Integrating artificial intelligence and Internet of Things (IoT) for enhanced crop monitoring and management in precision agriculture

被引：5
作者：
Sharma, Kushagra [1 ]
Shivandu, Shiv Kumar [2 ]
机构：
[1] Department of Horticulture (Fruit Science), Jawaharlal Nehru Krishi Vishwa Vidyalaya, Madhya Pradesh, Jabalpur
[2] Department of Horticulture (Fruit Science), College of Horticulture, Dr YS Parmar University of Horticulture and Forestry, Nauni, Himachal Pradesh, Solan
来源：
Sensors International | 2024年 / 5卷
基金：
美国国家科学基金会;
关键词：
Artificial Intelligence (AI); Crop monitoring; Internet of Things (IoT); Precision agriculture; Smart farming;
D O I：
10.1016/j.sintl.2024.100292
中图分类号：
学科分类号：
摘要：
The integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies is transforming precision agriculture by enhancing crop monitoring and management. This review explores cutting-edge methodologies and innovations in modern agriculture, including high-throughput phenotyping, remote sensing, and automated agricultural robots (AgroBots). These technologies automate tasks such as harvesting, sorting, and weed detection, significantly reducing labor costs and environmental impacts. High-throughput phenotyping leverages remote sensing, spectral imaging, and robotics to collect data on plant traits, enabling informed decisions on fertilization, irrigation, and pest management. DGPS and remote sensing offer precise, real-time data essential for soil condition assessment and crop health monitoring. Advanced image segmentation techniques ensure accurate detection of plants and fruits, overcoming challenges posed by varying lighting conditions and complex backgrounds. Case studies like the PACMAN SCRI project for apple crop load management and Project PANTHEON's SCADA system for hazelnut orchard management demonstrate the transformative potential of AI and IoT in optimizing agricultural practices. The upcoming integration of 5G and future 6G mobile networks promises to address connectivity challenges, promoting the widespread adoption of smart agricultural practices. However, several research gaps remain. Integrating diverse datasets, ensuring scalability for small and medium-sized farms, and enhancing real-time decision-making need further investigation. Developing robust AI models and IoT devices for varied agricultural conditions, creating user-friendly interfaces for farmers, and addressing privacy and security concerns are essential. Addressing these gaps can enhance the effectiveness and adoption of AI and IoT in precision agriculture, leading to more sustainable and productive farming practices. © 2024 The Authors
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