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SVM, CNN and VGG16 Classifiers of Artificial Intelligence Used for the Detection of Diseases of Rice Crop: A Review

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Sentimental Analysis and Deep Learning

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1408))

Abstract

The production of grains plays a vital role for the economy of any country. Smart methods are required that accurately helps to increase the efficiency of the grain production. The use the techniques of the tools of artificial intelligence, deep learning and image processing are highlighted in this paper. These procedures achieve especially compelling results for the revelation of diseases using the photos of leaves, seeds or reap field. In this special circumstance, the work presents a review that centers around precision agriculture for the high formation of quite possibly the main reap in the world: RICE. In this paper, the classifiers SVM, CNN and VGG16 which utilizes the methods of Artificial Intelligence utilized for crop illness location, seedlings wellbeing, and grain quality are reviewed.

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Authors and Affiliations

  1. University Centre for Research and Development, Chandigarh University, Gharuan, Mohali, Punjab, 140413, India

    Amit Verma

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  1. Amit Verma
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Correspondence to Amit Verma .

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Editors and Affiliations

  1. Institute of Engineering, Tribhuvan University, Pulchowk Campus, Lalitpur, Nepal

    Subarna Shakya

  2. Intelligent Systems Research Centre, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Songkla University, Songkhla, Thailand

    Sinchai Kamolphiwong

  4. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Ke-Lin Du

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Verma, A. (2022). SVM, CNN and VGG16 Classifiers of Artificial Intelligence Used for the Detection of Diseases of Rice Crop: A Review. In: Shakya, S., Balas, V.E., Kamolphiwong, S., Du, KL. (eds) Sentimental Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1408. Springer, Singapore. https://doi.org/10.1007/978-981-16-5157-1_71

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