This paper explores the potential of smart crop management based on the incorporation of tools like digital agriculture, which considers current technological tools applied in agriculture, such as the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), to improve crop production efficiency and sustainability. This is essential in the context of varying climatic conditions that affect the availability of resources for agriculture. The integration of tools such as IoT and sensor networks can allow farmers to obtain real-time data on their crops, assessing key health factors, such as soil conditions, plant water status, presence of pests, and environmental factors, among others, which can finally result in data-based decision-making to optimize irrigation, fertilization, and pest control. Also, this can be enhanced by incorporating tools such as drones and unmanned aerial vehicles (UAVs), which can increase monitoring capabilities through comprehensive field surveys and high-precision crop growth tracking. On the other hand, big data analytics and AI are crucial in analyzing extensive datasets to uncover patterns and trends and provide valuable insights for improving agricultural practices. This paper highlights the key technological advancements and applications in smart crop management, addressing challenges and barriers to the global adoption of these current and new types of technologies and emphasizing the need for ongoing research and collaboration to achieve sustainable and efficient crop production. 

Transformative Technologies in Digital Agriculture: Leveraging Internet of Things, Remote Sensing, and Artificial Intelligence for Smart Crop Management

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy.

pdf/towards-artificial-general-intelligence-agi-in-the-internet-50tgf9aj4k.pdf

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Irrigation plays a crucial role in maize cultivation, as watering is essential for optimizing crop yield and quality, particularly given maize’s sensitivity to soil moisture variations. In the current study, a hybrid Long Short-Term Memory (LSTM) approach is presented aiming to predict irrigation scheduling in maize fields in Bursa, Turkey. A critical aspect of the study was the use of the Aquacrop 7.0 model to simulate soil moisture content (MC) data due to data limitations in the investigated fields. This simulation model, developed by the Food and Agriculture Organization (FAO), helped overcome gaps in soil sensor data, enhancing the LSTM model’s predictions. The LSTM model was trained and tuned using a combination of soil, weather, and satellite-based plant vegetation data in order to predict soil moisture content (MC) reductions. The study’s results indicated that the LSTM model, supported by Aquacrop 7.0 simulations, was effective in predicting MC reduction across various time phases of the maize growing season, attaining R2 values ranging from 0.8163 to 0.9181 for Field 1 and from 0.7602 to 0.8417 for Field 2, demonstrating the potential of this approach for precise and efficient agricultural irrigation practices. 

A Hybrid LSTM Approach for Irrigation Scheduling in Maize Crop

DC microgrid technology provides a promising alternative to the currently existing AC microgrids, owing to ease of integration of renewable energy sources, lesser number of grid variables in need of control, and the rapid developments in DC energy storage technologies. The availibility of DC lighting loads, DC motors, and high efficiency DC-AC converters for high power applications make a strong case for the implementation of DC microgrids. This paper proposes a DC microgrid system, and a control algorithm for operating the aforementioned microgrid in Hybrid Mode. The microgrid integrates a PV array with MPP tracking, AC grid interfacing, a Battery Energy Storage System with CC-CV charging and cell balancing using a proposed cell balancing algorithm. Each component is studied and designed individually, and finally all are integrated to form the DC microgrid, whose operation in Hybrid Mode is demonstrated through simulated results.

Design and Development of Control Algorithm for a DC Microgrid System

Precision agriculture is a crucial field that addresses food security for the growing population. This article presents an innovative approach merging deep learning with a 5G Private Mobile Network (PMN) to construct a smart agriculture system. We simulated a 5G PMN infrastructure using UERANSIM and Free5GC with the Quantum Key Distribution Function (QKDF) integration. The 5G connectivity enhances agricultural practices through real-time crop healthcare monitoring and reliable transmission of irrigation control commands. In addition to using 5G, our proposed system integrates a lightweight model and three YOLOv8 models for AI-powered irrigation and crop health monitoring. While the lightweight model automatically generates irrigation schedules based on current weather conditions and soil moisture levels, optimizing crop water needs, the three YOLOv8 models are trained to predict growth stage, health status, and nutritional status (nitrogen, phosphorus, and potassium). All deep learning models are optimized for lettuce and feature lightweight architecture for easy deployment on low-power devices. Evaluation on test datasets reveals promising accuracy scores: the irrigation schedule generation model achieves 73%, the growth stage model attains 93.8%, the health status model reaches 87%, and the nutritional status model obtains 83%. This research contributes to improving precision agriculture while providing practical solutions to enhance irrigation management and crop health monitoring. We have also developed a QKDF that is integrated with the 5G core network to improve the security of the proposed system by establishing secure session keys with a length of 256 bits.

A Novel 5G PMN-Driven Approach for AI-powered Irrigation and Crop Health Monitoring

Renewable systems of energy namely wind - energy resource and solar - energy resource are considered to replenish the energy demands in near future. As, wind - energy resource and solar - energy resources will be alternatively available which are non-polluting in nature, this energy combination produces greater impact on the load management system. The hybrid system requires the best controller which efficiently captures the generated power without spill off and balance the voltage sharing between the renewable energies of the system. A novel perturb and observe algorithm with single module controller is designed for highest power tracking. During the hybridization of the renewable energy resources system, DC voltage balance and controlling process for tracking power are the major criteria to be taken care. In order to have the better design analysis, a separate MPPT controller for each of the energy system and combined MPPT controller is designed for the case study. The simulations are carried out using Simulink platform environment. Each case results are analyzed and reveals that the Single P & O controller cope-up with the results equal to the separate P & O controller for wind energy source and solar energy. It is found clear that a single controller itself meets the necessity and reduce the circuit complexity and cost as well.

Isolated DC Microgrid Energy Management System using Perturb and Observe Algorithm

Lack of energy storage technology is a key worry in the period of increased global demand for electricity. The use of solar energy is on the rise and is more efficient at producing power. The utilization of solar energy has expanded to include home and commercial applications. An island mode is utilized in battery storage in off-grid power distribution systems. Off-grid power is a standalone mode that is used for household purposes in the power distribution system. The goal of my project is to create an off-grid power system in my house that stores energy in batteries. The nearby homes receive the generated electricity from the off-grid system in exchange for payment. As a result, electricity is distributed through a particular non-EB supply at the moment it is distributed through a nearby home in wireless mode. Using an Arduino with a GSM module in wireless mode. The quantity of energy used by the nearby homes is computed using the readings from the energy meters. For the outputs, the MATLAB/SIMULINK program is used to test the proposed system's functionality. The EB Distribution box uses sensors and control systems to supply power to the neighboring homes.

Smart Power-Sharing System for Dormant Domestic Consumers using Green Energy using Wireless Networks

Intelligent irrigation scheduling scheme based on deep bi-directional LSTM technique

IoT technology has sparked a revolution across various sectors of everyday life, including agriculture, industries, etc by imbuing everyday objects with intelligence. In agriculture, innovative technologies have been developed to address challenges related to crop production systems, encompassing chemical, physical, biological, and socioeconomic constraints. This paper focuses on the sensor technologies that prove invaluable to farmers to increase production. These sensors serve to monitor diverse parameters such as temperature, humidity, rainfall, nutrient levels, and so on. The sensors outlined in the paper encompass an array of functions, including providing insights into soil and air temperature, rainfall patterns, leaf moisture, chlorophyll content, wind direction, solar radiation, relative humidity, and atmospheric pressure and more. The proposed product has been tested in live agricultural fields, demonstrating an exceptional data feed accuracy of over 98%. The central premise of this paper lies in utilizing sensors to detect and relay information about pH levels, temperature, nitrogen, phosphorus, and potassium, rain detection crucial factors in effective crop management.

Agronomic Monitoring Module

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Isolated DC Microgrid Energy Management System using Perturb and Observe Algorithm

Smart Power-Sharing System for Dormant Domestic Consumers using Green Energy using Wireless Networks

Intelligent irrigation scheduling scheme based on deep bi-directional LSTM technique

Agronomic Monitoring Module