Introduction to deep learning in precision agriculture: Farm image feature detection using unmanned aerial vehicles through classification and optimization process of machine learning with convolution neural network

Ray Sheriff, Halimatu Sadiyah Abdellahi*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

With the increasing demand for maximizing efficiency in food production, technology can offer and perform farm operations for yield optimization. Modern agriculture can automate the entire process of cultivation from land preparation and preplanting to postharvest processes through data collection and processing. It implies that the analysis of big agricultural/farm data can be performed with the use of new information and communication technologies. This chapter applies precision agricultural technology with an unmanned aerial vehicle, embedded with optic and radiometric sensors, to obtain high spectral resolution images of a plantation’s status during a normal production/growth cycle. Then, the convolution neural network deep learning technique is employed to train images to develop an end-to-end multiclass classification system to determine the plant’s overall health status. Applying the pretrained model to the new images showed that the model was accurately able to predict any plant condition with an average of 99% accuracy.
Original languageEnglish
Title of host publicationDeep Learning for Sustainable Agriculture
EditorsRamesh Chandra Poonia, Vijander Singh, Soumya Ranjan Nayak
PublisherAcademic Press
Chapter3
Pages81-107
Number of pages26
ISBN (Electronic)978-0-323-85214-2
DOIs
Publication statusPublished - 14 Jan 2022
Externally publishedYes

Publication series

NameCognitive Data Science in Sustainable Computing
PublisherAcademic Press

Keywords

  • Convolutional Neural Network
  • Machine Learning
  • Deep Learning
  • Precision Agriculture
  • Data Processing

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