Integrating artificial intelligence (AI) with drones has emerged as a promising paradigm for advancing agriculture. This bibliometric analysis investigates the current state of research in this transformative domain by comprehensively reviewing 234 pertinent articles from Scopus and Web of Science databases. The problem involves harnessing AI-driven drones' potential to address agricultural challenges effectively. To address this, we conducted a bibliometric review, looking at critical components, such as prominent journals, co-authorship patterns across countries, highly cited articles, and the co-citation network of keywords. Our findings underscore a growing interest in using AI-integrated drones to revolutionize various agricultural practices. Noteworthy applications include crop monitoring, precision agriculture, and environmental sensing, indicative of the field’s transformative capacity. This pioneering bibliometric study presents a comprehensive synthesis of the dynamic research landscape, signifying the first extensive exploration of AI and drones in agriculture. The identified knowledge gaps point to future research opportunities, fostering the adoption and implementation of these technologies for sustainable farming practices and resource optimization. Our analysis provides essential insights for researchers and practitioners, laying the groundwork for steering agricultural advancements toward an enhanced efficiency and innovation era.

The traditional methods used to identify plant diseases mostly rely on expert opinion, which causes long waits and enormous expenses in the control of crop diseases and field activities, especially given that the majority of crop infections now in existence have tiny targets, occlusions, and looks that are similar to those of other diseases. To increase the efficiency and precision of rust disease classification in a fava bean field, a new optimized multilayer deep learning model called YOLOv8 is suggested in this study. 3296 images were collected from a farm in eastern Morocco for the fava bean rust disease dataset. We labeled all the data before training, evaluating, and testing our model. The results demonstrate that the model developed using transfer learning has a higher recognition precision than the other models, reaching 95.1%, and can classify and identify diseases into three severity levels: healthy, moderate, and critical. As performance indicators, the needed standards for mean Average Precision (mAP), recall, and F1 score are 93.7%, 90.3%, and 92%, respectively. The improved model's detection speed was 10.1 ms, sufficient for real-time detection. This study is the first to employ a new method to find rust in fava bean crops. Results are encouraging and supply new opportunities for crop disease research.

This article investigates the integration of the Internet of Things (IoT), digital twin (DT), and augmented reality (AR) technologies to advance digital services in the management, monitoring, and control of agricultural greenhouses, addressing the sector's technological demands. By leveraging advanced technologies, this research aims to enhance efficiency across various fields, including industry, logistics, and commerce. The core objective is to develop a prototype IoT system, incorporating DT and AR, to create a digital transformation environment. This system provides real-time visualization and monitoring of sensor data from physical IoT devices, enabling user interaction and control through microcontroller-based sensor nodes installed in the greenhouse. Data transmission is achieved using the MQTT protocol, while an AR application facilitates greenhouse visualization and control. The findings demonstrate the system's effectiveness in enabling real-time communication between IoT devices and the AR application, with users successfully performing tasks without prior experience, thereby validating the system's usability and efficiency.

Real-time monitoring of crop growth has become indispensable in modern agriculture, facilitating prompt detection of crop stress, diseases, and nutrient deficiencies by farmers. This study investigates the feasibility of leveraging unmanned aerial vehicles (UAVs) and deep learning algorithms for the real-time monitoring of Vicia faba L. crop growth stages, aimed at informing decisions related to irrigation, fertilization, and pest management. The study introduces a cutting-edge deep learning model tailored for accurate real-time monitoring of diverse growth stages based on neural architecture search (NAS). This model is benchmarked against seven other rigorously trained models using a diverse dataset of 2530 UAV-captured images, encompassing varied and complex lighting and background conditions. We meticulously fine-tuned the training parameters, closely examining and comparing the substantial performance of each model. Notably, the NAS-based architecture model proved outstanding results, achieving a precision rate of 95.80%, a recall rate of 98.80%, and a mAP@0.5_0.95 value of 71.30%. It strikes an optimal balance between precision, speed, and model size compared to alternative neural network models. The mean average precision (mAP) stands at 95.50%, and it maintains a refresh rate of 24.8 frames per second (FPS), all within a compact model size of 256 megabytes (MB). This chosen model achieves an impressive inference speed of 40.32 milliseconds per frame during testing with new images. This performance is underpinned by the current technology of the NVIDIA Quadro P1000, recognized for its high performance and significant pipelines/CUDA cores.

Integrating convolutional neural networks (CNNs) with the Internet of Things (IoT) is paramount in agriculture, particularly greenhouses. By leveraging IoT capabilities, operators can collect agro-environmental information inside the greenhouse based on installed sensor nodes. This data-driven approach minimizes water, fertilizer, and energy waste. Simul-taneously, CNNs enhance the monitoring systems by facilitating early detection and classification of crop diseases. Our research proposes a comprehensive solution: an online technology platform for intelligent greenhouses based on IoT and CNNs. This platform effectively collects environmental and physical variables and detects diseases in real time using image-based analysis. The results of our study demonstrate that the system architecture is a reliable IoT platform, leading to significant energy savings. Moreover, the disease identification accuracy and classification process achieved an impressive rate of over 98 %, ensuring the system's efficacy in identifying and categorizing diseases. Additionally, the system exhibits a recall rate of over 90 %, indicating its ability to identify and recall crop disease instances accurately.