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A Review of CNN Applications in Smart Agriculture Using Multimodal Data.

Mohammad El Sakka^1,2, Mihai Ivanovici³, Lotfi Chaari^1,4

¹Institut de Recherche en Informatique de Toulouse, IRIT UMR5505 CNRS, 31400 Toulouse, France.

Sensors (Basel, Switzerland)

|January 25, 2025

Summary

Convolutional Neural Networks (CNNs) revolutionize smart agriculture by enhancing weed and disease detection, crop classification, and yield prediction. These AI tools are crucial for sustainable farming and meeting global food security demands.

Keywords:

convolutional neural network crop classification crop disease detection machine learning remote sensing smart agriculture water management weed detection yield prediction

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Area of Science:

Agricultural Science
Computer Science
Artificial Intelligence

Background:

Smart agriculture integrates technology to optimize farm efficiency, aligning with Agriculture 5.0 principles.
Convolutional Neural Networks (CNNs) are increasingly vital for analyzing agricultural data.
Existing literature reviews provide a foundation for understanding CNN applications in agriculture.

Purpose of the Study:

To review and analyze the applications of Convolutional Neural Networks (CNNs) in smart agriculture.
To contextualize CNNs within the framework of Agriculture 5.0.
To identify current advancements, methodologies, and future research directions for CNNs in agriculture.

Main Methods:

Comprehensive literature review of over 115 recent studies on CNN applications in agriculture.
Bibliometric analysis of the broader research landscape.
Analysis of CNN approaches including image classification, segmentation, regression, and object detection.
Evaluation of diverse data types (RGB, multispectral, radar, thermal) and data sources (UAV, satellite).

Main Results:

CNNs demonstrate significant advancements in weed detection, disease detection, crop classification, water management, and yield prediction.
CNNs, particularly with UAV and satellite data, enable real-time, large-scale crop monitoring for advanced farm management.
Comparative analysis indicates CNNs outperform traditional machine learning and other deep learning models in processing high-dimensional or temporal agricultural data.

Conclusions:

Convolutional Neural Networks (CNNs) are pivotal tools for sustainable agriculture, addressing challenges like climate variability and food security.
Future research should focus on integrating IoT, cloud platforms, and large language models for enhanced data processing and regulatory insights.
Improving data accessibility and developing hybrid models are key to advancing CNN applications in agriculture.