Smart Farming System Research Articles

From Reality to Virtuality: Revolutionizing Livestock Farming Through Digital Twins

The impacts of climate change on agricultural production are becoming more severe, leading to increased food insecurity. Adopting more progressive methodologies, like smart farming instead of conventional methods, is essential for enhancing production. Consequently, livestock production is swiftly evolving towards smart farming systems, propelled by rapid advancements in technology such as cloud computing, the Internet of Things, big data, machine learning, augmented reality, and robotics. A Digital Twin (DT), an aspect of cutting-edge digital agriculture technology, represents a virtual replica or model of any physical entity (physical twin) linked through real-time data exchange. A DT conceptually mirrors the state of its physical counterpart in real time and vice versa. DT adoption in the livestock sector remains in its early stages, revealing a knowledge gap in fully implementing DTs within livestock systems. DTs in livestock hold considerable promise for improving animal health, welfare, and productivity. This research provides an overview of the current landscape of digital transformation in the livestock sector, emphasizing applications in animal monitoring, environmental management, precision agriculture, and supply chain optimization. Our findings highlight the need for high-quality data, comprehensive data privacy measures, and integration across varied data sources to ensure accurate and effective DT implementation. Similarly, the study outlines their possible applications and effects on livestock and the challenges and limitations, including concerns about data privacy, the necessity for high-quality data to ensure accurate simulations and predictions, and the intricacies involved in integrating various data sources. Finally, the paper delves into the possibilities of digital twins in livestock, emphasizing potential paths for future research and progress.

Application of Extreme Machine Learning for Smart Agricultural Robots to Reduce Manoeuvering Adaptability Errors

The agricultural robots limit complicated manoeuvring jobs, reducing the need for human intervention. Automation and control of these robots are performed by observing and practising the crop type, regulations, and external weather conditions. This article proposes and discusses a Manoeuvering Adaptable Task Processing Model (MATPM). This model is designed to improve the adaptability and precision of agricultural robots coinciding with farming and climatic conditions. For this purpose, extreme machine learning is employed to learn, align, and respond to external conditions to improve precise manoeuvring. The external impacting features and their adaptability to the crop and climatic conditions are valued using the learning process. In this process, maximum adaptability is considered to improve precision agriculture. The tasks are then classified based on adaptability and executed sequentially. If an unpredictable impacting factor hinders a task, then the adaptability is paused from training and it improves the chances of training by using different completed and paused tasks from the previous manoeuvring processes. Therefore, precision in smart farming and robot system adaptability is ensured with fewer adaptability errors. From the comparative assessment, the proposed model improves adaptability by 8.71 % and precision by 11.44 %, with 8.96 % less adaptability error for various tasks/ day.

Daily Light Integral Maps for Agriculture Lighting Design in Spain

The Daily Light Integral (DLI) is a key metric to optimise plant cultivation, influence photosynthesis, morphology, yield, and nutritional quality. Our study focuses on (i) the creation of the spatiotemporal DLI map for a representative Mediterranean country, Spain, (ii) the presentation of the improved semi-automatic DLI mapping workflow, (iii) the challenge of the comparability of different DLI value scalings in global (continent) and local context (country). DLI maps provide essential insights into the spatial and temporal distribution of natural and artificial light, critical for optimizing agricultural practices and enhancing crop yields. Spain's diverse topography and climate create significant regional and seasonal variations in DLI values. Using data from SunTracker Technologies and global weather stations, we developed detailed DLI maps at 30-meter resolution. These maps reveal high DLI values in coastal and southern regions, especially in summer, and lower values in northern and interior regions during winter. Our findings emphasize the importance of tailored lighting strategies in agriculture, suggesting higher scaling (from 5 to 2 mol·m−2·d−1) resolution for European contexts. This research underpins the development of smart agricultural systems that both quantitatively and qualitatively regulate lighting conditions (supplementary light and shading management) to maximize crop productivity and sustainability. Integrating DLI maps with IoT and AI technologies offers predictive insights for precision agriculture, highlighting the need for advanced training to leverage these tools effectively.

Real-Time Soil Nutrient Monitoring Using NPK Sensors: Enhancing Precision Agriculture

Prediction of various parameters in the agriculture field using sensors is a significant topic nowadays. However, in many scenarios, the sensor data does not accurately detect the real parameter(/s) in the agriculture field. The sensor data may vary due to various external factors, whereas the real parameters don’t vary too much for a particular agriculture field. The present work introduces a modified neural network approach to predict real agricultural parameters from sensor data with accuracy caused by several external factors and demonstrates enhanced predictive accuracy and adaptability. The neural network takes the sensor data as input in various weather conditions and tries to find out the original real parameters of that sensor data. The real-time sensor data was collected from multiple agricultural sites. The results demonstrated high predictive accuracy, with the neural network outperforming traditional statistical methods in forecasting soil moisture and other vital variables. Additionally, the model’s ability to generalize across different environmental conditions enhances its applicability in various crop management scenarios. The study concludes that neural networks hold significant potential for improving the efficiency of smart agriculture systems by providing timely, data driven insights for farmers and agronomists. Further research will explore the integration of deep learning models and edge computing to enhance scalability and realtime responsiveness in field applications. The aforementioned research highlights the significance of NPK sensors in sustainable farming methods, namely in enabling accurate nutrient management via real-time data.

Direct plasma treatment of caryopses after flowering in brewer’s rice cultivar Yamadanishiki enhanced those grain qualities through “Smart Agriculture System”

In present investigation, the effort is done to enhance the grain quality in brewer’s rice cultivar Yamadanishiki with the plasma treatment of caryopsis (rice fruit) on ripening process. Seedlings transplanted from a paddy field into pots were grown in a greenhouse, and each caryopsis was treated with plasma on 1, 5, 10 and 15 days after flowering (DAF). The ratio of white-core grains to total number of grains was decreased in the grains treated on DAF1, same level on DAF5, and increased on DAF10 and 15, respectively, compared with control grains. Moreover, same treatment test was conducted with seedlings transplanted from a paddy field into pots and grown in growth chambers equipped with a sensing system to monitor environmental and growth conditions, referred to the climatic conditions of paddy fields. The ratio of white-core grains to total number of grains was decreased in the grains treated on DAF1, and increased on DAF5, 10 and 15, respectively. We demonstrated that plasma treatment of caryopsis affected the formation of white core, and that environmental conditions in the growth chamber were simulated to a paddy field. We would advocate the next-generation agriculture using ICT and plasma, “Smart Agriculture System”, for producing high-quality crops.

Predictive Modeling in IoT-Driven Agriculture: A Comparative Study of Regression and Classification with LIME Interpretability

Abstract. This study aims to compare the performance of regression and classification machine learning models in crop prediction by analyzing data from IoT-driven agriculture. Linear regression and random forest regression models were used to predict the percentage of root growth dry matter, while logistic regression and support vector machines were employed to classify crop production regions. To enhance model interpretability, the LIME tool was applied to analyze feature importance. The experimental results demonstrate that the models perform well in terms of prediction accuracy, and LIME provided clear feature explanations, helping identify the variables with the greatest impact on prediction outcomes. This research offers data-driven insights for optimizing resource management in smart agricultural systems.

Industry 4.0 and Agriculture

Smart agriculture system creates an unprecedented power asymmetry suitable for agro-industrial production. In addition, it will bring to the producers the convenience when measured by the current use of the smart agriculture system. It is increasing the power of potential monopoly companies in the fields. It is useful for agricultural production as well as seeds, pesticides and fertilizers. It has been observed that it is difficult to protect producers or countries with low or limited economic resources in terms of food safety. It is also difficult to find answers that will both eliminate the concerns about the sustainability of traditional knowledge and lead to the destruction of local knowledge, genetic resources and biodiversity. In line with current globalization trends, it is taking shape.

Reliable and Resilient Wireless Communications in IoT-Based Smart Agriculture: A Case Study of Radio Wave Propagation in a Corn Field

In the past few years, one of the largest industries in the world, the agriculture sector, has faced many challenges, such as climate change and the depletion of limited natural resources. Smart Agriculture, based on IoT, is considered a transformative force that will play a crucial role in the further advancement of the agri-food sector. Furthermore, in IoT-based Smart Agriculture systems, radio wave propagation faces unique challenges (such as attenuation in vegetation and soil and multiple reflections) because of sensor nodes deployed in agriculture fields at or slightly above the ground level. In our study, we present, for the first time, several models (Multi-slope, Weissberger, and COST-235) suitable for planning radio coverage in a cornfield for Smart Agriculture applications. We received signal level measurements as a function of distance in a corn field (R3 corn stage) at 0.9 GHz and 2.4 GHz using two transmitting and two receiving antenna heights, with both horizontal and vertical polarization. The results indicate that radio wave propagation in a corn field is influenced not only by the surrounding environment (i.e., corn), but also by the antenna polarization and the positions of the transmitting and receiving antennas relative to the ground.

Pseudo-Labeling and Time-Series Data Analysis Model for Device Status Diagnostics in Smart Agriculture

This study proposes an automated data-labeling model that combines a pseudo-labeling algorithm with waveform segmentation based on Long Short-Term Memory (LSTM) to effectively label time-series data in smart agriculture. This model aims to address the inefficiency of manual labeling for large-scale data generated by agricultural systems, enhancing the performance and scalability of predictive models. Our proposed method leverages key features of time-series data to automatically generate labels for new data, thereby improving model accuracy and streamlining data processing. By automating the labeling process, we reduce dependence on manual labeling, which is often labor-intensive and prone to errors in large datasets. This approach enables the efficient preparation of labeled data for applications such as anomaly detection, pattern recognition, and predictive modeling in smart agriculture. Experimental results demonstrate that the automated labeling model achieves 89% accuracy in agricultural environments and reduces data processing time by 30%. Future research will focus on extending the model’s applicability to diverse agricultural settings, enhancing generalization performance, and improving real-time processing capabilities, thereby advancing intelligent and sustainable smart agriculture systems.

AGROTECH: A Smart Agricultural System Using IOT and ML

Agriculture remains one of the most important sectors that support human survival which supplies basic needs, a source of income for many dependants' community. Despite these advantages, resource scarcity, adverse environmental conditions and pest infestations remain serious threats to crop products. To mitigate these concerns, we present a smart agriculture system which uses the most modern technologies like IoT (Internet of Things) machine learning (ML) and sensors with automation. The system consists of an intelligent irrigation mechanism for efficient utilization of water, an animal detection system used to trace cattle, and a Light Dependent Resistor (LDR) Sensor joined with buzzer for early detection of any global environmental changes. Additionally, the integration of plant disease detection capabilities further enhances the system's effectiveness. By employing various sensors to collect data on environmental factors, including moisture and temperature, the proposed framework enables timely decision-making for crop management. The aim is to provide farmers with actionable insights, ensuring food security while minimizing resource consumption and economic losses. This review paper discusses the potential benefits and implementation strategies of such an intelligent agricultural system, emphasizing the need for innovation in agricultural practices to meet the growing demands of an increasing population

Petri net modeling and analysis of an IoT‐enabled system for real‐time monitoring of eggplants

AbstractAs the current agricultural industry is facing several challenges such as climate changes and lack of qualified farmers, it is extremely necessary to ensure sustainable agriculture and food supply by smart farming (SF). The SF can assist farmers and the associated stakeholders in making correct decisions on improving the yield and quality of agricultural products. In this study, an SF system based on low‐cost Raspberry Pi and Internet of Things (IoT) technologies has been successfully implemented. Through the data items detected by sensors to deeply manage the planting process, the IoT‐enabled communication protocol under ISO standards of Message Queuing Telemetry Transport (MQTT) was used. The bar charts of real‐time environmental parameters are presented on ThingsBoard to achieve the goal of data visualization. Meanwhile, a web server is built for the customized requirements, and the historical datasets are stored in the SQLite database system. Furthermore, a Petri net (PN) model was employed to detect all possible abnormal processes and to verify the feasibility and soundness of an IoT‐enabled system by using a software tool, WoPeD. Finally, the experimental results show that the plants cultivated by the proposed IoT‐enabled system are superior to those cultivated by the existing systems in many perspectives, including the monitoring distance, power consumption, precision, deadlock detection, and running time.

Tracking and Behavior Analysis of Group-Housed Pigs Based on a Multi-Object Tracking Approach

Smart farming technologies to track and analyze pig behaviors in natural environments are critical for monitoring the health status and welfare of pigs. This study aimed to develop a robust multi-object tracking (MOT) approach named YOLOv8 + OC-SORT(V8-Sort) for the automatic monitoring of the different behaviors of group-housed pigs. We addressed common challenges such as variable lighting, occlusion, and clustering between pigs, which often lead to significant errors in long-term behavioral monitoring. Our approach offers a reliable solution for real-time behavior tracking, contributing to improved health and welfare management in smart farming systems. First, the YOLOv8 is employed for the real-time detection and behavior classification of pigs under variable light and occlusion scenes. Second, the OC-SORT is utilized to track each pig to reduce the impact of pigs clustering together and occlusion on tracking. And, when a target is lost during tracking, the OC-SORT can recover the lost trajectory and re-track the target. Finally, to implement the automatic long-time monitoring of behaviors for each pig, we created an automatic behavior analysis algorithm that integrates the behavioral information from detection and the tracking results from OC-SORT. On the one-minute video datasets for pig tracking, the proposed MOT method outperforms JDE, Trackformer, and TransTrack, achieving the highest HOTA, MOTA, and IDF1 scores of 82.0%, 96.3%, and 96.8%, respectively. And, it achieved scores of 69.0% for HOTA, 99.7% for MOTA, and 75.1% for IDF1 on sixty-minute video datasets. In terms of pig behavior analysis, the proposed automatic behavior analysis algorithm can record the duration of four types of behaviors for each pig in each pen based on behavior classification and ID information to represent the pigs’ health status and welfare. These results demonstrate that the proposed method exhibits excellent performance in behavior recognition and tracking, providing technical support for prompt anomaly detection and health status monitoring for pig farming managers.

Blockchain-based Reputation System for Smart Farm Anomaly Detection and Prevention

While smart farming is becoming increasingly popular as a way to improve the efficiency and sustainability of agroecosystems and global food security, its vulnerability to cyber-attacks is rapidly increasing. A distributed reputation system can help detect and prevent malicious activities by tracking the reputation of IP addresses of devices, products, and entities in a smart farming system, thereby enhancing its security and trust. In this paper, we propose a blockchain-based reputation system for IP addresses in smart farms for real-time intrusion detection and prevention. The study uses the Ethereum blockchain smart contracts, Oracles, Intrusion Detection System (IDS), and a Proof-of-Reputation (PoR) consensus mechanism. The system was evaluated on two datasets, the Spamhaus Blocklist, and the Malware Domain List datasets. It achieved high accuracy, recall, and precision rates, F1 score, as well as low false positive and false negative rates. This shows that the proposed system has high potential to be an effective tool for improving malware detection and prevention in real-time, creating a trusted supply chain for a smart farming ecosystem against a wide range of attack types, including denial-of-service attacks, probe attacks, and user-to-root attacks.

Fuzzy automatic control of the irrigation process for the IoT-based smart farming systems

This paper is dedicated to the development and research of the advanced IoT-based fuzzy control system of the irrigation process for smart farming complexes of various types. The proposed automatic control system makes it possible to attain sufficiently high quality indicators of the soil moisture and pH control, which significantly improve the overall efficiency of irrigation processes and, as a result, the processes of growing various plants. In particular, more accurate control of soil moisture and pH allows improving soil microbial activity, optimizing nutrient uptake, increasing water utilization efficiency within the cultivated plants, which directly contribute to increased crop yields and sustainable resource management in agriculture. The designed system is created based on the principles of (a) hierarchical two-level IoT-based control, (b) simple and reliable two-channel fuzzy logic control with high performance and accuracy, as well as (c) easy customization and adaptability for various smart farming complexes. To evaluate the effectiveness of the proposed advanced system, the simulation experiments for automatic control of an irrigation process using the developed fuzzy controllers are carried out in this study at given optimal parameters (soil moisture and pH level) of growing conditions for two different crops: tomato and beet. The analysis of the obtained results of computer simulation shows that the designed system has higher efficiency and quality indicators compared to existing analogs when used for two different crops with significantly different optimal parameters of growing conditions.

Plant Leaf Disease Detection Using Xception Model

The traditional farming practices have been causing significant financial losses to farmers due to various reasons. However, the implementation of a modern, smart agricultural system utilizing machine learning techniques appears promising in safeguarding farmers and traders against these risks. This advanced system facilitates farmers in identifying common diseases through simple image recognition, employing a variety of image processing methods. Notably, the Convolutional Neural Network (CNN) algorithm stands out as an effective choice among these methods. Interestingly, among the available models, there has been limited utilization of the Xception model, and no comprehensive comparative study involving this model with different classifiers was found. To address this gap, a study was undertaken in two distinct approaches. Firstly, the Xception model demonstrated remarkable accuracy in detecting plant diseases, achieving an impressive 98.3 percent accuracy rate. In comparison, other classifiers such as logistic regression and three additional methods attained accuracy rates of 93 percent and 92 percent, respectively. Secondly, a comparative analysis was conducted on the top 12 papers out of a selection of 45 papers, each employing different methods. The Xception method once again proved to be effective in this context. Through these tests and review studies, the Xception method emerged as a reliable and superior choice. It is expected that this research will provide valuable insights for researchers and stakeholders, potentially guiding the development of new research initiatives in this field.

CNN-MLP-Based Configurable Robotic Arm for Smart Agriculture

Amidst escalating global populations and dwindling arable lands, enhancing agricultural productivity and sustainability is imperative. Addressing the inefficiencies of traditional agriculture, which struggles to meet the demands of large-scale production, this paper introduces a highly configurable smart agricultural robotic arm system (CARA), engineered using convolutional neural networks and multilayer perceptron. CARA integrates a highly configurable robotic arm, an image acquisition module, and a deep processing center, embodying the convergence of advanced robotics and artificial intelligence to facilitate precise and efficient agricultural tasks including harvesting, pesticide application, and crop inspection. Rigorous experimental validations confirm that the system significantly enhances operational efficiency, adapts seamlessly to diverse agricultural contexts, and bolsters the precision and sustainability of farming practices. This study not only underscores the vital role of intelligent automation in modern agriculture but also sets a precedent for future agricultural innovations.

Deep Hybrid Model for Pest Detection: IoT‐UAV‐Based Smart Agriculture System

ABSTRACTModern technology is revolutionising traditional farming processes by introducing new and streamlined approaches. Despite these advancements, challenges such as disease identification, insect detection and weather forecasting persist. To address these issues, this work proposes a DHMPD‐based IoT‐UAV smart agriculture system focused on pest detection. The method involves several stages: data acquisition, preprocessing, data augmentation, segmentation, feature extraction and classification. During data acquisition, a ‘Pest data set’ is collected. Preprocessing includes Z‐score normalisation to produce better‐normalised images. Data augmentation involves rotating images to create different orientations. The segmentation stage uses an updated HDBSCAN process, which improves the distance calculation between pixels using hybridised Euclidean and Minkowski distances. Feature extraction retrieves various features from segmented images, including modified MBP features, colour‐based features and shape‐based features. After feature extraction, the classification phase is performed by a hybrid technique with DL approaches such as improved DBN and LSTM approaches. Finally, classification results are averaged to predict pest detection accurately. The approach's effectiveness is evaluated through various assessments, aiming to overcome current limitations and enhance smart agriculture systems. The proposed DHMPD method was compared with state‐of‐the‐art approaches and traditional classifiers, achieving a maximum accuracy of 0.936, outperforming conventional methods in accurately detecting pests. Hence, the proposed work holds immense promise to advance the capabilities of smart agriculture systems, offering practical solutions that can benefit farmers, agricultural researchers and industries involved in crop management and food production.

Promoting flower yields, photosynthetic pigments and some secondary metabolite in Marguerite daisy (Chrysanthemum frutescens L.) cut flower under the smart farming system by pinching technique

Promoting flower yields, photosynthetic pigments and some secondary metabolite in Marguerite daisy (<i>Chrysanthemum frutescens</i> L.) cut flower under the smart farming system by pinching technique

SWINDU (Smart Farming dengan Wind Turbine) = Rancang Bangun Wind Turbine Sederhana berbasis ESP32 untuk Smart Farming Deteksi Kelembapan Tanah pada Sayuran Pokcoy

This research describes the design and implementation of a wind turbine system integrated with an ESP32 microcontroller to detect soil moisture in pakcoy vegetable plants. The main objective of this research is to create a sustainable solution that combines renewable energy with smart crop monitoring technology. The system uses a wind turbine as the main energy source that can generate electricity to operate the soil moisture sensor and DHT11 temperature sensor. The data obtained from these two sensors is used to measure and monitor the environmental conditions of pakcoy plants in real-time. The goal is to support the development of renewable energy, such as wind turbines, to address climate change and safeguard natural resources. Benefits include a reduction in the negative impacts of conventional energy and support for global efforts to address climate change. The methodology involves designing a wind turbine prototype, using temperature and humidity sensors, and integrating smart farming systems. The research also includes a research schedule. The results are expected to be an efficient prototype for generating electrical energy and a MitApp application for monitoring crop and wind energy data. This proposal is relevant to environmental and climate change issues and has the potential for a positive impact on sustainability and the environment.

Artificial intelligence-based smart agricultural systems for saffron cultivation with integration of Unmanned Aerial Vehicle imagery and deep learning approaches

This paper presents an integrated Unmanned Aerial Vehicle (UAV) imagery system and artificial intelligence-based smart farming for saffron cultivation. This work is about monitoring the growth of saffron flowers through UAV imagery every week or month during the pre-harvesting time while the percentage of the covered area by leaf and saffron flowers is calculated. This is crucial for predicting saffron growing areas over cultivated regions. From the vertical viewing of UAV imagery from above, it is problematic from the bird’s eye perspective to identify the particular saffron-growing areas of things, so the multi-class classification of flowers (subjects) recognition system is proposed. So, the implementation of this work is divided into four phases. The first two phases (Phase 1 and Phase 2) are about the algorithms for Saffron growing region detections and predictions using the modified You Only Look Once (YOLO) higher version model followed by statistical analysis based region corner detections. The outcomes of the first two phases are used to form a database of saffron flowers with different scales, orientations, and deformations. This is further used to build a two-class classification model in the third phase (Phase 3) that discriminates saffron vs. non-saffron flowers. In the fourth phase, the two-class classification model is extended to a multi-class flower recognition model for recognizing 900 flower images database available in the largest social media website dedicated exclusively to gardening in the Phase 4. Extensive experiments are conducted, and the performances are compared with some existing state-of-the-art methods that show the superiority of the proposed system.