Special Issue "Electronics for Agriculture"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 December 2022 | Viewed by 22375

Special Issue Editors

Prof. Dr. Simon Pearson
E-Mail Website
Guest Editor
Lincoln Institute for Agri-Food Technology, University of Lincoln, Lincoln LN6 7TS, UK
Interests: agricultural robotics and automation; environmental physiology of fresh produce and ornamental crops; modified atmosphere packaging; farm decision support systems
Dr. Ye Liu
E-Mail Website
Guest Editor
School of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
Interests: agricultural internet of things; wireless sensor networks; energy harvesting; embedded machine learning

Special Issue Information

Dear Colleagues,

Electronics plays an important role in all fields, especially during the wave of the fourth industrial revolution (Industry 4.0). For example, ubiquitous sensing can be achieved through remote sensing, wireless sensor networks, drone swarm, and crowdsensing. Ubiquitous computing in any device, in any location, and in any format comes true with the help of embedded computing, edge/fog computing, and cloud computing. Benefitting from artificial intelligence and big data technologies, ubiquitous intelligence makes the world smarter.

Human society has experienced three agricultural revolutions from traditional indigenous farming to mechanized farming, and elementary smart agriculture more recently. However, the agriculture industry at the current stage still faces many challenges, including global food security, ecological and public health problems, animal welfare, lack of digitization, lack of intelligence, food safety, inefficient supply chain management, etc.

The above challenges are expected to be addressed by applying cutting-edge electronic and information technologies into agriculture toward the fourth agricultural revolution (Agriculture 4.0). This has attracted unprecedented attention from governments, industry, and academia all over the world. The objective of this Special Issue is to provide a forum for researchers from diverse interdisciplinary areas to present their latest achievements in smart agriculture.

Prof. Dr. Lei Shu
Prof. Dr. Simon Pearson
Dr. Ye Liu
Dr. Mohamed Amine Ferrag
Dr. Leandros Maglaras
Guest Editors

Manuscript Submission Information

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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. Electronics is an international peer-reviewed open access semimonthly 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

  • New electronic equipment and device for new smart agricultural application
  • Industry 4.0 technologies for Agriculture 4.0 (agronomy, horticulture, forestry, aquaculture, livestock farming, etc.)
  • Internet of Things in agriculture (WSNs, UAVs, remote sensing, crowdsensing, etc.)
  • Artificial intelligence in agriculture (artificial neural networks, deep learning, machine vision, image recognition, distributed computing, parallel computing, federated learning, etc.)
  • Robotics and autonomous systems in agriculture
  • Big data analytics in agriculture (data fusion, data mining, knowledge extraction, etc.)
  • Fault diagnosis during agricultural production and harvesting
  • Security and privacy preserving in the Agricultural Internet of Things (threats and attack models, cryptographic mechanisms, anonymity and secret sharing, secure mathematical models, privacy-preserving technology, blockchain technology, authentication and access control, etc.)
  • Smart agricultural applications (plant phenotype, automated flower picking, meat quality analysis, plant disease recognition, three-dimensional reconstruction, rice seed classification, air quality monitoring and control, analysis of poultry behavior, etc.)

Published Papers (17 papers)

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Research

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Article
Detection of Impurity Rate of Machine-Picked Cotton Based on Improved Canny Operator
Electronics 2022, 11(7), 974; https://doi.org/10.3390/electronics11070974 - 22 Mar 2022
Viewed by 467
Abstract
Aiming at the real-time detection of the impurity rate in machine-picked cotton processing, a detection method for the impurity rate in machine-picked cotton was proposed based on an improved Canny operator. According to the characteristics of different saturations between cotton and impurities, the [...] Read more.
Aiming at the real-time detection of the impurity rate in machine-picked cotton processing, a detection method for the impurity rate in machine-picked cotton was proposed based on an improved Canny operator. According to the characteristics of different saturations between cotton and impurities, the impurities were separated by extracting the image S channel. Due to problems existing in the traditional Canny operator’s edge detection, the Gaussian filter was replaced by employing mean filtering and nonlocal mean denoising, which could effectively remove the noise in the image. A YOLO V5 neural network was used to classify and identify the impurities after segmentation, and the densities of various impurities were measured. The volume–weight (V–W) model was established to solve the impurity rate based on mass. Compared with a single thread, the data processing time was shortened by 43.65%, and the frame rate was effectively improved by using multithread technology. By solving the average value of the impurity rate, the anti-interference performance of the algorithm was enhanced, and has the characteristics of real-time detection and stability. This method solved the problems of low speed, poor real-time detection, and ease of interference, and can be used to guide the cotton production process. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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Article
Methodology to Differentiate Legume Species in Intercropping Agroecosystems Based on UAV with RGB Camera
Electronics 2022, 11(4), 609; https://doi.org/10.3390/electronics11040609 - 16 Feb 2022
Viewed by 547
Abstract
Mixed crops are one of the fundamental pillars of agroecological practices. Row intercropping is one of the mixed cropping options based on the combination of two or more species to reduce their impacts. Nonetheless, from a monitoring perspective, the coexistence of different species [...] Read more.
Mixed crops are one of the fundamental pillars of agroecological practices. Row intercropping is one of the mixed cropping options based on the combination of two or more species to reduce their impacts. Nonetheless, from a monitoring perspective, the coexistence of different species with different characteristics complicates some processes, requiring a series of adaptations. This article presents the initial development of a procedure that differentiates between chickpea, lentil, and ervil in an intercropping agroecosystem. The images have been taken with a drone at the height of 12 and 16 m and include the three crops in the same photograph. The Vegetation Index and Soil Index are used and combined. After generating the index, aggregation techniques are used to minimize false positives and false negatives. Our results indicate that it is possible to differentiate between the three crops, with the difference between the chickpea and the other two legume species clearer than that between the lentil and the ervil in images gathered at 16 m. The accuracy of the proposed methodology is 95% for chickpea recognition, 86% for lentils, and 60% for ervil. This methodology can be adapted to be applied in other crop combinations to improve the detection of abnormal plant vigour in intercropping agroecosystems. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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Article
Optimized Deep Learning Algorithms for Tomato Leaf Disease Detection with Hardware Deployment
Electronics 2022, 11(1), 140; https://doi.org/10.3390/electronics11010140 - 03 Jan 2022
Cited by 1 | Viewed by 633
Abstract
Smart agriculture has taken more attention during the last decade due to the bio-hazards of climate change impacts, extreme weather events, population explosion, food security demands and natural resources shortage. The Egyptian government has taken initiative in dealing with plants diseases especially tomato [...] Read more.
Smart agriculture has taken more attention during the last decade due to the bio-hazards of climate change impacts, extreme weather events, population explosion, food security demands and natural resources shortage. The Egyptian government has taken initiative in dealing with plants diseases especially tomato which is one of the most important vegetable crops worldwide that are affected by many diseases causing high yield loss. Deep learning techniques have become the main focus in the direction of identifying tomato leaf diseases. This study evaluated different deep learning models pre-trained on ImageNet dataset such as ResNet50, InceptionV3, AlexNet, MobileNetV1, MobileNetV2 and MobileNetV3.To the best of our knowledge MobileNetV3 has not been tested on tomato leaf diseases. Each of the former deep learning models has been evaluated and optimized with different techniques. The evaluation shows that MobileNetV3 Small has achieved an accuracy of 98.99% while MobileNetV3 Large has achieved an accuracy of 99.81%. All models have been deployed on a workstation to evaluate their performance by calculating the prediction time on tomato leaf images. The models were also deployed on a Raspberry Pi 4 in order to build an Internet of Things (IoT) device capable of tomato leaf disease detection. MobileNetV3 Small had a latency of 66 ms and 251 ms on the workstation and the Raspberry Pi 4, respectively. On the other hand, MobileNetV3 Large had a latency of 50 ms on the workstation and 348 ms on the Raspberry Pi 4. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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Article
Environmental Perception Q-Learning to Prolong the Lifetime of Poultry Farm Monitoring Networks
Electronics 2021, 10(23), 3024; https://doi.org/10.3390/electronics10233024 - 03 Dec 2021
Viewed by 451
Abstract
The reduction of the effects of heat-stress phenomena on poultry health and energy conservation of poultry farm monitoring networks are highly related problems. To address these problems, we propose environmental perception Q-learning (EPQL) to prolong the lifetime of poultry farm monitoring networks. EPQL [...] Read more.
The reduction of the effects of heat-stress phenomena on poultry health and energy conservation of poultry farm monitoring networks are highly related problems. To address these problems, we propose environmental perception Q-learning (EPQL) to prolong the lifetime of poultry farm monitoring networks. EPQL consists of an environmental-perception module and Q-learning. According to the temperature and humidity model of heat stress, an environmental-perception module determines the transmission rate, while Q-learning adjusts the transmission rate according to the success rate of packet transmission and the remaining energy. In real-world tests, our poultry farm monitoring networks used only about 8% of energy in a month. The real-time information of these monitoring networks was available on smartphones. In laboratory tests, compared with CSMA/CA (23.67 days), S-MAC (109.37 days), and T-MAC (252.79 days) under real systems with 2000 mAh battery, the battery-life performance of EPQL (436.48 days) was better. Moreover, EPQL reduces the packet loss rate by about 60% while simultaneously decreasing the average delay by about 20%. Generally, based on the framework of EPQL, the implemented temperature and humidity model of heat stress for poultry could be replaced by other models to extend its applicability range. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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Article
Recognition of Rice Sheath Blight Based on a Backpropagation Neural Network
Electronics 2021, 10(23), 2907; https://doi.org/10.3390/electronics10232907 - 24 Nov 2021
Cited by 2 | Viewed by 401
Abstract
Rice sheath blight is one of the main diseases in rice production. The traditional detection method, which needs manual recognition, is usually inefficient and slow. In this study, a recognition method for identifying rice sheath blight based on a backpropagation (BP) neural network [...] Read more.
Rice sheath blight is one of the main diseases in rice production. The traditional detection method, which needs manual recognition, is usually inefficient and slow. In this study, a recognition method for identifying rice sheath blight based on a backpropagation (BP) neural network is posed. Firstly, the sample image is smoothed by median filtering and histogram equalization, and the edge of the lesion is segmented using a Sobel operator, which largely reduces the background information and significantly improves the image quality. Then, the corresponding feature parameters of the image are extracted based on color and texture features. Finally, a BP neural network is built for training and testing with excellent tunability and easy optimization. The results demonstrate that when the number of hidden layer nodes is set to 90, the recognition accuracy of the BP neural network can reach up to 85.8%. Based on the color and texture features of the rice sheath blight image, the recognition algorithm constructed with a BP neural network has high accuracy and can effectively make up for the deficiency of manual recognition. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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Article
Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method
Electronics 2021, 10(22), 2813; https://doi.org/10.3390/electronics10222813 - 16 Nov 2021
Cited by 1 | Viewed by 399
Abstract
Accurately extracting the grape cluster at the front of overlapping grape clusters is the primary problem of the grape-harvesting robot. To solve the difficult problem of identifying and segmenting the overlapping grape clusters in the cultivation environment of a trellis, a simple method [...] Read more.
Accurately extracting the grape cluster at the front of overlapping grape clusters is the primary problem of the grape-harvesting robot. To solve the difficult problem of identifying and segmenting the overlapping grape clusters in the cultivation environment of a trellis, a simple method based on the deep learning network and the idea of region growing is proposed. Firstly, the region of grape in an RGB image was obtained by the finely trained DeepLabV3+ model. The idea of transfer learning was adopted when training the network with a limited number of training sets. Then, the corresponding region of the grape in the depth image captured by RealSense D435 was processed by the proposed depth region growing algorithm (DRG) to extract the front cluster. The depth region growing method uses the depth value instead of gray value to achieve clustering. Finally, it fils the holes in the clustered region of interest, extracts the contours, and maps the obtained contours to the RGB image. The images captured by RealSense D435 in a natural trellis environment were adopted to evaluate the performance of the proposed method. The experimental results showed that the recall and precision of the proposed method were 89.2% and 87.5%, respectively. The demonstrated performance indicated that the proposed method could satisfy the requirements of practical application for robotic grape harvesting. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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Article
A WiFi-Based Sensor Network for Flood Irrigation Control in Agriculture
Electronics 2021, 10(20), 2454; https://doi.org/10.3390/electronics10202454 - 10 Oct 2021
Cited by 1 | Viewed by 803
Abstract
The role of agriculture in society is vital due to factors such as providing food for the population, is a major source of employment worldwide, and one of the most important sources of revenue for countries. Furthermore, in recent years, the interest in [...] Read more.
The role of agriculture in society is vital due to factors such as providing food for the population, is a major source of employment worldwide, and one of the most important sources of revenue for countries. Furthermore, in recent years, the interest in optimizing the use of water resources has increased due to aspects such as climate change. This has led to the introduction of technology in the fields by means of sensor networks that allow remote monitoring and control of cultivated lands. In this paper, we present a system for flood irrigation in agriculture comprised of a sensor network based on WiFi communication. Different sensors measure atmospheric parameters such as temperature, humidity, and rain, soil parameters such as humidity, and water parameters such as water temperature, salinity, and water height to decide on the need of activating the floodgates for irrigation. The user application displays the data gathered by the sensors, shows a graphical representation of the state of irrigation of each ditch, and allows farmers to manage the irrigation of their fields. Finally, different tests were performed on a plot of vegetables to evaluate the correct performance of the system and the coverage of the sensor network on a vegetated area with different deployment options. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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Article
Soil Erosion Prediction Based on Moth-Flame Optimizer-Evolved Kernel Extreme Learning Machine
Electronics 2021, 10(17), 2115; https://doi.org/10.3390/electronics10172115 - 31 Aug 2021
Cited by 1 | Viewed by 715
Abstract
Soil erosion control is a complex, integrated management process, constructed based on unified planning by adjusting the land use structure, reasonably configuring engineering, plant, and farming measures to form a complete erosion control system, while meeting the laws of soil erosion, economic and [...] Read more.
Soil erosion control is a complex, integrated management process, constructed based on unified planning by adjusting the land use structure, reasonably configuring engineering, plant, and farming measures to form a complete erosion control system, while meeting the laws of soil erosion, economic and social development, and ecological and environmental security. The accurate prediction and quantitative forecasting of soil erosion is a critical reference indicator for comprehensive erosion control. This paper applies a new swarm intelligence optimization algorithm to the soil erosion classification and prediction problem, based on an enhanced moth-flame optimizer with sine–cosine mechanisms (SMFO). It is used to improve the exploration and detection capability by using the positive cosine strategy, meanwhile, to optimize the penalty parameter and the kernel parameter of the kernel extreme learning machine (KELM) for the rainfall-induced soil erosion classification prediction problem, to obtain more-accurate soil erosion classifications and the prediction results. In this paper, a dataset of the Vietnam Son La province was used for the model evaluation and testing, and the experimental results show that this SMFO-KELM method can accurately predict the results, with significant advantages in terms of classification accuracy (ACC), Mathews correlation coefficient (MCC), sensitivity (sensitivity), and specificity (specificity). Compared with other optimizer models, the adopted method is more suitable for the accurate classification of soil erosion, and can provide new solutions for natural soil supply capacity analysis, integrated erosion management, and environmental sustainability judgment. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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Article
A Real-Time Detection Algorithm for Kiwifruit Defects Based on YOLOv5
Electronics 2021, 10(14), 1711; https://doi.org/10.3390/electronics10141711 - 17 Jul 2021
Cited by 11 | Viewed by 3966
Abstract
Defect detection is the most important step in the postpartum reprocessing of kiwifruit. However, there are some small defects difficult to detect. The accuracy and speed of existing detection algorithms are difficult to meet the requirements of real-time detection. For solving these problems, [...] Read more.
Defect detection is the most important step in the postpartum reprocessing of kiwifruit. However, there are some small defects difficult to detect. The accuracy and speed of existing detection algorithms are difficult to meet the requirements of real-time detection. For solving these problems, we developed a defect detection model based on YOLOv5, which is able to detect defects accurately and at a fast speed. The main contributions of this research are as follows: (1) a small object detection layer is added to improve the model’s ability to detect small defects; (2) we pay attention to the importance of different channels by embedding SELayer; (3) the loss function CIoU is introduced to make the regression more accurate; (4) under the prerequisite of no increase in training cost, we train our model based on transfer learning and use the CosineAnnealing algorithm to improve the effect. The results of the experiment show that the overall performance of the improved network YOLOv5-Ours is better than the original and mainstream detection algorithms. The [email protected] of YOLOv5-Ours has reached 94.7%, which was an improvement of nearly 9%, compared to the original algorithm. Our model only takes 0.1 s to detect a single image, which proves the effectiveness of the model. Therefore, YOLOv5-Ours can well meet the requirements of real-time detection and provides a robust strategy for the kiwi flaw detection system. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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Article
Intelligent Measurement of Morphological Characteristics of Fish Using Improved U-Net
Electronics 2021, 10(12), 1426; https://doi.org/10.3390/electronics10121426 - 14 Jun 2021
Cited by 1 | Viewed by 813
Abstract
In the smart mariculture, batch testing of breeding traits is a key issue in the breeding of improved fish varieties. The body length (BL), body width (BW) and body area (BA) features of fish are important indicators. They are of great significance in [...] Read more.
In the smart mariculture, batch testing of breeding traits is a key issue in the breeding of improved fish varieties. The body length (BL), body width (BW) and body area (BA) features of fish are important indicators. They are of great significance in breeding, feeding and classification. To accurately and intelligently obtain the morphological characteristic sizes of fish in actual scenes, data augmentation is first used to greatly expand the published fish dataset, thereby ensuring the robustness of the training model. Then, an improved U-net segmentation and measurement algorithm is proposed, which uses a dilated convolution with a dilation rate 2 and a convolution to partially replace the convolution in the original U-net. This operation can enlarge the partial convolution receptive field and achieve more accurate segmentation for large targets in the scene. Finally, a line fitting method based on the least squares method is proposed, which is combined with the body shape features of fish and can accurately measure the BL and BW of inclined fish. Experimental results show that the Mean Intersection over Union (mIoU) is 97.6% and the average relative error of the area is 0.69%. Compared with the unimproved U-net, the average relative error of the area is reduced to about half. Moreover, with the improved U-net and the line fitting method, the average relative error of BL and the average relative error of BW of inclined fish decrease to 0.37% and 0.61%, respectively. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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Article
Design and Implementation of a Hydroponic Strawberry Monitoring and Harvesting Timing Information Supporting System Based on Nano AI-Cloud and IoT-Edge
Electronics 2021, 10(12), 1400; https://doi.org/10.3390/electronics10121400 - 10 Jun 2021
Cited by 3 | Viewed by 1002
Abstract
The strawberry market in South Korea is actually the largest market among horticultural crops. Strawberry cultivation in South Korea changed from field cultivation to facility cultivation in order to increase production. However, the decrease in production manpower due to aging is increasing the [...] Read more.
The strawberry market in South Korea is actually the largest market among horticultural crops. Strawberry cultivation in South Korea changed from field cultivation to facility cultivation in order to increase production. However, the decrease in production manpower due to aging is increasing the demand for the automation of strawberry cultivation. Predicting the harvest of strawberries is an important research topic, as strawberry production requires the most manpower for harvest. In addition, the growing environment has a great influence on strawberry production as hydroponic cultivation of strawberries is increasing. In this paper, we design and implement an integrated system that monitors strawberry hydroponic environmental data and determines when to harvest with the concept of IoT-Edge-AI-Cloud. The proposed monitoring system collects, stores and visualizes strawberry growing environment data. The proposed harvest decision system classifies the strawberry maturity level in images using a deep learning algorithm. The monitoring and analysis results are visualized in an integrated interface, which provides a variety of basic data for strawberry cultivation. Even if the strawberry cultivation area increases, the proposed system can be easily expanded and flexibly based on a virtualized container with the concept of IoT-Edge-AI-Cloud. The monitoring system was verified by monitoring a hydroponic strawberry environment for 4 months. In addition, the harvest decision system was verified using strawberry pictures acquired from Smart Berry Farm. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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Article
Knowledge-Based System for Crop Pests and Diseases Recognition
Electronics 2021, 10(8), 905; https://doi.org/10.3390/electronics10080905 - 10 Apr 2021
Cited by 3 | Viewed by 889
Abstract
With the rapid increase in the world’s population, there is an ever-growing need for a sustainable food supply. Agriculture is one of the pillars for worldwide food provisioning, with fruits and vegetables being essential for a healthy diet. However, in the last few [...] Read more.
With the rapid increase in the world’s population, there is an ever-growing need for a sustainable food supply. Agriculture is one of the pillars for worldwide food provisioning, with fruits and vegetables being essential for a healthy diet. However, in the last few years the worldwide dispersion of virulent plant pests and diseases has caused significant decreases in the yield and quality of crops, in particular fruit, cereal and vegetables. Climate change and the intensification of global trade flows further accentuate the issue. Integrated Pest Management (IPM) is an approach to pest control that aims at maintaining pest insects at tolerable levels, keeping pest populations below an economic injury level. Under these circumstances, the early identification of pests and diseases becomes crucial. In this work, we present the first step towards a fully fledged, semantically enhanced decision support system for IPM. The ultimate goal is to build a complete agricultural knowledge base by gathering data from multiple, heterogeneous sources and to develop a system to assist farmers in decision making concerning the control of pests and diseases. The pest classifier framework has been evaluated in a simulated environment, obtaining an aggregated accuracy of 98.8%. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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Article
Double-Threshold Segmentation of Panicle and Clustering Adaptive Density Estimation for Mature Rice Plants Based on 3D Point Cloud
Electronics 2021, 10(7), 872; https://doi.org/10.3390/electronics10070872 - 06 Apr 2021
Cited by 1 | Viewed by 744
Abstract
Crop density estimation ahead of the combine harvester provides a valuable reference for operators to keep the feeding amount stable in agriculture production, and, as a consequence, guaranteeing the working stability and improving the operation efficiency. For the current method depending on LiDAR, [...] Read more.
Crop density estimation ahead of the combine harvester provides a valuable reference for operators to keep the feeding amount stable in agriculture production, and, as a consequence, guaranteeing the working stability and improving the operation efficiency. For the current method depending on LiDAR, it is difficult to extract individual plants for mature rice plants with luxuriant branches and leaves, as well as bent and intersected panicles. Therefore, this paper proposes a clustering adaptive density estimation method based on the constructed LiDAR measurement system and double-threshold segmentation. The Otsu algorithm is adopted to construct a double-threshold according to elevation and inflection intensity in different parts of the rice plant, after reducing noise through the statistical outlier removal (SOR) algorithm. For adaptively parameter adjustment of supervoxel clustering and mean-shift clustering during density estimation, the calculation relationship between influencing factors (including seed-point size and kernel-bandwidth size) and number of points are, respectively, deduced by analysis. The experiment result of density estimation proved the two clustering methods effective, with a Root Mean Square Error (RMSE) of 9.968 and 5.877, and a Mean Absolute Percent Error (MAPE) of 5.67% and 3.37%, and the average accuracy was more than 90% and 95%, respectively. This estimation method is of positive significance for crop density measurement and could lay the foundation for intelligent harvest. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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Article
A Smartphone-Based Application for Scale Pest Detection Using Multiple-Object Detection Methods
Electronics 2021, 10(4), 372; https://doi.org/10.3390/electronics10040372 - 03 Feb 2021
Cited by 13 | Viewed by 2229
Abstract
Taiwan’s economy mainly relies on the export of agricultural products. If even the suspicion of a pest is found in the crop products after they are exported, not only are the agricultural products returned but the whole batch of crops is destroyed, resulting [...] Read more.
Taiwan’s economy mainly relies on the export of agricultural products. If even the suspicion of a pest is found in the crop products after they are exported, not only are the agricultural products returned but the whole batch of crops is destroyed, resulting in extreme crop losses. The species of mealybugs, Coccidae, and Diaspididae, which are the primary pests of the scale insect in Taiwan, can not only lead to serious damage to the plants but also severely affect agricultural production. Hence, to recognize the scale pests is an important task in Taiwan’s agricultural field. In this study, we propose an AI-based pest detection system for solving the specific issue of detection of scale pests based on pictures. Deep-learning-based object detection models, such as faster region-based convolutional networks (Faster R-CNNs), single-shot multibox detectors (SSDs), and You Only Look Once v4 (YOLO v4), are employed to detect and localize scale pests in the picture. The experimental results show that YOLO v4 achieved the highest classification accuracy among the algorithms, with 100% in mealybugs, 89% in Coccidae, and 97% in Diaspididae. Meanwhile, the computational performance of YOLO v4 has indicated that it is suitable for real-time application. Moreover, the inference results of the YOLO v4 model further help the end user. A mobile application using the trained scale pest recognition model has been developed to facilitate pest identification in farms, which is helpful in applying appropriate pesticides to reduce crop losses. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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Article
Improving Energy Efficiency of Irrigation Wells by Using an IoT-Based Platform
Electronics 2021, 10(3), 250; https://doi.org/10.3390/electronics10030250 - 22 Jan 2021
Cited by 6 | Viewed by 1464
Abstract
The irrigation sector has undergone a remarkable transformation in recent decades due to the application of pressurized water distribution technologies, improving the management of limited water resources. As a result of this transformation, irrigation has become, together with agricultural machinery, the primary consumer [...] Read more.
The irrigation sector has undergone a remarkable transformation in recent decades due to the application of pressurized water distribution technologies, improving the management of limited water resources. As a result of this transformation, irrigation has become, together with agricultural machinery, the primary consumer of energy within the agri-food sector. Furthermore, the energy cost of operating pumping equipment during a farming season represents 30–40% of the crop’s total cost. For this reason, one of the most interesting challenges in this scope is that of improving energy efficiency and reducing economic costs so that productive work become more and more competitive. Energy audit makes possible to determine the efficiency of installations, and enables to determine energy saving protocols (requirements), for this reason the aim of this article is to carry out these by using IoT-based systems. The proposed system improves decision-making on agricultural pumping management by classifying wells’ efficiency and integrating the data sets that determine their efficiency into a single information model. The system monitors energy efficiency according to different parameters such as: infrastructure, energy consumption, electric rates, manometric height or type of installation, making it possible to enhance each pumping operation’s decisions. This solution has been deployed in an irrigation community in southeast Spain whose results have warned about the lack of efficiency in two of its wells: in one of them it is proposed that they be replaced, due to the high cost of pumping water, and in the other, hydraulic mechanisms are implemented to improve the water-energy binomial. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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Article
An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in “Luogang” Orange
Electronics 2021, 10(1), 80; https://doi.org/10.3390/electronics10010080 - 04 Jan 2021
Cited by 2 | Viewed by 1018
Abstract
The objective of this study was to find an efficient method for measuring the total soluble solid content (TSSC) and water content of “Luogang” orange. Quick, accurate, and nondestructive detection tools (VIS/NIR spectroscopy, NIR spectroscopy, machine vision, and electronic nose), four data processing [...] Read more.
The objective of this study was to find an efficient method for measuring the total soluble solid content (TSSC) and water content of “Luogang” orange. Quick, accurate, and nondestructive detection tools (VIS/NIR spectroscopy, NIR spectroscopy, machine vision, and electronic nose), four data processing methods (Savitzky–Golay (SG), genetic algorithm (GA), multi-source information fusion (MIF), convolutional neural network (CNN) as the deep learning method, and a partial least squares regression (PLSR) modeling method) were compared and investigated. The results showed that the optimal TSSC detection method was based on VIS/NIR and machine vision data fusion and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the TSSC detection results were 0.8580 and 0.4276, respectively. The optimal water content detection result was based on VIS/NIR data and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the water content detection results were 0.7013 and 0.0063, respectively. This optimized method largely improved the internal quality detection accuracy of “Luogang” orange when compared to the data from a single detection tool with traditional data processing method, and provides a reference for the accuracy improvement of internal quality detection of other fruits. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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Review

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Review
A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning
Electronics 2021, 10(1), 81; https://doi.org/10.3390/electronics10010081 - 04 Jan 2021
Cited by 14 | Viewed by 2145
Abstract
Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development [...] Read more.
Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition. Consequently, PPIR technology that is based on deep learning is becoming increasingly popular. First, this paper introduces the development and application of PPIR technology, followed by its classification and analysis. Second, it presents the theory of four types of deep learning methods and their applications in PPIR. These methods include the convolutional neural network, deep belief network, recurrent neural network, and stacked autoencoder, and they are applied to identify plant species, diagnose plant diseases, etc. Finally, the difficulties and challenges of deep learning in PPIR are discussed. Full article
(This article belongs to the Special Issue Electronics for Agriculture)
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