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Deep Learning in Agriculture whats happening Nathaniel Narra Prof. Tarmo Lipping Sigala group, Signal Processing Lab, TUT/Pori System of input and output: simplified Stimulus Response System of input and output: simplified

  1. Deep Learning in Agriculture – what’s happening Nathaniel Narra Prof. Tarmo Lipping Sigala group, Signal Processing Lab, TUT/Pori

  2. System of input and output: simplified Stimulus Response

  3. System of input and output: simplified Temperature Water Yield Solar radiation Stimulus Response Soil prop. ….

  4. Temperature Weather Solar radiation Precipitation Humidity … Irrigation Intervention Fertilizers Compost Herbicides … Soil type Mineral content (N,P,K,..) Soil properties Organic content Moisture …

  5. Temperature Weather Solar radiation Precipitation Humidity … Remote Sensing Artificial Intelligence Irrigation Intervention Fertilizers Compost Herbicides … Soil type Mineral content (N,P,K,..) Soil properties Organic content Moisture …

  6. Temperature Weather Solar radiation Precipitation Humidity … Remote Machine Learning Sensing Deep Learning CNN Irrigation Intervention Fertilizers Compost Herbicides … Soil type Mineral content (N,P,K,..) Soil properties Organic content Moisture …

  7. ("machine learning" OR "deep learning" OR "artificial intelligence" OR "neural network") AND ("agriculture")

  8. Artificial Intelligence Sensor Data Agronomy Methods Temperature Solar radiation Precipitation Humidity Agriculture information processing Machine Learning … Agriculture production system optimal control Irrigation Deep Learning Fertilizers + Remote sensing Compost image data Herbicides Smart agriculture machinery equipment CNN … (Convolutional Neural Networks) Agricultural economic system management Soil type Mineral content (N,P ,K,..) Organic content Moisture … https://granular.ag/farm-management-software/

  9. Artificial Intelligence Sensor Data Subject areas Methods Temperature Solar radiation Precipitation Humidity Plant Machine Learning … Animal Irrigation Deep Learning Fertilizers + Remote sensing Compost image data Herbicides Land CNN … (Convolutional Neural Networks) Mechanization Soil type Mineral content (N,P ,K,..) Organic content Moisture …

  10. Artificial Intelligence Sensor Data Subject areas Methods • Crop classification • Phenology recogn. Temperature Solar radiation Precipitation • Disease detection Humidity Remote sensing Plant Machine Learning … image data • Weed/pest detection • Hyperspectral • Fruit counting Animal Irrigation Deep Learning • Multi-spectral Fertilizers + • SAR Compost • Yield prediction • Infrared/Thermal Herbicides • LIDAR Land CNN … • NIR (Convolutional • Optical Neural Networks) • X-ray Mech. Soil type Mineral content (N,P ,K,..) Organic content Moisture …

  11. • Crop classification • Phenology recogn. • Disease detection Kussul et al. 2017; DOI: 10.1109/JSTARS.2016.2560141 • Weed/pest detection • Fruit counting • Yield prediction Rebetez et al. 2016; ISBN: 978-287587027-8

  12. Cotton Pepper Corn • Crop classification • Phenology recogn. • Disease detection • Weed/pest detection • Fruit counting • Yield prediction Yalcin, Hulya . “Plant phenology recognition using deep learning: Deep-Pheno .” 2017 6th International Conference on Agro-Geoinformatics (2017): 1-5.

  13. • Crop classification • Phenology recogn. • Disease detection • Weed/pest detection • Fruit counting • Yield prediction accuracy of 99.35% Mohanty et al. 2016; DOI: 10.3389/fpls.2016.01419

  14. • Crop classification • Phenology recogn. • Disease detection • Weed/pest detection Dyrmann et al. 2017; DOI: 10.1017/S2040470017000206 • Fruit counting • Yield prediction McCool et al. 2017; DOI: 10.1109/LRA.2017.2667039

  15. Chen et al. 2017; DOI: 10.1109/LRA.2017.2651944 • Crop classification • Phenology recogn. • Disease detection • Weed/pest detection • Fruit counting • Yield prediction Bargoti & Underwood 2016; arXiv:1610.03677v2

  16. What next? “…one key shortcoming: no major company has really delivered on the promise of  facilitating better in-season decision-making .” (Barclay Rogers, agfundernews, Sep 2018) The next big wave in agtech will be better in-season decision-making, including:   Directing resource allocation based upon on actual field performance  Informing in-season fertilizer applications  Detecting pest and disease pressure  Evaluating product performance  Guiding irrigation decisions  Forecasting field-level yields  Providing better management zones https://agfundernews.com/whats-next-for-agtech.html/

  17. Future? Hyperspectral  imaging : greater source of data for analysis Drone tech  Crop models: AI  methods Databases and  decision making? https://agfundernews.com/growing-impact-hyperspectral-imagery-agrifood-tech.html/ VTT creates the world's first hyperspectral iPhone camera https://phys.org/news/2016-11-vtt-world-hyperspectral-iphone-camera.html

  18. Artificial Intelligence Sensor Data Subject areas Methods • Crop classification • Phenology recogn. Temperature Solar radiation Precipitation • Disease detection Humidity Remote sensing Plant Machine Learning … image data • Weed/pest detection • Hyperspectral • Fruit counting Animal Irrigation Deep Learning • Multi-spectral Fertilizers + • SAR Compost • Yield prediction • Infrared/Thermal Herbicides • LIDAR Land CNN … • NIR (Convolutional • Optical Neural Networks) • X-ray Mech. Soil type Mineral content (N,P ,K,..) Organic content Moisture Impedance? … MIKÄ-DATA context

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