Sensors International 5 (2024) 100292 Available online 3 August 2024 2666-3511/© 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Integrating artificial intelligence and Internet of Things (IoT) for enhanced crop monitoring and management in precision agriculture Kushagra Sharma a , Shiv Kumar Shivandu b, * a Department of Horticulture (Fruit Science), Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur, Madhya Pradesh, 482004, India b Department of Horticulture (Fruit Science), College of Horticulture, Dr YS Parmar University of Horticulture and Forestry, Nauni, Solan, Himachal Pradesh, 173230, India ARTICLE INFO Keywords: Precision agriculture Artificial Intelligence (AI) Internet of Things (IoT) Crop monitoring Smart farming 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 detec- tion, 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 back- grounds. 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 pri- vacy 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. 1. Introduction Precision agriculture entails utilizing information and communica- tion technology (ICT) to manage the spatial and temporal variability of fields. By assessing these variations in field and crop characteristics, areas with similar traits, known as management zones, can be identified. Professor Pierre C. Robert, is often considered pioneer of precision farming, described it as an information revolution facilitated by new technologies. He highlighted that precision agriculture transcends merely adopting new technologies; it establishes a more precise and sophisticated farm management system [1]. In the years ahead, food production needs to rise substantially to satisfy the demands of a global population projected to reach 9.7 billion by mid-century [2]. To address field variability, a range of technologies has been developed, utilizing ground-based sensing systems. These systems include mobile platforms and tractors equipped with cameras, ultrasonic sensors (sonar), and light detection and ranging (LiDAR) sensors, also known as laser sensors. The large-scale mechanization of agriculture in the twentieth cen- tury replaced labor with machinery, boosting land productivity and achieving economies of scale. Precision farming holds significant promise for increasing farmer incomes, enhancing both the extrinsic and intrinsic quality of agricultural products, and reducing the adverse environmental effects of farming [3]. Although not a cure-all, precision farming can significantly contribute to more sustainable agricultural practices. The ongoing fourth industrial revolution is revolutionizing agriculture, emerging in the era of Agriculture 4.0. This new phase is defined by data-driven management, innovative tool-based production, sustainability, professionalization, and a minimized environmental * Corresponding author. E-mail address: marginshiv05@gmail.com (S.K. Shivandu). Contents lists available at ScienceDirect Sensors International journal homepage: www.keaipublishing.com/en/journals/sensors-international https://doi.org/10.1016/j.sintl.2024.100292 Received 1 July 2024; Accepted 3 August 2024