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AI, IoT and Smart Sensors for Precision Agriculture

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 12987

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Special Issue Editors

Dr. Nahina Islam

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Guest Editor

School of Engineering and Technology, Melbourne Campus, Central Queensland University, Rockhampton, Australia
Interests: artificial intelligence (AI) for autonomous decision making for IoT based applications in smart farming, smart cities etc.; precision livestock; remote sensing; application of IoT and UAVs for smart farming; UAV image processing; deep learning alogrithms; AI for facial recognition; AI for fraud detection; AI for drowsiness detection for safe driving
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Dr. Dinko Oletic

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Guest Editor

Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia
Interests: acoustic sensors; passive ultrasonic sensors; piezoelectric sensors; MEMS; resonators; microfabrication; microsystems; low-power design; always-on sensor systems; passive sensing; near-zero sensing; wake-up sensing; sensor interfaces; sensor front-ends; embedded electronics; near-sensor processing; algorithms on embedded processors; hardware implementation of algorithms on FPGA; digital signal processing on energy-constrained devices; machine learning on low-power hardware; application domains of agriculture; medicine; biology

Special Issue Information

Dear Colleagues,

In "The 2030 Agenda for Sustainable Development", the United Nations (UN) and the international community set the target of eliminating global hunger by 2030. However, the world’s population is anticipated to reach to 10 billion by 2050, as per a 2018 report by the World Resources Institutes (WRI). Hence, to fulfill this anticipated increase in food demand, the use of artificial intelligence (AI)-, IoT- and smart sensor-based precision agriculture and precision livestock is inevitable. The aims of precision agriculture and livestock are to improve productivity, increase yields and profitability and reduce environmental footprints through techniques such as efficient irrigation, the targeted and precise use of pesticides and fertilizers for crops, and the vaccination scheduling and tracking of livestock. The implementation of AI, IoT and smart sensors can bring promising developments and innovations to agricultural sectors through data science, computer vision and deep learning-based algorithms.

The main purpose of this Special Issue is to identify and report innovative and novel research outcomes on the application of AI, IoT, smart sensors, machine learning, deep learning, remote sensing and autonomous systems in smart farming and precision livestock. At the same time, we welcome contributions on the use of advanced sensors, sensing systems and field instrumentation in smart agriculture.

Contributions may include, but are not limited to, the use of autonomous tractors, sprinklers and other instruments; infestation detection and removal using UAV images; crop health monitoring and yield prediction; smart and autonomous irrigation; soil mapping and fertilizer advisories; vegetation stress identification; livestock monitoring; tracking and controlling; vaccination scheduling of livestock; and the use of big data and high-performance computing for agriculture and livestock.

Regards,

Dr. Nahina Islam
Dr. Dinko Oletic
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (10 papers)

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Research

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29 pages, 4580 KiB

Open AccessArticle

Pattern Mining-Based Pig Behavior Analysis for Health and Welfare Monitoring

by Hassan Seif Mluba, Othmane Atif, Jonguk Lee, Daihee Park and Yongwha Chung

Sensors 2024, 24(7), 2185; https://doi.org/10.3390/s24072185 - 28 Mar 2024

Viewed by 348

Abstract

The increasing popularity of pigs has prompted farmers to increase pig production to meet the growing demand. However, while the number of pigs is increasing, that of farm workers has been declining, making it challenging to perform various farm tasks, the most important among them being managing the pigs’ health and welfare. This study proposes a pattern mining-based pig behavior analysis system to provide visualized information and behavioral patterns, assisting farmers in effectively monitoring and assessing pigs’ health and welfare. The system consists of four modules: (1) data acquisition module for collecting pigs video; (2) detection and tracking module for localizing and uniquely identifying pigs, using tracking information to crop pig images; (3) pig behavior recognition module for recognizing pig behaviors from sequences of cropped images; and (4) pig behavior analysis module for providing visualized information and behavioral patterns to effectively help farmers understand and manage pigs. In the second module, we utilize ByteTrack, which comprises YOLOx as the detector and the BYTE algorithm as the tracker, while MnasNet and LSTM serve as appearance features and temporal information extractors in the third module. The experimental results show that the system achieved a multi-object tracking accuracy of 0.971 for tracking and an F1 score of 0.931 for behavior recognition, while also highlighting the effectiveness of visualization and pattern mining in helping farmers comprehend and manage pigs’ health and welfare. Full article

(This article belongs to the Special Issue AI, IoT and Smart Sensors for Precision Agriculture)

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17 pages, 6316 KiB

Open AccessArticle

Leveraging Google Earth Engine for a More Effective Grassland Management: A Decision Support Application Perspective

by Cecilia Parracciani, Daniela Gigante, Federica Bonini, Anna Grassi, Luciano Morbidini, Mariano Pauselli, Bernardo Valenti, Emanuele Lilli, Francesco Antonielli and Marco Vizzari

Sensors 2024, 24(3), 834; https://doi.org/10.3390/s24030834 - 27 Jan 2024

Cited by 1 | Viewed by 671

Abstract

Grasslands cover a substantial portion of the earth’s surface and agricultural land and is crucial for human well-being and livestock farming. Ranchers and grassland management authorities face challenges in effectively controlling herders’ grazing behavior and grassland utilization due to underdeveloped infrastructure and poor communication in pastoral areas. Cloud-based grazing management and decision support systems (DSS) are needed to address this issue, promote sustainable grassland use, and preserve their ecosystem services. These systems should enable rapid and large-scale grassland growth and utilization monitoring, providing a basis for decision-making in managing grazing and grassland areas. In this context, this study contributes to the objectives of the EU LIFE IMAGINE project, aiming to develop a Web-GIS app for conserving and monitoring Umbria’s grasslands and promoting more informed decisions for more sustainable livestock management. The app, called “Praterie” and developed in Google Earth Engine, utilizes historical Sentinel-2 satellite data and harmonic modeling of the EVI (Enhanced Vegetation Index) to estimate vegetation growth curves and maturity periods for the forthcoming vegetation cycle. The app is updated in quasi-real time and enables users to visualize estimates for the upcoming vegetation cycle, including the maximum greenness, the days remaining to the subsequent maturity period, the accuracy of the harmonic models, and the grassland greenness status in the previous 10 days. Even though future additional developments can improve the informative value of the Praterie app, this platform can contribute to optimizing livestock management and biodiversity conservation by providing timely and accurate data about grassland status and growth curves. Full article

(This article belongs to the Special Issue AI, IoT and Smart Sensors for Precision Agriculture)

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16 pages, 3423 KiB

Open AccessArticle

Optimizing Winter Air Quality in Pig-Fattening Houses: A Plasma Deodorization Approach

by Liping Zhang, Meng Zhang, Qianfeng Yu, Shiguang Su, Yan Wang, Yu Fang and Wei Dong

Sensors 2024, 24(2), 324; https://doi.org/10.3390/s24020324 - 05 Jan 2024

Cited by 1 | Viewed by 678

Abstract

This study aimed to evaluate the effect of two circulation modes of a plasma deodorization unit on the air environment of pig-fattening houses in winter. Two pig-fattening houses were selected, one of which was installed with a plasma deodorizing device with two modes of operation, alternating internal and external circulation on a day-by-day basis. The other house did not have any form of treatment and was used as the control house. Upon installing the system, this study revealed that in the internal circulation mode, indoor temperature and humidity were sustained at elevated levels, with the NH₃ and H₂S concentrations decreasing by 63.87% and 100%, respectively, in comparison to the control house. Conversely, in the external circulation mode, the indoor temperature and humidity remained subdued, accompanied by a 16.43% reduction in CO₂ concentration. The adept interchange between these two operational modes facilitates the regulation of indoor air quality within a secure environment. This not only effectively diminishes deleterious gases in the pig-fattening house but also achieves the remote automation of environmental monitoring and hazardous gas management; thereby, it mitigates the likelihood of diseases and minimizes breeding risks. Full article

(This article belongs to the Special Issue AI, IoT and Smart Sensors for Precision Agriculture)

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22 pages, 11798 KiB

Open AccessArticle

SE-VisionTransformer: Hybrid Network for Diagnosing Sugarcane Leaf Diseases Based on Attention Mechanism

by Cuimin Sun, Xingzhi Zhou, Menghua Zhang and An Qin

Sensors 2023, 23(20), 8529; https://doi.org/10.3390/s23208529 - 17 Oct 2023

Cited by 2 | Viewed by 985

Abstract

Sugarcane is an important raw material for sugar and chemical production. However, in recent years, various sugarcane diseases have emerged, severely impacting the national economy. To address the issue of identifying diseases in sugarcane leaf sections, this paper proposes the SE-VIT hybrid network. Unlike traditional methods that directly use models for classification, this paper compares threshold, K-means, and support vector machine (SVM) algorithms for extracting leaf lesions from images. Due to SVM’s ability to accurately segment these lesions, it is ultimately selected for the task. The paper introduces the SE attention module into ResNet-18 (CNN), enhancing the learning of inter-channel weights. After the pooling layer, multi-head self-attention (MHSA) is incorporated. Finally, with the inclusion of 2D relative positional encoding, the accuracy is improved by 5.1%, precision by 3.23%, and recall by 5.17%. The SE-VIT hybrid network model achieves an accuracy of 97.26% on the PlantVillage dataset. Additionally, when compared to four existing classical neural network models, SE-VIT demonstrates significantly higher accuracy and precision, reaching 89.57% accuracy. Therefore, the method proposed in this paper can provide technical support for intelligent management of sugarcane plantations and offer insights for addressing plant diseases with limited datasets. Full article

(This article belongs to the Special Issue AI, IoT and Smart Sensors for Precision Agriculture)

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17 pages, 811 KiB

Open AccessArticle

AI-Enabled Traffic Control Prioritization in Software-Defined IoT Networks for Smart Agriculture

by Fahad Masood, Wajid Ullah Khan, Sana Ullah Jan and Jawad Ahmad

Sensors 2023, 23(19), 8218; https://doi.org/10.3390/s23198218 - 02 Oct 2023

Cited by 5 | Viewed by 1474

Abstract

Smart agricultural systems have received a great deal of interest in recent years because of their potential for improving the efficiency and productivity of farming practices. These systems gather and analyze environmental data such as temperature, soil moisture, humidity, etc., using sensor networks and Internet of Things (IoT) devices. This information can then be utilized to improve crop growth, identify plant illnesses, and minimize water usage. However, dealing with data complexity and dynamism can be difficult when using traditional processing methods. As a solution to this, we offer a novel framework that combines Machine Learning (ML) with a Reinforcement Learning (RL) algorithm to optimize traffic routing inside Software-Defined Networks (SDN) through traffic classifications. ML models such as Logistic Regression (LR), Random Forest (RF), k-nearest Neighbours (KNN), Support Vector Machines (SVM), Naive Bayes (NB), and Decision Trees (DT) are used to categorize data traffic into emergency, normal, and on-demand. The basic version of RL, i.e., the Q-learning (QL) algorithm, is utilized alongside the SDN paradigm to optimize routing based on traffic classes. It is worth mentioning that RF and DT outperform the other ML models in terms of accuracy. Our results illustrate the importance of the suggested technique in optimizing traffic routing in SDN environments. Integrating ML-based data classification with the QL method improves resource allocation, reduces latency, and improves the delivery of emergency traffic. The versatility of SDN facilitates the adaption of routing algorithms depending on real-time changes in network circumstances and traffic characteristics. Full article

(This article belongs to the Special Issue AI, IoT and Smart Sensors for Precision Agriculture)

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19 pages, 1894 KiB

Open AccessArticle

IoT and Ensemble Long-Short-Term-Memory-Based Evapotranspiration Forecasting for Riyadh

by Muhammad Asif Nauman, Mahlaqa Saeed, Oumaima Saidani, Tayyaba Javed, Latifah Almuqren, Rab Nawaz Bashir and Rashid Jahangir

Sensors 2023, 23(17), 7583; https://doi.org/10.3390/s23177583 - 01 Sep 2023

Cited by 1 | Viewed by 892

Abstract

Evapotranspiration (ET) is the fundamental component of efficient water resource management. Accurate forecasting of ET is essential for efficient water utilization in agriculture. ET forecasting is a complex process due to the requirements of large meteorological variables. The recommended approach is based on the Internet of Things (IoT) and an ensemble-learning-based approach for meteorological data collection and ET forecasting with limited meteorological conditions. IoT is part of the recommended approach to collect real-time data on meteorological variables. The daily maximum temperature (T), mean humidity (Hm), and maximum wind speed (Ws) are used to forecast evapotranspiration (ET). Long short-term memory (LSTM) and ensemble LSTM with bagged and boosted approaches are implemented and evaluated for their accuracy in forecasting ET values using meteorological data from 2001 to 2023. The results demonstrate that the bagged LSTM approach accurately forecasts ET with limited meteorological conditions in Riyadh, Saudi Arabia, with the coefficient of determination (R

^{2}

) of 0.94 compared to the boosted LSTM and off-the-shelf LSTM with R

^{2}

of 0.91 and 0.77, respectively. The bagged LSTM model is also more efficient with small values of root mean squared error (RMSE) and mean squared error (MSE) of 0.42 and 0.53 compared to the boosted LSTM and off-the-shelf LSTM models. Full article

(This article belongs to the Special Issue AI, IoT and Smart Sensors for Precision Agriculture)

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21 pages, 7353 KiB

Open AccessArticle

In-Field Tobacco Leaf Maturity Detection with an Enhanced MobileNetV1: Incorporating a Feature Pyramid Network and Attention Mechanism

by Yi Zhang, Yushuang Zhu, Xiongwei Liu, Yingjian Lu, Chan Liu, Xixin Zhou and Wei Fan

Sensors 2023, 23(13), 5964; https://doi.org/10.3390/s23135964 - 27 Jun 2023

Cited by 4 | Viewed by 1447

Abstract

The maturity of tobacco leaves plays a decisive role in tobacco production, affecting the quality of the leaves and production control. Traditional recognition of tobacco leaf maturity primarily relies on manual observation and judgment, which is not only inefficient but also susceptible to subjective interference. Particularly in complex field environments, there is limited research on in situ field maturity recognition of tobacco leaves, making maturity recognition a significant challenge. In response to this problem, this study proposed a MobileNetV1 model combined with a Feature Pyramid Network (FPN) and attention mechanism for in situ field maturity recognition of tobacco leaves. By introducing the FPN structure, the model fully exploits multi-scale features and, in combination with Spatial Attention and SE attention mechanisms, further enhances the expression ability of feature map channel features. The experimental results show that this model, with a size of 13.7 M and FPS of 128.12, performed outstandingly well on the task of field maturity recognition of tobacco leaves, achieving an accuracy of 96.3%, superior to classical models such as VGG16, VGG19, ResNet50, and EfficientNetB0, while maintaining excellent computational efficiency and small memory footprint. Experiments were conducted involving noise perturbations, changes in environmental brightness, and occlusions to validate the model’s robustness in dealing with the complex environments that may be encountered in actual applications. Finally, the Score-CAM algorithm was used for result visualization. Heatmaps showed that the vein and color variations of the leaves provide key feature information for maturity recognition. This indirectly validates the importance of leaf texture and color features in maturity recognition and, to some extent, enhances the credibility of the model. The model proposed in this study maintains high performance while having low storage requirements and computational complexity, making it significant for in situ field maturity recognition of tobacco leaves. Full article

(This article belongs to the Special Issue AI, IoT and Smart Sensors for Precision Agriculture)

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13 pages, 4236 KiB

Open AccessArticle

Development of Cotton Picker Fire Monitoring System Based on GA-BP Algorithm

by Weipeng Zhang, Bo Zhao, Shengbo Gao, Yuankun Zheng, Liming Zhou and Suchun Liu

Sensors 2023, 23(12), 5553; https://doi.org/10.3390/s23125553 - 13 Jun 2023

Cited by 1 | Viewed by 1193

Abstract

Due to the characteristics of the cotton picker working in the field and the physical characteristics of cotton, it is easy to burn during the operation, and it is difficult to be detected, monitored, and alarmed. In this study, a fire monitoring system of cotton pickers based on GA optimized BP neural network model was designed. By integrating the monitoring data of SHT21 temperature and humidity sensors and CO concentration monitoring sensors, the fire situation was predicted, and an industrial control host computer system was developed to monitor the CO gas concentration in real time and display it on the vehicle terminal. The BP neural network was optimized by using the GA genetic algorithm as the learning algorithm, and the data collected by the gas sensor were processed by the optimized network, which effectively improved the data accuracy of CO concentration during fires. In this system, the CO concentration in the cotton box of the cotton picker was validated, and the measured value of sensor was compared with the actual value, which verified the effectiveness of the optimized BP neural network model with GA. The experimental verification showed that the system monitoring error rate was 3.44%, the accurate early warning rate was over 96.5%, and the false alarm rate and the missed alarm rate were less than 3%. In this study, the fire of cotton pickers can be monitored in real time and an early warning can be made in time, and a new method was provided for accurate monitoring of fire in the field operation of cotton pickers. Full article

(This article belongs to the Special Issue AI, IoT and Smart Sensors for Precision Agriculture)

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20 pages, 6890 KiB

Open AccessArticle

YOLOv7-Peach: An Algorithm for Immature Small Yellow Peaches Detection in Complex Natural Environments

by Pingzhu Liu and Hua Yin

Sensors 2023, 23(11), 5096; https://doi.org/10.3390/s23115096 - 26 May 2023

Cited by 6 | Viewed by 1561

Abstract

Using object detection techniques on immature fruits to find out their quantity and position is a crucial step for intelligent orchard management. A yellow peach target detection model (YOLOv7-Peach) based on the improved YOLOv7 was proposed to address the problem of immature yellow peach fruits in natural scenes that are similar in color to the leaves but have small sizes and are easily obscured, leading to low detection accuracy. First, the anchor frame information from the original YOLOv7 model was updated by the K-means clustering algorithm in order to generate anchor frame sizes and proportions suitable for the yellow peach dataset; second, the CA (coordinate attention) module was embedded into the backbone network of YOLOv7 so as to enhance the network’s feature extraction for yellow peaches and to improve the detection accuracy; then, we accelerated the regression convergence process of the prediction box by replacing the object detection regression loss function with EIoU. Finally, the head structure of YOLOv7 added the P2 module for shallow downsampling, and the P5 module for deep downsampling was removed, effectively improving the detection of small targets. Experiments showed that the YOLOv7-Peach model had a 3.5% improvement in mAp (mean average precision) over the original one, much higher than that of SSD, Objectbox, and other target detection models in the YOLO series, and achieved better results under different weather conditions and a detection speed of up to 21 fps, suitable for real-time detection of yellow peaches. This method could provide technical support for yield estimation in the intelligent management of yellow peach orchards and also provide ideas for the real-time and accurate detection of small fruits with near background colors. Full article

(This article belongs to the Special Issue AI, IoT and Smart Sensors for Precision Agriculture)

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Review

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20 pages, 2141 KiB

Open AccessReview

A Review of Successes and Impeding Challenges of IoT-Based Insect Pest Detection Systems for Estimating Agroecosystem Health and Productivity of Cotton

by Denis O. Kiobia, Canicius J. Mwitta, Kadeghe G. Fue, Jason M. Schmidt, David G. Riley and Glen C. Rains

Sensors 2023, 23(8), 4127; https://doi.org/10.3390/s23084127 - 20 Apr 2023

Cited by 5 | Viewed by 2538

Abstract

Using artificial intelligence (AI) and the IoT (Internet of Things) is a primary focus of applied engineering research to improve agricultural efficiency. This review paper summarizes the engagement of artificial intelligence models and IoT techniques in detecting, classifying, and counting cotton insect pests and corresponding beneficial insects. The effectiveness and limitations of AI and IoT techniques in various cotton agricultural settings were comprehensively reviewed. This review indicates that insects can be detected with an accuracy of between 70 and 98% using camera/microphone sensors and enhanced deep learning algorithms. However, despite the numerous pests and beneficial insects, only a few species were targeted for detection and classification by AI and IoT systems. Not surprisingly, due to the challenges of identifying immature and predatory insects, few studies have designed systems to detect and characterize them. The location of the insects, sufficient data size, concentrated insects on the image, and similarity in species appearance are major obstacles when implementing AI. Similarly, IoT is constrained by a lack of effective field distance between sensors when targeting insects according to their estimated population size. Based on this study, the number of pest species monitored by AI and IoT technologies should be increased while improving the system’s detection accuracy. Full article

(This article belongs to the Special Issue AI, IoT and Smart Sensors for Precision Agriculture)

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