Application and Research of Computer Intelligent Technology in Modern Agricultural Machinery Equipment

Every country, including China, is deeply concerned and interested in the topic of agricultural machinery automation. The world’s population is growing at an astronomical rate, and as a result, the need of food is also growing at an astronomical rate. Farmers are forced to apply more toxic pesticide...

Full description

Saved in:
Bibliographic Details
Published in:Computational intelligence and neuroscience Vol. 2022; pp. 1 - 11
Main Authors: Zhou, Hongyu, Liu, Jinqi, Huang, Fan
Format: Journal Article
Language:English
Published: United States Hindawi 09.08.2022
John Wiley & Sons, Inc
Subjects:
Agricultural equipment
Agricultural equipment and supplies industry
Agricultural land
Agricultural practices
Agricultural production
Agriculture
Agriculture - methods
Algorithms
Artificial intelligence
Artificial neural networks
Automation
Barren lands
China
Computers
Feature extraction
Fuzzy logic
Internet of Things
Irrigation
Machine learning
Neural networks
Optimization
Pesticides
Pesticides - analysis
Plant diseases
Principal components analysis
Technology
Technology application
Water management
Wireless communications
China
Beijing China
Mongolia
ISSN:1687-5265, 1687-5273, 1687-5273
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Every country, including China, is deeply concerned and interested in the topic of agricultural machinery automation. The world’s population is growing at an astronomical rate, and as a result, the need of food is also growing at an astronomical rate. Farmers are forced to apply more toxic pesticides since traditional methods are not up to the task of meeting the rising demand. This has a major impact on agricultural practices, and in the long run, the land becomes barren and unproductive. Intelligent technologies such as Internet of Things, wireless communication, and machine learning can help with crop disease and pesticide storage management, as well as water management and irrigation. In this paper, we design and analyze an intelligent system that automatically predicts the agricultural land features for irrigation purpose. Initially, the dataset is collected and preprocessed using normalization. The features are extracted using principal component analysis (PCA). For automatic prediction by the equipment, we propose heterogeneous fuzzy-based artificial neural network (HF-ANN) with genetic quantum spider monkey optimization (GQ-SMO) algorithm. Analyses and comparisons are made between the proposed approach and current methodologies. The findings indicate the effectiveness of the proposed system.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ObjectType-Correction/Retraction-3
Academic Editor: Vijay Kumar
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2022/9978167