Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis

Yadav, Sargam; Kaushik, Abhishek; Sharma, Mahak; Sharma, Shubham

doi:10.3390/agriengineering4020029

Cited by 30 publications

(22 citation statements)

References 132 publications

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“…The motivation for this research is to build a model which can predict the disease on the tomato leaves in the early stages (Kulkarni et al, 2021). Farmers have limited awareness of the technologies and innovations in the agriculture field and they need exposure to new technological innovations (Yadav et al, 2022). Keeping this limitation in mind, the model presented in this study can be used by any farmer with minimum facilities and basic knowledge of the computer, and irrespective of location, region, and time.…”

Section: Introductionmentioning

confidence: 99%

Disruptive technologies for smart farming in developing countries: Tomato leaf disease recognition systems based on machine learning

Kovvuri

Kaushik

Yadav

2023

E J Info Sys Dev Countries

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Food security is a major concern in every developing country. Farmers face many problems while cultivating plants and they must take precautions at every stage of cultivation. Plants get diseases for various reasons like bacteria, insects, and fungus. Some diseases can be detected by examining the symptoms on the leaves. Early detection of diseases is a major concern and may require a thorough examination of the plants by an agricultural professional. This process is expensive and time taking. Machine learning (ML) algorithms help in image recognition and can be used to detect diseases on time without the need of an agricultural professional. In this project, the diseases in tomato leaves will be detected using image processing. The data from the images are extracted using different vectorization methods and classification algorithms like logistic regression (LR), support vector machine (SVM), and k‐nearest neighbors (KNN). Vectors of size 32 × 32 and 64 × 64 are used for training with normalizer scaling and no scaling. Out of the different approaches that were explored, SVM with the radial basis function (RBF) kernel gives the highest accuracy of 85% with no scaling and 64 × 64 image dimension.

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Section: Introductionmentioning

confidence: 99%

Disruptive technologies for smart farming in developing countries: Tomato leaf disease recognition systems based on machine learning

Kovvuri

Kaushik

Yadav

2023

E J Info Sys Dev Countries

Self Cite

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“…The knowledge and training of farmers in using technology effectively, data analysis, data storage, the power needs of IoT devices, and implementation costs are major issues that demand attention [35], [36]. Only 1% of the articles have discussed about using a platform powered by ML/AI to support farmer training and awareness.…”

Section: Review Discussionmentioning

confidence: 99%

Study of applications of Internet of Things and Machine Learning for Smart Agriculture

Gupta

Malhotra

Kumar

2022

2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)

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The concept of "smart agriculture" relies on the integration of sensors within an Internet of Things (IoT) network. Machine learning (ML) algorithms are integrated at various levels of the IoT system design to augment its functionality and enhance its capability. This article is a bibliometric review of 42 articles published between 2018 and 2022 using the Web of Science database. The results of the review showed an exponential growth in the use of ML algorithms in IoT systems for different agriculture applications. Additionally, two key research questions are addressed in this article, one being the development of IoT-ML-enabled smart agriculture over the past five years, and the second being the main research gaps for applications of machine learning and IoT in smart agriculture. The article concludes with a discussion of the results and future directions for research in the field.

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“…DLF has the potential to be disruptive in management (Yadav, et al, 2022). Now, disruptive does not necessarily mean that technology is negative.…”

Section: Disruption and Critical Analysismentioning

confidence: 99%

The Significance and Ethics of Digital Livestock Farming

Neethirajan¹

2023

Preprint

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The emergence of precision and digital livestock farming presents an opportunity for sustainable animal farming practices that enhance animal welfare and health. However, this transformation of modern animal farming through digital technology has several implications for the technological, social, economic, and environmental aspects of farming. It is crucial to analyze the ethical considerations associated with the digitalization of modern animal farming, particularly in the context of human-animal relationships and potential objectification. This analysis can help develop frameworks for improving animal welfare and promoting sustainability in animal farming.One of the primary ethical concerns of digital livestock farming is the potential for a digital divide between farmers who have access to advanced technologies and those who do not. This could lead to a disparity in animal welfare and health outcomes for different groups of animals. Additionally, the use of artificial intelligence in digital livestock farming may lead to a loss of personal connection between farmers and animals, which could impact the animal's well-being. Another ethical concern of digital livestock farming is the potential for objectification of animals as mere data points. The use of sensors and other monitoring technologies can provide valuable data on animal health and behavior, but it is important to remember that animals are sentient beings with complex emotional and social needs. The use of digital technologies should not lead to neglect of animal welfare or a lack of human responsibility towards animals.Furthermore, social context becomes essential while integrating technologies in the livestock farming to overcome ethics. By considering the cultural and societal norms of different communities, we can ensure that the use of digital technologies does not undermine these values. To address these ethical challenges, the development of standards and codes of conduct for the adoption and implementation of digital livestock farming tools and platforms can help ensure that animal welfare and sustainability are prioritized. This can help alleviate the privacy concerns of stakeholders and improve sustainability in animal farming practices. Additionally, the use of virtual and augmented reality technologies can provide a way to enhance human-animal interactions and provide more personalized care to animals, further promoting animal welfare.

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Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis

Cited by 30 publications

References 132 publications

Disruptive technologies for smart farming in developing countries: Tomato leaf disease recognition systems based on machine learning

Disruptive technologies for smart farming in developing countries: Tomato leaf disease recognition systems based on machine learning

Study of applications of Internet of Things and Machine Learning for Smart Agriculture

The Significance and Ethics of Digital Livestock Farming

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