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Special Issue "Internet of Things (IoT) for Precision Agriculture Practices"

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A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: closed (20 October 2022) | Viewed by 30380

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Special Issue Editor

Prof. Dr. Raul Morais dos Santos

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Guest Editor

Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal
Interests: sensor interfaces; microelectronics; wireless sensor networks; IoT; precision viticulture; energy harvesting; proximal sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The global population is increasing significantly and is foreseen to reach 8.5 billion by 2030 and 9.7 billion in 2050. This increase, together with current and new environmental requirements, demands new agriculture production methods. Mature technologies are now available that rely on intelligent sensor networks and remote monitoring—using both satellites and UAVs—to acquire crop-related data and can be coupled with the use of collaborative robots for the execution of many of the agricultural operations. The applications of such technologies include forecasting models of harvest, creating real-time productivity maps, detecting spectral signatures of diseases and pests, and applying artificial intelligence for use in machine–machine processes.

Internet of Things (IoT) is part of this announced revolution. Now more than ever, it makes sense. Considering concerns about the environment and economic sustainability, the optimization of agricultural practice efficiency through the application of the latest computing and sensing technologies in order to save resources and manage all issues raised by the climate emergency is clearly the way forward.

This Special Issue covers precision agriculture (PA), smart farming, IoT, Internet of Everything (IoE), cloud and fog computing, big data, data analytics, and machine learning, as these are emergent technological topics that are becoming quite popular when addressing the sustainable management of agricultural practices with ecological awareness.

Prof. Dr. Raul Morais

Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 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.

Keywords

Internet of Things (IoT)
Wireless sensor networks
Precision agriculture
Field servers
Smart sensors
Sensor fusion
Data integration
Fog computing
Big data
Data analytics
Machine learning

Published Papers (14 papers)

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Research

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Open AccessArticle

Agricultural IoT Data Storage Optimization and Information Security Method Based on Blockchain

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Agriculture 2023, 13(2), 274; https://doi.org/10.3390/agriculture13020274 - 23 Jan 2023

Viewed by 661

Abstract

Given the issues of low efficiency of agricultural Internet of Things (IoT) data collection and data storage security, this study proposes a fast and reliable storage method for IoT data based on blockchain. Firstly, it performs RC5 encryption for data in the IoT sensor module. Secondly, it aggregates the same batch of collected data in the gateway into a transaction and reconstructs the Merkle ordered tree to verify the data integrity. Finally, it modifies the configuration rules of blockchain to improve the efficiency of blockchain data storage. Compared with experimental results for hash values of blockchain storage data and the stored data itself in the blockchain, the proposed method has significant advantages in data writing, and its efficiency in data reading was nearly 10 times higher than the other methods. At the same time, the method has the advantages of confidentiality, integrity, availability, controllability and non-repudiation of information security. The study can provide a solution for efficient collection and secure storage of agricultural IoT data, and it can provide technical support for realizing decentralized agricultural IoT data collection. Full article

(This article belongs to the Special Issue Internet of Things (IoT) for Precision Agriculture Practices)

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Open AccessArticle

Exploring Barriers to the Adoption of Internet of Things-Based Precision Agriculture Practices

Gaganpreet Singh Hundal

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Agriculture 2023, 13(1), 163; https://doi.org/10.3390/agriculture13010163 - 09 Jan 2023

Viewed by 750

Abstract

The production of row crops in the Midwestern (Indiana) region of the US has been facing environmental and economic sustainability issues. There has been an increase in trend for the application of fertilizers (nitrogen & phosphorus), farm machinery fuel costs and decreasing labor productivity leading to non-optimized usage of farm inputs. Literature describes how sustainable practices such as profitability (return on investments), operational cost reduction, hazardous waste reduction, delivery performance and overall productivity might be adopted in the context of precision agriculture technologies (variable rate irrigation, variable rate fertilization, cloud-based analytics, and telematics for farm machinery navigation). The literature review describes low adoption of Internet of Things (IoT)-based precision agriculture technologies, such as variable rate fertilizer (39%), variable rate pesticide (8%), variable rate irrigation (4%), cloud-based data analytics (21%) and telematics (10%) amongst Midwestern row crop producers. Barriers to the adoption of IoT-based precision agriculture technologies cited in the literature include cost effectiveness, power requirements, wireless communication range, data latency, data scalability, data storage, data processing and data interoperability. Therefore, this study focused on exploring and understanding decision-making variables related to barriers through three focus group interview sessions conducted with eighteen (n = 18) subject matter experts (SME) in IoT- based precision agriculture practices. Dependency relationships described between cost, data latency, data scalability, power consumption, communication range, type of wireless communication and precision agriculture application is one of the main findings. The results might inform precision agriculture practitioners, producers and other stakeholders about variables related to technical and operational barriers for the adoption of IoT-based precision agriculture practices. Full article

(This article belongs to the Special Issue Internet of Things (IoT) for Precision Agriculture Practices)

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Open AccessArticle

Improved Cotton Seed Breakage Detection Based on YOLOv5s

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Agriculture 2022, 12(10), 1630; https://doi.org/10.3390/agriculture12101630 - 07 Oct 2022

Viewed by 727

Abstract

Convolutional neural networks have been widely used in nondestructive testing of agricultural products. Aiming at the problems of missing detection, false detection, and slow detection, a lightweight improved cottonseed damage detection method based on YOLOv5s is proposed. Firstly, the focus element of the YOLOv5s backbone network is replaced by Denseblock, simplifying the number of modules in the backbone network layer, reducing redundant information, and improving the feature extraction ability of the network. Secondly, the collaborative attention (CA) mechanism module is added after the SPP pooling layer, and a large target detection layer is reduced to guide the network to pay more attention to the location, channel, and dimension information of small targets. Thirdly, Ghostconv is used instead of the conventional convolution layer in the neck feature fusion layer to reduce the amount of floating-point calculation and speed up the reasoning speed of the model. The CIOU loss function is selected as the border regression loss function to improve the recall rate of the model. Lastly, the model was verified using an ablation experiment and compared with the YOLOv4, Yolov5s, and SSD-VGG16 network models. The accuracy, recall rate, and map value of the improved network model were 92.4%, 91.7%, and 98.1%, respectively, and the average recognition time of each image was 97 fps. The results show that the improved network can effectively solve the problem of missing detection, reduce false detection, and have better recognition performance. This method can provide technical support for real-time and accurate detection of damaged cottonseed in a cottonseed screening device. Full article

(This article belongs to the Special Issue Internet of Things (IoT) for Precision Agriculture Practices)

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Open AccessArticle

A Real-Time Sorting Robot System for Panax Notoginseng Taproots Equipped with an Improved Deeplabv3+ Model

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Agriculture 2022, 12(8), 1271; https://doi.org/10.3390/agriculture12081271 - 20 Aug 2022

Cited by 1 | Viewed by 936

Abstract

The classification of the taproots of Panax notoginseng is conducive to improving the economic added value of its products. In this study, a real-time sorting robot system for Panax notoginseng taproots was developed based on the improved DeepLabv3+ model. The system is equipped with the improved DeepLabv3+ classification model for different grades of Panax notoginseng taproots. The model uses Xception as the taproot feature extraction network of Panax notoginseng. In the residual structure of the Xception network, a group normalization layer with deep separable convolution is adopted. Meanwhile, the global maximum pooling method is added in the Atrous Spatial Pyramid Pooling (ASPP) part to retain more texture information, and multiple shallow effective feature layers are designed to overlap in the decoding part to minimize the loss of features and improve the segmentation accuracy of Panax notoginseng taproots of all grades. The model test results show that the Xception-DeepLabv3+ model performs better than VGG16-U-Net and ResNet50-PSPNet models, with a Mean Pixel Accuracy (MPA) and a Mean Intersection over Union (MIoU) of 78.98% and 88.98% on the test set, respectively. The improved I-Xce-DeepLabv3+ model achieves an average detection time of 0.22 s, an MPA of 85.72%, and an MIoU of 90.32%, and it outperforms Xce-U-Net, Xce-PSPNet, and Xce-DeepLabv3+ models. The system control software was developed as a multi-threaded system to design a system grading strategy, which solves the problem that the identification signal is not synchronized with the grading signal. The system test results show that the average sorting accuracy of the system is 77% and the average false detection rate is 21.97% when the conveyor belt running speed is 1.55 m/s. The separation efficiency for a single-channel system is 200–300 kg/h, which can replace the manual work of three workers. The proposed method meets the requirements of current Panax notoginseng processing enterprises and provides technical support for the intelligent separation of Panax notoginseng taproots. Full article

(This article belongs to the Special Issue Internet of Things (IoT) for Precision Agriculture Practices)

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Open AccessFeature PaperArticle

VineInspector: The Vineyard Assistant

Jorge Mendes

Emanuel Peres

Filipe Neves dos Santos

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Agriculture 2022, 12(5), 730; https://doi.org/10.3390/agriculture12050730 - 22 May 2022

Cited by 2 | Viewed by 2221

Abstract

Proximity sensing approaches with a wide array of sensors available for use in precision viticulture contexts can nowadays be considered both well-know and mature technologies. Still, several in-field practices performed throughout different crops rely on direct visual observation supported on gained experience to assess aspects of plants’ phenological development, as well as indicators relating to the onset of common plagues and diseases. Aiming to mimic in-field direct observation, this paper presents VineInspector: a low-cost, self-contained and easy-to-install system, which is able to measure microclimatic parameters, and also to acquire images using multiple cameras. It is built upon a stake structure, rendering it suitable for deployment across a vineyard. The approach through which distinguishable attributes are detected, classified and tallied in the periodically acquired images, makes use of artificial intelligence approaches. Furthermore, it is made available through an IoT cloud-based support system. VineInspector was field-tested under real operating conditions to assess not only the robustness and the operating functionality of the hardware solution, but also the AI approaches’ accuracy. Two applications were developed to evaluate VineInspector’s consistency while a viticulturist’ assistant in everyday practices. One was intended to determine the size of the very first grapevines’ shoots, one of the required parameters of the well known 3–10 rule to predict primary downy mildew infection. The other was developed to tally grapevine moth males captured in sex traps. Results show that VineInspector is a logical step in smart proximity monitoring by mimicking direct visual observation from experienced viticulturists. While the latter traditionally are responsible for a set of everyday practices in the field, these are time and resource consuming. VineInspector was proven to be effective in two of these practices, performing them automatically. Therefore, it enables both the continuous monitoring and assessment of a vineyard’s phenological development in a more efficient manner, making way to more assertive and timely practices against pests and diseases. Full article

(This article belongs to the Special Issue Internet of Things (IoT) for Precision Agriculture Practices)

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Open AccessArticle

Robust Multi-Gateway Authentication Scheme for Agriculture Wireless Sensor Network in Society 5.0 Smart Communities

Haqi Khalid

Shaiful Jahari Hashim

Sharifah Mumtazah Syed Ahmad

Fazirulhisyam Hashim

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Muhammad Akmal Chaudhary

Agriculture 2021, 11(10), 1020; https://doi.org/10.3390/agriculture11101020 - 19 Oct 2021

Viewed by 1765

Abstract

Recent Society 5.0 efforts by the Government of Japan are aimed at establishing a sustainable human-centered society by combining new technologies such as sensor networks, edge computing, Internet of Things (IoT) ecosystems, artificial intelligence (AI), big data, and robotics. Many research works have been carried out with an increasing emphasis on the fundamentals of wireless sensor networks (WSN) for different applications; namely precision agriculture, environment, medical care, security, and surveillance. In the same vein, almost all of the known authentication techniques rely on the single gateway node, which is unsuitable for the current sensor nodes that are broadly distributed in the real world. Despite technological advances, resource constraints and vulnerability to an attacker physically capturing some sensor nodes have remained an important and challenging research field for developing wireless sensor network user authentication. This work proposes a new authentication scheme for agriculture professionals based on a multi-gateway communication model using a fuzzy extractor algorithm to support the Society 5.0 environment. The scheme provides a secure mutual authentication using the well-established formal method called BAN logic. The formal security verification of the proposed scheme is validated with the AVISPA tool, a powerful validation method for network security applications. In addition, the security of the scheme was informally analyzed to demonstrate that the scheme is secure from different attacks, e.g., sensor capture, replay, and other network and physical attacks. Furthermore, the communication and computation costs of the proposed scheme are evaluated and show better performance than the existing authentication schemes. Full article

(This article belongs to the Special Issue Internet of Things (IoT) for Precision Agriculture Practices)

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Open AccessArticle

Morphological and Physiological Responses of Brassica chinensis on Different Far-Red (FR) Light Treatments Using Internet-of-Things (IoT) Technology

Ahmad Nizar Harun

Robiah Ahmad

Norliza Mohamed

Abd Rahman Abdul Rahim

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Hazilah Mad Kaidi

Agriculture 2021, 11(8), 728; https://doi.org/10.3390/agriculture11080728 - 31 Jul 2021

Cited by 2 | Viewed by 1938

Abstract

Advanced technology in agriculture has enabled the manipulation of the artificial light spectrum in plant development such as improving yield and plant growth. Light manipulation using light-emitting diodes or LEDs can inhibit, delay, or even promote flowering. Some studies have shown that far-red (FR) light can stop flowering, but studies have not fully explored the best method involving intensity and duration to induce plant growth. This paper presents results on LED light manipulation techniques, particularly FR light, on plant flowering control and plant elongation. The light manipulation technique on the combination of colors, photoperiods, and intensities proved that it can stop flowering, and stimulate and control the growth of plants during cultivation. The system was monitored using an Internet-of-Things (IoT) remote monitoring system, and it performed data mining. The results showed that plants that were grown under artificial sunlight (T5) and normal light (T1) treatments were superior compared to others. The FR light delayed flowering until 50 days of planting and accelerated the plant growth and increased the fresh weight by 126%. The experiment showed that a high variable intensity at 300 µmol m⁻¹s⁻¹ showed a great performance and produced the largest leaf area of 1517.0 cm² and the highest fresh weight of 492.92 g. This study provides new insights to the researchers and the farming community on artificial light systems in improving plant factory production efficiency and in determining the best plant cultivation approach to create a stronger indoor farming management plant. Full article

(This article belongs to the Special Issue Internet of Things (IoT) for Precision Agriculture Practices)

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Open AccessArticle

High Accuracy Pre-Harvest Sugarcane Yield Forecasting Model Utilizing Drone Image Analysis, Data Mining, and Reverse Design Method

Bhoomin Tanut

Rattapoom Waranusast

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Panomkhawn Riyamongkol

Agriculture 2021, 11(7), 682; https://doi.org/10.3390/agriculture11070682 - 19 Jul 2021

Cited by 6 | Viewed by 2102

Abstract

This article presents a new model for forecasting the sugarcane yield that substantially reduces current rates of assessment errors, providing a more reliable pre-harvest assessment tool for sugarcane production. This model, called the Wondercane model, integrates various environmental data obtained from sugar mill surveys and government agencies with the analysis of aerial images of sugarcane fields obtained with drones. The drone images enable the calculation of the proportion of unusable sugarcane (the defect rate) in the field. Defective cane can result from adverse weather or other cultivation issues. The Wondercane model is developed on the principle of determining the yield not through data in regression form but rather through data in classification form. The Reverse Design method and the Similarity Relationship method are applied for feature extraction of the input factors and the target outputs. The model utilizes data mining to recognize and classify the dataset from the sugarcane field. Results show that the optimal performance of the model is achieved when: (1) the number of Input Factors is five, (2) the number of Target Outputs is 32, and (3) the Random Forest algorithm is used. The model recognized the 2019 training data with an accuracy of 98.21%, and then it correctly forecast the yield of the 2019 test data with an accuracy of 89.58% (10.42% error) when compared to the actual yield. The Wondercane model correctly forecast the harvest yield of a 2020 dataset with an accuracy of 98.69% (1.31% error). The Wondercane model is therefore an accurate and robust tool that can substantially reduce the issue of sugarcane yield estimate errors and provide the sugar industry with improved pre-harvest assessment of sugarcane yield. Full article

(This article belongs to the Special Issue Internet of Things (IoT) for Precision Agriculture Practices)

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Open AccessEditor’s ChoiceArticle

Improved Soil Moisture and Electrical Conductivity Prediction of Citrus Orchards Based on IoT Using Deep Bidirectional LSTM

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Agriculture 2021, 11(7), 635; https://doi.org/10.3390/agriculture11070635 - 07 Jul 2021

Cited by 11 | Viewed by 1873

Abstract

In order to create an irrigation scheduling plan for use in large-area citrus orchards, an environmental information collection system of citrus orchards was established based on the Internet of Things (IoT). With the environmental information data, deep bidirectional long short-term memory (Bid-LSTM) networks are proposed to improve soil moisture (SM) and soil electrical conductivity (SEC) predictions, providing a meaningful reference for the irrigation and fertilization of citrus orchards. The IoT system contains SM, SEC, air temperature and humidity, wind speed, and precipitation sensors, while the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) were calculated to evaluate the performance of the models. The performance of the deep Bid-LSTM model was compared with a multi-layer neural network (MLNN). The results for the performance criteria reveal that the proposed deep Bid-LSTM networks perform better than the MLNN model, according to many of the evaluation indicators of this study. Full article

(This article belongs to the Special Issue Internet of Things (IoT) for Precision Agriculture Practices)

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Open AccessEditor’s ChoiceArticle

A Versatile, Low-Power and Low-Cost IoT Device for Field Data Gathering in Precision Agriculture Practices

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Agriculture 2021, 11(7), 619; https://doi.org/10.3390/agriculture11070619 - 30 Jun 2021

Cited by 16 | Viewed by 3028

Abstract

Spatial and temporal variability characterization in Precision Agriculture (PA) practices is often accomplished by proximity data gathering devices, which acquire data from a wide variety of sensors installed within the vicinity of crops. Proximity data acquisition usually depends on a hardware solution to which some sensors can be coupled, managed by a software that may (or may not) store, process and send acquired data to a back-end using some communication protocol. The sheer number of both proprietary and open hardware solutions, together with the diversity and characteristics of available sensors, is enough to deem the task of designing a data acquisition device complex. Factoring in the harsh operational context, the multiple DIY solutions presented by an active online community, available in-field power approaches and the different communication protocols, each proximity monitoring solution can be regarded as singular. Data acquisition devices should be increasingly flexible, not only by supporting a large number of heterogeneous sensors, but also by being able to resort to different communication protocols, depending on both the operational and functional contexts in which they are deployed. Furthermore, these small and unattended devices need to be sufficiently robust and cost-effective to allow greater in-field measurement granularity 365 days/year. This paper presents a low-cost, flexible and robust data acquisition device that can be deployed in different operational contexts, as it also supports three different communication technologies: IEEE 802.15.4/ZigBee, LoRa/LoRaWAN and GRPS. Software and hardware features, suitable for using heat pulse methods to measure sap flow, leaf wetness sensors and others are embedded. Its power consumption is of only 83

μ

A during sleep mode and the cost of the basic unit was kept below the EUR 100 limit. In-field continuous evaluation over the past three years prove that the proposed solution—SPWAS’21—is not only reliable but also represents a robust and low-cost data acquisition device capable of gathering different parameters of interest in PA practices. Full article

(This article belongs to the Special Issue Internet of Things (IoT) for Precision Agriculture Practices)

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Open AccessArticle

An Efficient Case Retrieval Algorithm for Agricultural Case-Based Reasoning Systems, with Consideration of Case Base Maintenance

Zhaoyu Zhai

José-Fernán Martínez Ortega

Néstor Lucas Martínez

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Huanliang Xu

Agriculture 2020, 10(9), 387; https://doi.org/10.3390/agriculture10090387 - 03 Sep 2020

Viewed by 2414

Abstract

Case-based reasoning has considerable potential to model decision support systems for smart agriculture, assisting farmers in managing farming operations. However, with the explosive amount of sensing data, these systems may achieve poor performance in knowledge management like case retrieval and case base maintenance. Typical approaches of case retrieval have to traverse all past cases for matching similar ones, leading to low efficiency. Thus, a new case retrieval algorithm for agricultural case-based reasoning systems is proposed in this paper. At the initial stage, an association table is constructed, containing the relationships between all past cases. Afterwards, attributes of a new case are compared with an entry case. According to the similarity measurement, associated similar or dissimilar cases are then compared preferentially, instead of traversing the whole case base. The association of the new case is generated through case retrieval and added in the association table at the step of case retention. The association table is also updated when a closer relationship is detected. The experiment result demonstrates that our proposal enables rapid case retrieval with promising accuracy by comparing a fewer number of past cases. Thus, the retrieval efficiency of our proposal outperforms typical approaches. Full article

(This article belongs to the Special Issue Internet of Things (IoT) for Precision Agriculture Practices)

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Review

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Open AccessReview

A Systematic Review on Automatic Insect Detection Using Deep Learning

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Agriculture 2023, 13(3), 713; https://doi.org/10.3390/agriculture13030713 - 19 Mar 2023

Viewed by 348

Abstract

Globally, insect pests are the primary reason for reduced crop yield and quality. Although pesticides are commonly used to control and eliminate these pests, they can have adverse effects on the environment, human health, and natural resources. As an alternative, integrated pest management has been devised to enhance insect pest control, decrease the excessive use of pesticides, and enhance the output and quality of crops. With the improvements in artificial intelligence technologies, several applications have emerged in the agricultural context, including automatic detection, monitoring, and identification of insects. The purpose of this article is to outline the leading techniques for the automated detection of insects, highlighting the most successful approaches and methodologies while also drawing attention to the remaining challenges and gaps in this area. The aim is to furnish the reader with an overview of the major developments in this field. This study analysed 92 studies published between 2016 and 2022 on the automatic detection of insects in traps using deep learning techniques. The search was conducted on six electronic databases, and 36 articles met the inclusion criteria. The inclusion criteria were studies that applied deep learning techniques for insect classification, counting, and detection, written in English. The selection process involved analysing the title, keywords, and abstract of each study, resulting in the exclusion of 33 articles. The remaining 36 articles included 12 for the classification task and 24 for the detection task. Two main approaches—standard and adaptable—for insect detection were identified, with various architectures and detectors. The accuracy of the classification was found to be most influenced by dataset size, while detection was significantly affected by the number of classes and dataset size. The study also highlights two challenges and recommendations, namely, dataset characteristics (such as unbalanced classes and incomplete annotation) and methodologies (such as the limitations of algorithms for small objects and the lack of information about small insects). To overcome these challenges, further research is recommended to improve insect pest management practices. This research should focus on addressing the limitations and challenges identified in this article to ensure more effective insect pest management. Full article

(This article belongs to the Special Issue Internet of Things (IoT) for Precision Agriculture Practices)

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Open AccessEditor’s ChoiceReview

Smart Sensing with Edge Computing in Precision Agriculture for Soil Assessment and Heavy Metal Monitoring: A Review

Mohammad Nishat Akhtar

Abdurrahman Javid Shaikh

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Agriculture 2021, 11(6), 475; https://doi.org/10.3390/agriculture11060475 - 21 May 2021

Cited by 16 | Viewed by 5493

Abstract

With the implementation of the Internet of Things, the agricultural domain has become data-driven, allowing for well-timed and cost-effective farm management while remaining environmentally sustainable. Thus, the incorporation of Internet of Things in the agricultural domain is the need of the hour for developing countries whose gross domestic product primarily depends on the farming sector. It is worth highlighting that developing nations lack the infrastructure for precision agriculture; therefore, it has become necessary to come up with a methodological paradigm which can accommodate a complete model to connect ground sensors to the compute nodes in a cost-effective way by keeping the data processing limitations and constraints in consideration. In this regard, this review puts forward an overview of the state-of-the-art technologies deployed in precision agriculture for soil assessment and pollutant monitoring with respect to heavy metal in agricultural soil using various sensors. Secondly, this manuscript illustrates the processing of data generated from the sensors. In this regard, an optimized method of data processing derived from cloud computing has been shown, which is called edge computing. In addition to this, a new model of high-performance-based edge computing is also shown for efficient offloading of data with smooth workflow optimization. In a nutshell, this manuscript aims to open a new corridor for the farming sector in developing nations by tackling challenges and providing substantial consideration. Full article

(This article belongs to the Special Issue Internet of Things (IoT) for Precision Agriculture Practices)

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Open AccessReview

Research Status and Prospects on Plant Canopy Structure Measurement Using Visual Sensors Based on Three-Dimensional Reconstruction

Jizhang Wang

Yun Zhang

and

Rongrong Gu

Agriculture 2020, 10(10), 462; https://doi.org/10.3390/agriculture10100462 - 08 Oct 2020

Cited by 8 | Viewed by 3475

Abstract

Three-dimensional (3D) plant canopy structure analysis is an important part of plant phenotype studies. To promote the development of plant canopy structure measurement based on 3D reconstruction, we reviewed the latest research progress achieved using visual sensors to measure the 3D plant canopy structure from four aspects, including the principles of 3D plant measurement technologies, the corresponding instruments and specifications of different visual sensors, the methods of plant canopy structure extraction based on 3D reconstruction, and the conclusion and promise of plant canopy measurement technology. In the current research phase on 3D structural plant canopy measurement techniques, the leading algorithms of every step for plant canopy structure measurement based on 3D reconstruction are introduced. Finally, future prospects for a standard phenotypical analytical method, rapid reconstruction, and precision optimization are described. Full article

(This article belongs to the Special Issue Internet of Things (IoT) for Precision Agriculture Practices)

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