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A Self-Predictable Crop Yield Platform (SCYP) Based On Crop Diseases Using Deep Learning

Author

Listed:
  • SangSik Lee

    (Department of Biomedical Engineering, Catholic Kwandong University, Gangneung 25601, Korea)

  • YiNa Jeong

    (Department of Computer Engineering, Catholic Kwandong University, Gangneung 25601, Korea)

  • SuRak Son

    (Department of Computer Engineering, Catholic Kwandong University, Gangneung 25601, Korea)

  • ByungKwan Lee

    (Department of Computer Engineering, Catholic Kwandong University, Gangneung 25601, Korea)

Abstract

This paper proposes a self-predictable crop yield platform (SCYP) based on crop diseases using deep learning that collects weather information (temperature, humidity, sunshine, precipitation, etc.) and farm status information (harvest date, disease information, crop status, ground temperature, etc.), diagnoses crop diseases by using convolutional neural network (CNN), and predicts crop yield based on factors such as climate change, crop diseases, and others by using artificial neural network (ANN). The SCYP consists of an image preprocessing module (IPM) to determine crop diseases through the Google Vision API and image resizing, a crop disease diagnosis module (CDDM) based on CNN to diagnose the types and extent of crop diseases through photographs, and a crop yield prediction module (CYPM) based on ANN by using information of crop diseases, remaining time until harvest (based on the date), current temperature, humidity and precipitation (amount of snowfall) in the area, sunshine amount, ground temperature, atmospheric pressure, moisture evaporation in the ground, etc. Four experiments were conducted to verify the efficiency of the SCYP. In the CDMM, the accuracy and operation time of each model were measured using three neural network models: CNN, region-CNN(R-CNN), and you only look once (YOLO). In the CYPM, rectified linear unit (ReLU), Sigmoid, and Step activation functions were compared to measure ANN accuracy. The accuracy of CNN was about 3.5% higher than that of R-CNN and about 5.4% higher than that of YOLO. The operation time of CNN was about 37 s less than that of R-CNN and about 72 s less than that of YOLO. The CDDM had slightly less operation time, but in this paper, we prefer accuracy over operation time to diagnose crop diseases efficiently and accurately. When the activation function of the ANN used in the CYPM was ReLU, the accuracy of the ANN was 2% higher than that of Sigmoid and 7% higher than that of Step. The CYPM prediction was about 34% more accurate when using multiple diseases than when not using them. Therefore, the SCYP can predict farm yields more accurately than traditional methods.

Suggested Citation

  • SangSik Lee & YiNa Jeong & SuRak Son & ByungKwan Lee, 2019. "A Self-Predictable Crop Yield Platform (SCYP) Based On Crop Diseases Using Deep Learning," Sustainability, MDPI, vol. 11(13), pages 1-21, July.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:13:p:3637-:d:245004
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    References listed on IDEAS

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    1. Joshua Sikhu Okonya & Walter Ocimati & Anastase Nduwayezu & Déo Kantungeko & Nicolas Niko & Guy Blomme & James Peter Legg & Jürgen Kroschel, 2019. "Farmer Reported Pest and Disease Impacts on Root, Tuber, and Banana Crops and Livelihoods in Rwanda and Burundi," Sustainability, MDPI, vol. 11(6), pages 1-20, March.
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    Cited by:

    1. Siraj Osman Omer, 2021. "Application of Bayesian Networks of Genotype by Environment Interaction Evaluation Under Plant Disease, Soil Types and Climate Condition-using Bayesia Lab," Academic Journal of Applied Mathematical Sciences, Academic Research Publishing Group, vol. 7(3), pages 158-166, 07-2021.
    2. Mohsen Niazian & Gniewko Niedbała, 2020. "Machine Learning for Plant Breeding and Biotechnology," Agriculture, MDPI, vol. 10(10), pages 1-23, September.
    3. Chen, Chi-Hua & Song, Fangying & Hwang, Feng-Jang & Wu, Ling, 2020. "A probability density function generator based on neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).

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