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

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Abstract

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.

整合人工智能和物联网 (IoT),加强精准农业中的作物监测和管理
人工智能(AI)和物联网(IoT)技术的融合正在通过加强作物监测和管理来改变精准农业。本综述探讨了现代农业的前沿方法和创新,包括高通量表型、遥感和自动化农业机器人(AgroBots)。这些技术实现了收割、分拣和杂草检测等任务的自动化,大大降低了劳动力成本和对环境的影响。高通量表型技术利用遥感、光谱成像和机器人技术收集植物性状数据,从而在施肥、灌溉和病虫害管理方面做出明智的决策。DGPS 和遥感技术可提供土壤条件评估和作物健康监测所需的精确、实时数据。先进的图像分割技术可确保准确检测植物和果实,克服不同光照条件和复杂背景带来的挑战。用于苹果作物负载管理的 PACMAN SCRI 项目和用于榛子果园管理的 PANTHEON 项目 SCADA 系统等案例研究表明,人工智能和物联网在优化农业实践方面具有变革潜力。即将整合的 5G 和未来的 6G 移动网络有望解决连接难题,促进智能农业实践的广泛采用。然而,仍存在一些研究空白。整合不同的数据集、确保中小型农场的可扩展性以及加强实时决策都需要进一步研究。针对不同的农业条件开发强大的人工智能模型和物联网设备、为农民创建用户友好型界面以及解决隐私和安全问题都至关重要。解决这些问题可以提高人工智能和物联网在精准农业中的有效性和采用率,从而实现更可持续、更高产的农业实践。
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