling end-users to contribute their experience within an AI pipeline for bespoke outputs. Transfer learning holds promise in contextualising place-based AI to agroecological regions, production systems or enterprise mixes, even with limited data inputs. We find that the rate of AI scientific and software development in recent times has outpaced the evolution of adequate legal and institutional regulations, and often social, moral and ethical license to operate, revealing consumer issues associated with data ownership, legitimacy and trust. We opine that AI has great potential to elicit sustainable outcomes in food security, social innovation and environmental stewardship, albeit such potential is more likely to be realised through concurrent development of appropriate ethical, moral and legal dimensions.

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A fusion approach using GIS, green area detection, weather API and GPT for satellite image based fertile land discovery and crop suitability

Balasundaram,

Abdul Aziz,

Gupta

et al. 2024

Sci Rep

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Proper utilization of agricultural land is a big challenge as they often laid over as waste lands. Farming is a significant occupation in any country and improving it further by promoting more farming opportunities will take the country towards making a huge leap forward. The issue in achieving this would be the lack of knowledge of cultivable land for food crops. The objective of this work is to utilize modern computer vision technology to identify and map cultivable land for agricultural needs. With increasing population and demand for food, improving the farming sector is crucial. However, the challenge lies in the lack of suitable land for food crops cultivation. To tackle this issue, we propose to use sophisticated image processing techniques on satellite images of the land to determine the regions that are capable of growing food crops. The solution architecture includes enhancement of satellite imagery using sophisticated pan sharpening techniques, notably the Brovey transformation, aiming to transform dull satellite images into sharper versions, thereby improving the overall quality and interpretability of the visual data. Making use of the weather data on the location observed and taking into factors like the soil moisture, weather, humidity, wind, sunlight times and so on, this data is fed into a generative pre-trained transformer model which makes use of it and gives a set of crops that are suitable to be grown on this piece of land under the said conditions. The results obtained by the proposed fusion approach is compared with the dataset provided by the government for different states in India and the performance was measured. We achieved an accuracy of 80% considering the crop suggested by our model and the predominant crop of the region. Also, the classification report detailing the performance of the proposed model is presented.

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Augmentation on Satellite Imagery with Information Integrated Farming

Cited by 5 publications

References 2 publications

A Low cost Augmented Reality system for Wide Area Indoor Navigation

A Low cost Augmented Reality system for Wide Area Indoor Navigation

Irrigation with Artificial Intelligence: Problems, Premises, Promises

A fusion approach using GIS, green area detection, weather API and GPT for satellite image based fertile land discovery and crop suitability

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