Special Issue "Image Analysis Techniques in Agriculture"

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

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Prof. Dr. Maciej Zaborowicz
E-Mail Website1 Website2
Guest Editor
Department of Biosystems Engineering, Poznań University of Life Sciences, Poznan, Poland
Interests: computer sciences; process modeling and optimization; neural networks; biogas and bioenergy production; neural image analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Dawid Wojcieszak
E-Mail Website
Co-Guest Editor
Institute of Biosystems Engineering, Poznań University of Life Sciences, Poznan, Poland
Interests: agricultural engineering; biosystems engineering; Agriculture 4.0; energy management systems in agriculture; energy input output analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In modern agriculture, which is currently so-called Agriculture 4.0, the main emphasis is placed on the development of precise tools supporting all activities related to food production. Increasing attention is now being paid not only to the quantity but also, and above all, to the quality of agricultural products, as well as to waste and its characteristics. Computer image analysis, which in many respects supports precision farming, is becoming a widely used tool. It should be mentioned that the quality of individual products, such as cereals, vegetables, and fruits, should be assessed, but it is also possible to assess the condition of a field and plantation. These tools have an advisory function and in many cases are able to prevent losses to the agricultural producer. The 21st century is a century of information, including information encoded in graphic form, most often in the form of a digital image. Appropriate acquisition of material, and then determination of characteristics and production of methods that will directly or indirectly support the agricultural production, e.g., in the aspect of plant protection, etc. belongs among the broad interests of modern Agriculture 4.0.

Dr. Maciej Zaborowicz
Dr. Dawid Wojcieszak
Guest Editors

Manuscript Submission Information

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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 1600 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

  • Imaging techniques in agriculture
  • Acquisition of the image of products of agricultural origin
  • Acquisition of characteristics
  • Image processing
  • Data processing
  • Image analysis
  • Neural image analysis
  • Product evaluation
  • Quality assessment
  • Safety of food products
  • Other agricultural topics (Image analysis)

Published Papers (15 papers)

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Research

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Article
Study on Plant Growth and Nutrient Uptake under Different Aeration Intensity in Hydroponics with the Application of Particle Image Velocimetry
Agriculture 2021, 11(11), 1140; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11111140 - 14 Nov 2021
Viewed by 387
Abstract
Aeration is considered beneficial for hydroponics. However, little information is available on the effects of aeration, and even less on solutions that use bubble flow and their agronomic effects. In this study, the effects of aeration intensity on plants were studied through cultivation [...] Read more.
Aeration is considered beneficial for hydroponics. However, little information is available on the effects of aeration, and even less on solutions that use bubble flow and their agronomic effects. In this study, the effects of aeration intensity on plants were studied through cultivation experiments and flow field visualization. It was found that the growth of plants did not increase linearly with an increase in aeration intensity. From the results of this study, when the aeration intensity was within the low range (0.07–0.15 L·L−1 NS·min−1), increasing the aeration intensity increased the plant growth. However, after the aeration intensity reached a certain extent (0.15–1.18 L·L−1 NS·min−1), some indicators did not change significantly. When the aeration intensity continued to increase (1.18–2.35 L·L−1 NS·min−1), growth began to decrease. These results show that for increasing dissolved oxygen and promoting plant growth, the rule is not “the higher the aeration intensity, the better”. There is a reasonable range of aeration intensity within which crops grow normally and rapidly. In addition, increasing the aeration intensity means increasing energy utilization and operating costs. In actual hydroponics production, it is very important to find a reasonable aeration intensity range. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)
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Article
Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)
Agriculture 2021, 11(11), 1098; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11111098 - 04 Nov 2021
Viewed by 291
Abstract
Background. Efficient analysis of large image data produced in greenhouse phenotyping experiments is often challenged by a large variability of optical plant and background appearance which requires advanced classification model methods and reliable ground truth data for their training. In the absence [...] Read more.
Background. Efficient analysis of large image data produced in greenhouse phenotyping experiments is often challenged by a large variability of optical plant and background appearance which requires advanced classification model methods and reliable ground truth data for their training. In the absence of appropriate computational tools, generation of ground truth data has to be performed manually, which represents a time-consuming task. Methods. Here, we present a efficient GUI-based software solution which reduces the task of plant image segmentation to manual annotation of a small number of image regions automatically pre-segmented using k-means clustering of Eigen-colors (kmSeg). Results. Our experimental results show that in contrast to other supervised clustering techniques k-means enables a computationally efficient pre-segmentation of large plant images in their original resolution. Thereby, the binary segmentation of plant images in fore- and background regions is performed within a few minutes with the average accuracy of 96–99% validated by a direct comparison with ground truth data. Conclusions. Primarily developed for efficient ground truth segmentation and phenotyping of greenhouse-grown plants, the kmSeg tool can be applied for efficient labeling and quantitative analysis of arbitrary images exhibiting distinctive differences between colors of fore- and background structures. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)
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Article
Review on Multitemporal Classification Methods of Satellite Images for Crop and Arable Land Recognition
Agriculture 2021, 11(10), 999; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11100999 - 13 Oct 2021
Viewed by 343
Abstract
This paper presents a review of the conducted research in the field of multitemporal classification methods used for the automatic identification of crops and arable land using optical satellite images. The review and systematization of these methods in terms of the effectiveness of [...] Read more.
This paper presents a review of the conducted research in the field of multitemporal classification methods used for the automatic identification of crops and arable land using optical satellite images. The review and systematization of these methods in terms of the effectiveness of the obtained results and their accuracy allows for the planning towards further development in this area. The state of the art analysis concerns various methodological approaches, including selection of data in terms of spatial resolution, selection of algorithms, as well as external conditions related to arable land use, especially the structure of crops. The results achieved with use of various approaches and classifiers and subsequently reported in the literature vary depending on the crops and area of analysis and the sources of satellite data. Hence, their review and systematic conclusions are needed, especially in the context of the growing interest in automatic processes of identifying crops for statistical purposes or monitoring changes in arable land. The results of this study show no significant difference between the accuracy achieved from different machine learning algorithms, yet on average artificial neural network classifiers have results that are better by a few percent than others. For very fragmented regions, better results were achieved using Sentinel-2, SPOT-5 rather than Landsat images, but the level of accuracy can still be improved. For areas with large plots there is no difference in the level of accuracy achieved from any HR images. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)
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Article
Potato Surface Defect Detection Based on Deep Transfer Learning
Agriculture 2021, 11(9), 863; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11090863 - 10 Sep 2021
Cited by 1 | Viewed by 625
Abstract
Food defect detection is crucial for the automation of food production and processing. Potato surface defect detection remains challenging due to the irregular shape of potato individuals and various types of defects. This paper employs deep convolutional neural network (DCNN) models for potato [...] Read more.
Food defect detection is crucial for the automation of food production and processing. Potato surface defect detection remains challenging due to the irregular shape of potato individuals and various types of defects. This paper employs deep convolutional neural network (DCNN) models for potato surface defect detection. In particular, we applied transfer learning by fine-tuning a base model through three DCNN models—SSD Inception V2, RFCN ResNet101, and Faster RCNN ResNet101—on a self-developed dataset, and achieved an accuracy of 92.5%, 95.6%, and 98.7%, respectively. RFCN ResNet101 presented the best overall performance in detection speed and accuracy. It was selected as the final model for out-of-sample testing, further demonstrating the model’s ability to generalize. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)
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Article
A Comparative Study of Various Methods for Handling Missing Data in UNSODA
Agriculture 2021, 11(8), 727; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11080727 - 30 Jul 2021
Viewed by 580
Abstract
UNSODA, a free international soil database, is very popular and has been used in many fields. However, missing soil property data have limited the utility of this dataset, especially for data-driven models. Here, three machine learning-based methods, i.e., random forest (RF) regression, support [...] Read more.
UNSODA, a free international soil database, is very popular and has been used in many fields. However, missing soil property data have limited the utility of this dataset, especially for data-driven models. Here, three machine learning-based methods, i.e., random forest (RF) regression, support vector (SVR) regression, and artificial neural network (ANN) regression, and two statistics-based methods, i.e., mean and multiple imputation (MI), were used to impute the missing soil property data, including pH, saturated hydraulic conductivity (SHC), organic matter content (OMC), porosity (PO), and particle density (PD). The missing upper depths (DU) and lower depths (DL) for the sampling locations were also imputed. Before imputing the missing values in UNSODA, a missing value simulation was performed and evaluated quantitatively. Next, nonparametric tests and multiple linear regression were performed to qualitatively evaluate the reliability of these five imputation methods. Results showed that RMSEs and MAEs of all features fluctuated within acceptable ranges. RF imputation and MI presented the lowest RMSEs and MAEs; both methods are good at explaining the variability of data. The standard error, coefficient of variance, and standard deviation decreased significantly after imputation, and there were no significant differences before and after imputation. Together, DU, pH, SHC, OMC, PO, and PD explained 91.0%, 63.9%, 88.5%, 59.4%, and 90.2% of the variation in BD using RF, SVR, ANN, mean, and MI, respectively; and this value was 99.8% when missing values were discarded. This study suggests that the RF and MI methods may be better for imputing the missing data in UNSODA. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)
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Article
Disease Detection in Apple Leaves Using Deep Convolutional Neural Network
Agriculture 2021, 11(7), 617; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11070617 - 30 Jun 2021
Cited by 2 | Viewed by 970
Abstract
The automatic detection of diseases in plants is necessary, as it reduces the tedious work of monitoring large farms and it will detect the disease at an early stage of its occurrence to minimize further degradation of plants. Besides the decline of plant [...] Read more.
The automatic detection of diseases in plants is necessary, as it reduces the tedious work of monitoring large farms and it will detect the disease at an early stage of its occurrence to minimize further degradation of plants. Besides the decline of plant health, a country’s economy is highly affected by this scenario due to lower production. The current approach to identify diseases by an expert is slow and non-optimal for large farms. Our proposed model is an ensemble of pre-trained DenseNet121, EfficientNetB7, and EfficientNet NoisyStudent, which aims to classify leaves of apple trees into one of the following categories: healthy, apple scab, apple cedar rust, and multiple diseases, using its images. Various Image Augmentation techniques are included in this research to increase the dataset size, and subsequentially, the model’s accuracy increases. Our proposed model achieves an accuracy of 96.25% on the validation dataset. The proposed model can identify leaves with multiple diseases with 90% accuracy. Our proposed model achieved a good performance on different metrics and can be deployed in the agricultural domain to identify plant health accurately and timely. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)
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Article
Classification of Grain Storage Inventory Modes Based on Temperature Contour Map of Grain Bulk Using Back Propagation Neural Network
Agriculture 2021, 11(5), 451; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11050451 - 16 May 2021
Viewed by 698
Abstract
Inventory modes classification can reduce the workload of grain depot management and it is time-saving, not labor-intensive. This paper proposed a method of using a temperature contour map converted from digital temperature data to classify stored grain inventory modes in a large bulk [...] Read more.
Inventory modes classification can reduce the workload of grain depot management and it is time-saving, not labor-intensive. This paper proposed a method of using a temperature contour map converted from digital temperature data to classify stored grain inventory modes in a large bulk grain warehouse, which mainly included detection of inventory changes and routine operations performed (aeration). The back propagation (BP) neural network was used in this method to identify and classify grain storage inventory modes based on the temperature contour map for helping grain depot management work. The method extracted and combined color coherence vector (CCV), texture feature vector (TFV) and smoothness feature vector (SFV) of temperature contour maps as the input vector of the BP neural network, and used inventory modes as the output vector. The experimental results indicated that the accuracy of the BP neural network with vector (CCV and TFV and SFV) as the input vector was about 93.9%, and its training time and prediction time were 320 and 0.12 s, respectively. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)
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Article
3D Point Cloud on Semantic Information for Wheat Reconstruction
Agriculture 2021, 11(5), 450; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11050450 - 16 May 2021
Viewed by 836
Abstract
Phenotypic analysis has always played an important role in breeding research. At present, wheat phenotypic analysis research mostly relies on high-precision instruments, which make the cost higher. Thanks to the development of 3D reconstruction technology, the reconstructed wheat 3D model can also be [...] Read more.
Phenotypic analysis has always played an important role in breeding research. At present, wheat phenotypic analysis research mostly relies on high-precision instruments, which make the cost higher. Thanks to the development of 3D reconstruction technology, the reconstructed wheat 3D model can also be used for phenotypic analysis. In this paper, a method is proposed to reconstruct wheat 3D model based on semantic information. The method can generate the corresponding 3D point cloud model of wheat according to the semantic description. First, an object detection algorithm is used to detect the characteristics of some wheat phenotypes during the growth process. Second, the growth environment information and some phenotypic features of wheat are combined into semantic information. Third, text-to-image algorithm is used to generate the 2D image of wheat. Finally, the wheat in the 2D image is transformed into an abstract 3D point cloud and obtained a higher precision point cloud model using a deep learning algorithm. Extensive experiments indicate that the method reconstructs 3D models and has a heuristic effect on phenotypic analysis and breeding research by deep learning. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)
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Article
Classification of Amanita Species Based on Bilinear Networks with Attention Mechanism
Agriculture 2021, 11(5), 393; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11050393 - 26 Apr 2021
Cited by 2 | Viewed by 650
Abstract
The accurate classification of Amanita is helpful to its research on biological control and medical value, and it can also prevent mushroom poisoning incidents. In this paper, we constructed the Bilinear convolutional neural networks (B-CNN) with attention mechanism model based on transfer learning [...] Read more.
The accurate classification of Amanita is helpful to its research on biological control and medical value, and it can also prevent mushroom poisoning incidents. In this paper, we constructed the Bilinear convolutional neural networks (B-CNN) with attention mechanism model based on transfer learning to realize the classification of Amanita. When the model is trained, the weight on ImageNet is used for pre-training, and the Adam optimizer is used to update network parameters. In the test process, images of Amanita at different growth stages were used to further test the generalization ability of the model. After comparing our model with other models, the results show that our model greatly reduces the number of parameters while achieving high accuracy (95.2%) and has good generalization ability. It is an efficient classification model, which provides a new option for mushroom classification in areas with limited computing resources. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)
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Article
Assessment of the Content of Dry Matter and Dry Organic Matter in Compost with Neural Modelling Methods
Agriculture 2021, 11(4), 307; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11040307 - 01 Apr 2021
Cited by 1 | Viewed by 578
Abstract
Neural image analysis is commonly used to solve scientific problems of biosystems and mechanical engineering. The method has been applied, for example, to assess the quality of foodstuffs such as fruit and vegetables, cereal grains, and meat. The method can also be used [...] Read more.
Neural image analysis is commonly used to solve scientific problems of biosystems and mechanical engineering. The method has been applied, for example, to assess the quality of foodstuffs such as fruit and vegetables, cereal grains, and meat. The method can also be used to analyse composting processes. The scientific problem lets us formulate the research hypothesis: it is possible to identify representative traits of the image of composted material that are necessary to create a neural model supporting the process of assessment of the content of dry matter and dry organic matter in composted material. The effect of the research is the identification of selected features of the composted material and the methods of neural image analysis resulted in a new original method enabling effective assessment of the content of dry matter and dry organic matter. The content of dry matter and dry organic matter can be analysed by means of parameters specifying the colour of compost. The best developed neural models for the assessment of the content of dry matter and dry organic matter in compost are: in visible light RBF 19:19-2-1:1 (test error 0.0922) and MLP 14:14-14-11-1:1 (test error 0.1722), in mixed light RBF 30:30-8-1:1 (test error 0.0764) and MLP 7:7-9-7-1:1 (test error 0.1795). The neural models generated for the compost images taken in mixed light had better qualitative characteristics. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)
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Article
Multi-Feature Patch-Based Segmentation Technique in the Gray-Centered RGB Color Space for Improved Apple Target Recognition
Agriculture 2021, 11(3), 273; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture11030273 - 22 Mar 2021
Cited by 1 | Viewed by 750
Abstract
In the vision system of apple-picking robots, the main challenge is to rapidly and accurately identify the apple targets with varying halation and shadows on their surfaces. To solve this problem, this study proposes a novel, multi-feature, patch-based apple image segmentation technique using [...] Read more.
In the vision system of apple-picking robots, the main challenge is to rapidly and accurately identify the apple targets with varying halation and shadows on their surfaces. To solve this problem, this study proposes a novel, multi-feature, patch-based apple image segmentation technique using the gray-centered red-green-blue (RGB) color space. The developed method presents a multi-feature selection process, which eliminates the effect of halation and shadows in apple images. By exploring all the features of the image, including halation and shadows, in the gray-centered RGB color space, the proposed algorithm, which is a generalization of K-means clustering algorithm, provides an efficient target segmentation result. The proposed method is tested on 240 apple images. It offered an average accuracy rate of 98.79%, a recall rate of 99.91%, an F1 measure of 99.35%, a false positive rate of 0.04%, and a false negative rate of 1.18%. Compared with the classical segmentation methods and conventional clustering algorithms, as well as the popular deep-learning segmentation algorithms, the proposed method can perform with high efficiency and accuracy to guide robotic harvesting. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)
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Article
Identification Process of Selected Graphic Features Apple Tree Pests by Neural Models Type MLP, RBF and DNN
Agriculture 2020, 10(6), 218; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture10060218 - 10 Jun 2020
Cited by 4 | Viewed by 996
Abstract
In this paper, the classification capabilities of perceptron and radial neural networks are compared using the identification of selected pests feeding in apple tree orchards in Poland as an example. The goal of the study was the neural separation of five selected apple [...] Read more.
In this paper, the classification capabilities of perceptron and radial neural networks are compared using the identification of selected pests feeding in apple tree orchards in Poland as an example. The goal of the study was the neural separation of five selected apple tree orchard pests. The classification was based on graphical information coded as selected characteristic features of the pests, presented in digital images. In the paper, MLP (MultiLayer Perceptrons), RBF (Radial Basis Function) and DNN (Deep Neural Networks) neural classification models are compared, generated using learning files acquired on the basis of information contained in digital photographs of five selected pests. In order to classify the pests, neural modeling methods were used, including digital image analysis techniques. The qualitative analysis of the neural models enabled the selection of optimal neuron topology that was characterized by the highest classification capability. As representative graphic features were selected five selected coefficients of shape and two defined graphical features of the classified objects. The created neuron model is dedicated as a core for computer systems supporting the decision processes occurring during apple production, particularly in the context of apple tree orchard pest protection automation. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)
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Article
Robust Cherry Tomatoes Detection Algorithm in Greenhouse Scene Based on SSD
Agriculture 2020, 10(5), 160; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture10050160 - 09 May 2020
Cited by 7 | Viewed by 1092
Abstract
The detection of cherry tomatoes in greenhouse scene is of great significance for robotic harvesting. This paper states a method based on deep learning for cherry tomatoes detection to reduce the influence of illumination, growth difference, and occlusion. In view of such greenhouse [...] Read more.
The detection of cherry tomatoes in greenhouse scene is of great significance for robotic harvesting. This paper states a method based on deep learning for cherry tomatoes detection to reduce the influence of illumination, growth difference, and occlusion. In view of such greenhouse operating environment and accuracy of deep learning, Single Shot multi-box Detector (SSD) was selected because of its excellent anti-interference ability and self-taught from datasets. The first step is to build datasets containing various conditions in greenhouse. According to the characteristics of cherry tomatoes, the image samples with illumination change, images rotation and noise enhancement were used to expand the datasets. Then training datasets were used to train and construct network model. To study the effect of base network and the input size of networks, one contrast experiment was designed on different base networks of VGG16, MobileNet, Inception V2 networks, and the other contrast experiment was conducted on changing the network input image size of 300 pixels by 300 pixels, 512 pixels by 512 pixels. Through the analysis of the experimental results, it is found that the Inception V2 network is the best base network with the average precision of 98.85% in greenhouse environment. Compared with other detection methods, this method shows substantial improvement in cherry tomatoes detection. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)
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Article
Quality Evaluation of Potato Tubers Using Neural Image Analysis Method
Agriculture 2020, 10(4), 112; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture10040112 - 04 Apr 2020
Cited by 4 | Viewed by 985
Abstract
This paper describes the research aimed at developing an effective quality assessment method for potato tubers using neural image analysis techniques. Nowadays, the methods used to identify damage and diseases are time-consuming, require specialized knowledge, and often rely on subjective judgment. This study [...] Read more.
This paper describes the research aimed at developing an effective quality assessment method for potato tubers using neural image analysis techniques. Nowadays, the methods used to identify damage and diseases are time-consuming, require specialized knowledge, and often rely on subjective judgment. This study showed the use of the developed neural model as a tool supporting the evaluation of potato tubers during the sorting process in the storage room. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)
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Review

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Review
Automatic Detection and Monitoring of Insect Pests—A Review
Agriculture 2020, 10(5), 161; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture10050161 - 09 May 2020
Cited by 27 | Viewed by 4384
Abstract
Many species of insect pests can be detected and monitored automatically. Several systems have been designed in order to improve integrated pest management (IPM) in the context of precision agriculture. Automatic detection traps have been developed for many important pests. These techniques and [...] Read more.
Many species of insect pests can be detected and monitored automatically. Several systems have been designed in order to improve integrated pest management (IPM) in the context of precision agriculture. Automatic detection traps have been developed for many important pests. These techniques and new technologies are very promising for the early detection and monitoring of aggressive and quarantine pests. The aim of the present paper is to review the techniques and scientific state of the art of the use of sensors for automatic detection and monitoring of insect pests. The paper focuses on the methods for identification of pests based in infrared sensors, audio sensors and image-based classification, presenting the different systems available, examples of applications and recent developments, including machine learning and Internet of Things. Future trends of automatic traps and decision support systems are also discussed. Full article
(This article belongs to the Special Issue Image Analysis Techniques in Agriculture)
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