Electronics

2024

Jump to: 2023, 2022, 2021

23 pages, 2582 KiB

Open AccessReview

Smart Agriculture and Greenhouse Gas Emission Mitigation: A 6G-IoT Perspective

by Sofia Polymeni, Dimitrios N. Skoutas, Panagiotis Sarigiannidis, Georgios Kormentzas and Charalabos Skianis

Electronics 2024, 13(8), 1480; https://doi.org/10.3390/electronics13081480 - 13 Apr 2024

Cited by 4 | Viewed by 1814

Abstract

Smart farming has emerged as a promising approach to address the agriculture industry’s significant contribution to greenhouse gas (GHG) emissions. However, the effectiveness of current smart farming practices in mitigating GHG emissions remains a matter of ongoing debate. This review paper provides an [...] Read more.

Smart farming has emerged as a promising approach to address the agriculture industry’s significant contribution to greenhouse gas (GHG) emissions. However, the effectiveness of current smart farming practices in mitigating GHG emissions remains a matter of ongoing debate. This review paper provides an in-depth examination of the current state of GHG emissions in smart farming, highlighting the limitations of existing practices in reducing GHG emissions and introducing innovative strategies that leverage the advanced capabilities of 6G-enabled IoT (6G-IoT). By enabling precise resource management, facilitating emission source identification and mitigation, and enhancing advanced emission reduction techniques, 6G-IoT integration offers a transformative solution for managing GHG emissions in agriculture. However, while smart agriculture focuses on technological applications for immediate efficiency gains, it also serves as a crucial component of sustainable agriculture by providing the tools necessary for long-term environmental supervision and resource sustainability. As a result, this study also contributes to sustainable agriculture by providing insights and guiding future advancements in smart farming, particularly in the context of 6G-IoT, to develop more effective GHG mitigation strategies for smart farming applications, promoting a more sustainable agricultural future. Full article

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25 pages, 815 KiB

Open AccessArticle

Enhancing Safety in IoT Systems: A Model-Based Assessment of a Smart Irrigation System Using Fault Tree Analysis

by Alhassan Abdulhamid, Md Mokhlesur Rahman, Sohag Kabir and Ibrahim Ghafir

Electronics 2024, 13(6), 1156; https://doi.org/10.3390/electronics13061156 - 21 Mar 2024

Cited by 2 | Viewed by 1674

Abstract

The agricultural industry has the potential to undergo a revolutionary transformation with the use of Internet of Things (IoT) technology. Crop monitoring can be improved, waste reduced, and efficiency increased. However, there are risks associated with system failures that can lead to significant [...] Read more.

The agricultural industry has the potential to undergo a revolutionary transformation with the use of Internet of Things (IoT) technology. Crop monitoring can be improved, waste reduced, and efficiency increased. However, there are risks associated with system failures that can lead to significant losses and food insecurity. Therefore, a proactive approach is necessary to ensure the effective safety assessment of new IoT systems before deployment. It is crucial to identify potential causes of failure and their severity from the conceptual design phase of the IoT system within smart agricultural ecosystems. This will help prevent such risks and ensure the safety of the system. This study examines the failure behaviour of IoT-based Smart Irrigation Systems (SIS) to identify potential causes of failure. This study proposes a comprehensive Model-Based Safety Analysis (MBSA) framework to model the failure behaviour of SIS and generate analysable safety artefacts of the system using System Modelling Language (SysML). The MBSA approach provides meticulousness to the analysis, supports model reuse, and makes the development of a Fault Tree Analysis (FTA) model easier, thereby reducing the inherent limitations of informal system analysis. The FTA model identifies component failures and their propagation, providing a detailed understanding of how individual component failures can lead to the overall failure of the SIS. This study offers valuable insights into the interconnectedness of various component failures by evaluating the SIS failure behaviour through the FTA model. This study generates multiple minimal cut sets, which provide actionable insights into designing dependable IoT-based SIS. This analysis identifies potential weak points in the design and provides a foundation for safety risk mitigation strategies. This study emphasises the significance of a systematic and model-driven approach to improving the dependability of IoT systems in agriculture, ensuring sustainable and safe implementation. Full article

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2023

Jump to: 2024, 2022, 2021

24 pages, 7291 KiB

Open AccessReview

The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review

by Sofia Polymeni, Stefanos Plastras, Dimitrios N. Skoutas, Georgios Kormentzas and Charalabos Skianis

Electronics 2023, 12(12), 2651; https://doi.org/10.3390/electronics12122651 - 13 Jun 2023

Cited by 45 | Viewed by 5793

Abstract

Throughout human history, agriculture has undergone a series of progressive transformations based on ever-evolving technologies in an effort to increase productivity and profitability. Over the years, farming methods have evolved significantly, progressing from Agriculture 1.0, which relied on primitive tools, to Agriculture 2.0, [...] Read more.

Throughout human history, agriculture has undergone a series of progressive transformations based on ever-evolving technologies in an effort to increase productivity and profitability. Over the years, farming methods have evolved significantly, progressing from Agriculture 1.0, which relied on primitive tools, to Agriculture 2.0, which incorporated machinery and advanced farming practices, and subsequently to Agriculture 3.0, which emphasized mechanization and employed intelligent machinery and technology to enhance productivity levels. To further automate and increase agricultural productivity while minimizing agricultural inputs and pollutants, a new approach to agricultural management based on the concepts of the fourth industrial revolution is being embraced gradually. This approach is referred to as “Agriculture 4.0” and is mainly implemented through the use of Internet of Things (IoT) technologies, enabling the remote control of sensors and actuators and the efficient collection and transfer of data. In addition, fueled by technologies such as robotics, artificial intelligence, quantum sensing, and four-dimensional communication, a new form of smart agriculture, called “Agriculture 5.0,” is now emerging. Agriculture 5.0 can exploit the growing 5G network infrastructure as a basis. However, only 6G-IoT networks will be able to offer the technological advances that will allow the full expansion of Agriculture 5.0, as can be inferred from the relevant scientific literature and research. In this article, we first introduce the scope of Agriculture 5.0 as well as the key features and technologies that will be leveraged in the much-anticipated 6G-IoT communication systems. We then highlight the importance and influence of these developing technologies in the further advancement of smart agriculture and conclude with a discussion of future challenges and opportunities. Full article

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21 pages, 3158 KiB

Open AccessArticle

Sustainable and Intelligent Phytoprotection in Photovoltaic Agriculture: New Challenges and Opportunities

by Kai Huang, Lei Shu, Kailiang Li, Yuejie Chen, Yan Zhu and Ravi Valluru

Electronics 2023, 12(5), 1221; https://doi.org/10.3390/electronics12051221 - 3 Mar 2023

Cited by 6 | Viewed by 2149

Abstract

Photovoltaic Agriculture (PA) is a new management system combining industry with modern agriculture that can effectively reduce the competition for limited land resource usage between electric power production and agricultural production. However, PA has been facing the challenge of managing plant protection measures [...] Read more.

Photovoltaic Agriculture (PA) is a new management system combining industry with modern agriculture that can effectively reduce the competition for limited land resource usage between electric power production and agricultural production. However, PA has been facing the challenge of managing plant protection measures because it is difficult to monitor plants grown under the photovoltaic panels by remote sensing satellites and pesticide applications using drones. To overcome this challenge, Solar Insecticidal Lamps (SILs) can be used for phytoprotection in PA. However, to effectively use SILs in PA, it is important to identify a suitable field location to maintain strong wireless communication signals. In this paper, two testbeds were designed and a series of experiments in PA was performed. The results indicate that there is considerable interference exists around the confluence box. A higher interference seriously reduces the Packet Reception Rate (PRR) of the nearby node, which is an important constraint for deploying wireless sensors in PA. Finally, new challenges and future research opportunities are proposed. Full article

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2022

Jump to: 2024, 2023, 2021

23 pages, 7530 KiB

Open AccessFeature PaperReview

6G-Enabled Smart Agriculture: A Review and Prospect

by Fan Zhang, Yu Zhang, Weidang Lu, Yuan Gao, Yi Gong and Jiang Cao

Electronics 2022, 11(18), 2845; https://doi.org/10.3390/electronics11182845 - 8 Sep 2022

Cited by 28 | Viewed by 6285

Abstract

As human society develops, the population is growing explosively and water and land resources are gradually being exhausted due to pollution. Smart agriculture is regarded as having an essential role in addressing the above challenges. Smart agriculture can significantly improve the agro-ecological environment [...] Read more.

As human society develops, the population is growing explosively and water and land resources are gradually being exhausted due to pollution. Smart agriculture is regarded as having an essential role in addressing the above challenges. Smart agriculture can significantly improve the agro-ecological environment and the yield and quality of agricultural products, and it can reduce the usage of pesticides and chemical fertilizers, thus alleviating the pollution of farmland and improving the sustainability of agricultural activities. The key to smart agriculture is in utilizing information and communication technologies to make agricultural cultivation and production automatic and intelligent. Specifically, wireless communications play an active role in the development of agriculture, and every generation of wireless communication technology drives agriculture to a more intelligent stage. In this article, we first review the wireless technologies which have mature applications in agriculture. Moreover, it is of importance to exploit the up-to-date communication technologies to further promote agricultural development. Therefore, we have surveyed the key technologies in sixth-generation mobile communication systems, as well as their existing and potential applications in smart agriculture. Full article

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21 pages, 9130 KiB

Open AccessArticle

Improved YOLO v5 Wheat Ear Detection Algorithm Based on Attention Mechanism

by Rui Li and Yanpeng Wu

Electronics 2022, 11(11), 1673; https://doi.org/10.3390/electronics11111673 - 24 May 2022

Cited by 59 | Viewed by 7077

Abstract

The detection and counting of wheat ears are essential for crop field management, but the adhesion and obscuration of wheat ears limit detection accuracy, with problems such as false detection, missed detection, and insufficient feature extraction capability. Previous research results have shown that [...] Read more.

The detection and counting of wheat ears are essential for crop field management, but the adhesion and obscuration of wheat ears limit detection accuracy, with problems such as false detection, missed detection, and insufficient feature extraction capability. Previous research results have shown that most methods for detecting wheat ears are of two types: colour and texture extracted by machine learning methods or convolutional neural networks. Therefore, we proposed an improved YOLO v5 algorithm based on a shallow feature layer. There are two main core ideas: (1) to increase the perceptual field by adding quadruple down-sampling in the feature pyramid to improve the detection of small targets, and (2) introducing the CBAM attention mechanism into the neural network to solve the problem of gradient disappearance during training. CBAM is a model that includes both spatial and channel attention, and by adding this module, the feature extraction capability of the network can be improved. Finally, to make the model have better generalization ability, we proposed the Mosaic-8 data enhancement method, with adjusted loss function and modified regression formula for the target frame. The experimental results show that the improved algorithm has an mAP of 94.3%, an accuracy of 88.5%, and a recall of 98.1%. Compared with the relevant model, the improvement effect is noticeable. It shows that the model can effectively overcome the noise of the field environment to meet the practical requirements of wheat ear detection and counting. Full article

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23 pages, 8768 KiB

Open AccessArticle

Detection of Impurity Rate of Machine-Picked Cotton Based on Improved Canny Operator

by Chengliang Zhang, Tianhui Li and Jianyu Li

Electronics 2022, 11(7), 974; https://doi.org/10.3390/electronics11070974 - 22 Mar 2022

Cited by 9 | Viewed by 2836

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

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19 pages, 6002 KiB

Open AccessFeature PaperArticle

Methodology to Differentiate Legume Species in Intercropping Agroecosystems Based on UAV with RGB Camera

by Lorena Parra, David Mostaza-Colado, Jose F. Marin, Pedro V. Mauri and Jaime Lloret

Electronics 2022, 11(4), 609; https://doi.org/10.3390/electronics11040609 - 16 Feb 2022

Cited by 4 | Viewed by 2790

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

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19 pages, 6166 KiB

Open AccessArticle

Optimized Deep Learning Algorithms for Tomato Leaf Disease Detection with Hardware Deployment

by Hesham Tarek, Hesham Aly, Saleh Eisa and Mohamed Abul-Soud

Electronics 2022, 11(1), 140; https://doi.org/10.3390/electronics11010140 - 3 Jan 2022

Cited by 48 | Viewed by 6162

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

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2021

Jump to: 2024, 2023, 2022

13 pages, 5850 KiB

Open AccessArticle

Environmental Perception Q-Learning to Prolong the Lifetime of Poultry Farm Monitoring Networks

by Zike Wu, Pan Pan, Jieqiang Liu, Beibei Shi, Ming Yan and Hongguang Zhang

Electronics 2021, 10(23), 3024; https://doi.org/10.3390/electronics10233024 - 3 Dec 2021

Cited by 2 | Viewed by 2285

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

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12 pages, 2277 KiB

Open AccessArticle

Recognition of Rice Sheath Blight Based on a Backpropagation Neural Network

by Yi Lu, Zhiyang Li, Xiangqiang Zhao, Shuaishuai Lv, Xingxing Wang, Kaixuan Wang and Hongjun Ni

Electronics 2021, 10(23), 2907; https://doi.org/10.3390/electronics10232907 - 24 Nov 2021

Cited by 11 | Viewed by 2011

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

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21 pages, 8450 KiB

Open AccessArticle

Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method

by Yun Peng, Shengyi Zhao and Jizhan Liu

Electronics 2021, 10(22), 2813; https://doi.org/10.3390/electronics10222813 - 16 Nov 2021

Cited by 9 | Viewed by 2407

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

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29 pages, 44843 KiB

Open AccessArticle

A WiFi-Based Sensor Network for Flood Irrigation Control in Agriculture

by Jaime Lloret, Sandra Sendra, Julia García-Fernández, Laura García and Jose M. Jimenez

Electronics 2021, 10(20), 2454; https://doi.org/10.3390/electronics10202454 - 10 Oct 2021

Cited by 7 | Viewed by 4438

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

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24 pages, 3028 KiB

Open AccessArticle

Soil Erosion Prediction Based on Moth-Flame Optimizer-Evolved Kernel Extreme Learning Machine

by Chengcheng Chen, Xianchang Wang, Chengwen Wu, Majdi Mafarja, Hamza Turabieh and Huiling Chen

Electronics 2021, 10(17), 2115; https://doi.org/10.3390/electronics10172115 - 31 Aug 2021

Cited by 6 | Viewed by 3125

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

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13 pages, 8777 KiB

Open AccessArticle

A Real-Time Detection Algorithm for Kiwifruit Defects Based on YOLOv5

by Jia Yao, Jiaming Qi, Jie Zhang, Hongmin Shao, Jia Yang and Xin Li

Electronics 2021, 10(14), 1711; https://doi.org/10.3390/electronics10141711 - 17 Jul 2021

Cited by 241 | Viewed by 18930

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

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17 pages, 9569 KiB

Open AccessArticle

Intelligent Measurement of Morphological Characteristics of Fish Using Improved U-Net

by Chuang Yu, Zhuhua Hu, Bing Han, Peng Wang, Yaochi Zhao and Huaming Wu

Electronics 2021, 10(12), 1426; https://doi.org/10.3390/electronics10121426 - 14 Jun 2021

Cited by 15 | Viewed by 3281

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

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15 pages, 5123 KiB

Open AccessArticle

Design and Implementation of a Hydroponic Strawberry Monitoring and Harvesting Timing Information Supporting System Based on Nano AI-Cloud and IoT-Edge

by Sun Park and JongWon Kim

Electronics 2021, 10(12), 1400; https://doi.org/10.3390/electronics10121400 - 10 Jun 2021

Cited by 21 | Viewed by 6207

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

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21 pages, 3378 KiB

Open AccessArticle

Knowledge-Based System for Crop Pests and Diseases Recognition

by Miguel Ángel Rodríguez-García, Francisco García-Sánchez and Rafael Valencia-García

Electronics 2021, 10(8), 905; https://doi.org/10.3390/electronics10080905 - 10 Apr 2021

Cited by 22 | Viewed by 5505

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

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15 pages, 6727 KiB

Open AccessArticle

Double-Threshold Segmentation of Panicle and Clustering Adaptive Density Estimation for Mature Rice Plants Based on 3D Point Cloud

by Yixin Sun, Yusen Luo, Xiaoyu Chai, Pengpeng Zhang, Qian Zhang, Lizhang Xu and Lele Wei

Electronics 2021, 10(7), 872; https://doi.org/10.3390/electronics10070872 - 6 Apr 2021

Cited by 9 | Viewed by 2766

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

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14 pages, 16306 KiB

Open AccessArticle

A Smartphone-Based Application for Scale Pest Detection Using Multiple-Object Detection Methods

by Jian-Wen Chen, Wan-Ju Lin, Hui-Jun Cheng, Che-Lun Hung, Chun-Yuan Lin and Shu-Pei Chen

Electronics 2021, 10(4), 372; https://doi.org/10.3390/electronics10040372 - 3 Feb 2021

Cited by 86 | Viewed by 9100

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

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18 pages, 23856 KiB

Open AccessArticle

Improving Energy Efficiency of Irrigation Wells by Using an IoT-Based Platform

by Juan A. López-Morales, Juan A. Martínez and Antonio F. Skarmeta

Electronics 2021, 10(3), 250; https://doi.org/10.3390/electronics10030250 - 22 Jan 2021

Cited by 14 | Viewed by 4167

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

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19 pages, 972 KiB

Open AccessEditor’s ChoiceReview

A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

by Jianbin Xiong, Dezheng Yu, Shuangyin Liu, Lei Shu, Xiaochan Wang and Zhaoke Liu

Electronics 2021, 10(1), 81; https://doi.org/10.3390/electronics10010081 - 4 Jan 2021

Cited by 90 | Viewed by 8879

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

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12 pages, 6596 KiB

Open AccessArticle

An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in “Luogang” Orange

by Sai Xu, Huazhong Lu, Christopher Ference and Qianqian Zhang

Electronics 2021, 10(1), 80; https://doi.org/10.3390/electronics10010080 - 4 Jan 2021

Cited by 9 | Viewed by 2940

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

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