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Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Related Experiment Video

Updated: Jun 11, 2025

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
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Image Processing for Smart Agriculture Applications Using Cloud-Fog Computing.

Dušan Marković1, Zoran Stamenković2,3, Borislav Đorđević4

  • 1Faculty of Agronomy in Čačak, University of Kragujevac, Cara Dušana 34, 32102 Čačak, Serbia.

Sensors (Basel, Switzerland)
|September 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning model for smart agriculture, optimizing image classification on edge devices. This enhances early problem detection and resource management, improving efficiency and reducing costs in crop monitoring.

Keywords:
agriculture applicationcloud-fog computingdeep learningimage classification

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Area of Science:

  • Agricultural Technology
  • Computer Science
  • Artificial Intelligence

Background:

  • The Internet of Things (IoT) generates vast data, necessitating advanced analytical solutions, particularly in smart agriculture for crop monitoring.
  • Continuous monitoring of crop growth aids in timely interventions for disease, weed, and pest control, boosting agricultural productivity and sustainability.
  • Image analysis using Convolutional Neural Networks (CNNs) offers significant potential for enhancing decision-making systems in smart agriculture.

Purpose of the Study:

  • To develop a deep learning model for image classification optimized for resource-constrained Fog computing devices.
  • To enable early problem detection and optimize resource management in smart agriculture through efficient image processing.
  • To reduce agricultural operating costs and manual labor by leveraging edge and fog computing for data processing.

Main Methods:

  • Implementation of a Fog computing architecture connecting Cloud and Edge devices for data processing.
  • Development and optimization of a deep learning model for image classification suitable for hardware-limited devices.
  • Testing a tomato disease classification model on Field-Programmable Gate Arrays (FPGAs) to evaluate performance trade-offs.

Main Results:

  • The proposed solution effectively off-loads data processing to Edge and Fog devices, improving system responsiveness and reliability.
  • Significant reductions in data transmission and storage costs were achieved.
  • The optimized model for FPGA execution showed a minimal decrease in test accuracy (0.83%) for tomato disease classification, maintaining high performance (95.46%).

Conclusions:

  • The developed deep learning model is adaptable for implementation on resource-limited Fog computing devices, enhancing smart agriculture applications.
  • Optimizing image processing at the edge significantly improves system efficiency, reduces costs, and increases overall reliability and security.
  • The approach demonstrates a viable method for balancing model size and accuracy, crucial for deploying AI in edge environments for agriculture.