Computer Applications and Artificial Intelligence in Agriculture

Agriculture is the cornerstone industry of human society, directly influencing global food supply and security. However, with challenges such as growing global population and climate change, traditional agricultural production faces immense pressure. There is an urgent need for more efficient technologies and methods to boost agricultural productivity, improve yields and quality, and ensure the safety of agricultural products.

In this context, computer science and artificial intelligence (AI) have emerged as critical solutions to agricultural challenges. Through smart sensors, agricultural Internet of Things (IoT), and monitoring technologies, real-time tracking of crop growth conditions enables farmers to optimize irrigation, fertilization, and other practices. By leveraging spectral technology and image recognition, machine learning algorithms can swiftly identify pest and disease issues in fields, allowing timely interventions to protect crop health. Drones equipped with high-resolution cameras and multispectral sensors rapidly collect farmland data, including soil quality, vegetation growth status, and pest distribution. Autonomous tractors perform tasks such as plowing, planting, and harvesting with precision, improving crop yields and quality. The collection, integration, and analysis of agricultural big data enable smarter, more efficient, and data-driven farming practices, empowering farmers to refine cultivation strategies and reduce production costs. Additionally, AI technologies can be applied to the quality inspection of agricultural products. Intelligent detection systems automatically analyze characteristics such as appearance, size, and color, comparing them against standardized criteria to determine product quality grades. This ensures consistent safety and compliance with market standards for agricultural outputs.

This section “Computer Applications and Artificial Intelligence in Agriculture” publishes articles, technical notes, reviews, commentaries, and case or field reports presenting new developments and significant contributions. These include agricultural information sensing technology, spectral- and image-based pests and disease diagnosis methods, agricultural drone technology, intelligent agricultural machinery equipment, agricultural big data analysis methods, quality inspection of agricultural products, as well as case studies applying computer and artificial intelligence in agriculture across various spatial scales.

Related topics of interest: agricultural sensors and agricultural Internet of Things; image and spectral detection technologies; remote sensing applications; machine learning; artificial intelligence; agricultural drones; intelligent agricultural machinery equipment; agricultural big data; quality inspection of agricultural products; digital agriculture; precision agriculture