Cucumber Fruits Detection in Greenhouses Based on Instance Segmentation

Liu, Xiaoyang; Zhao, Debin; Jia, Weikuan; Ji, Wei; Ruan, Chengzhi; Sun, Yaqi

doi:10.1109/access.2019.2942144

Cited by 83 publications

(27 citation statements)

References 25 publications

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“…Some teams labelled the collected imagery datasets based on expert knowledge and used them for phenotyping certain crop traits. In literature, several authors focused on spatial mapping of climate or digital image data for greenhouse growing crops [ 67 , 68 , 69 , 70 , 71 , 72 ]. Here are still many opportunities for automated computer vision analysis and automated control in the future.…”

Section: Discussionmentioning

confidence: 99%

Cherry Tomato Production in Intelligent Greenhouses—Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality

Hemming

Zwart

Elings

et al. 2020

Sensors

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Greenhouses and indoor farming systems play an important role in providing fresh and nutritious food for the growing global population. Farms are becoming larger and greenhouse growers need to make complex decisions to maximize production and minimize resource use while meeting market requirements. However, highly skilled labor is increasingly lacking in the greenhouse sector. Moreover, extreme events such as the COVID-19 pandemic, can make farms temporarily less accessible. This highlights the need for more autonomous and remote-control strategies for greenhouse production. This paper describes and analyzes the results of the second “Autonomous Greenhouse Challenge”. In this challenge, an experiment was conducted in six high-tech greenhouse compartments during a period of six months of cherry tomato growing. The primary goal of the greenhouse operation was to maximize net profit, by controlling the greenhouse climate and crop with AI techniques. Five international teams with backgrounds in AI and horticulture were challenged in a competition to operate their own compartment remotely. They developed intelligent algorithms and use sensor data to determine climate setpoints and crop management strategy. All AI supported teams outperformed a human-operated greenhouse that served as reference. From the results obtained by the teams and from the analysis of the different climate-crop strategies, it was possible to detect challenges and opportunities for the future implementation of remote-control systems in greenhouse production.

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Section: Discussionmentioning

confidence: 99%

Cherry Tomato Production in Intelligent Greenhouses—Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality

Hemming

Zwart

Elings

et al. 2020

Sensors

View full text Add to dashboard Cite

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“…Moreover, the estimation of the purchased product position in 3D space is expected to be as validly as possible. Although being one of the most popular recognition methods, YOLOv3 [20,38] not only has heavy computation but also needs the GPU with the highest computing ability for Compute Unified Device Architecture (CUDA). Because of this development to be implemented in small and medium enterprise (SME) shops, we decided to utilize the onboard GPU, and YOLOv2, not YOLOv3.…”

Section: Purchased Products Recognitionmentioning

confidence: 99%

An Artificial Intelligence of Things-Based Picking Algorithm for Online Shop in the Society 5.0’s Context

Muslikhin

Horng

Yang

et al. 2021

Sensors

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In this study, an Artificial Intelligence of Things (AIoT)-based automated picking system was proposed for the development of an online shop and the services for automated shipping systems. Speed and convenience are two key points in Industry 4.0 and Society 5.0. In the context of online shopping, speed and convenience can be provided by integrating e-commerce platforms with AIoT systems and robots that are following consumers’ needs. Therefore, this proposed system diverts consumers who are moved by AIoT, while robotic manipulators replace human tasks to pick. To prove this idea, we implemented a modified YOLO (You Only Look Once) algorithm as a detection and localization tool for items purchased by consumers. At the same time, the modified YOLOv2 with data-driven mode was used for the process of taking goods from unstructured shop shelves. Our system performance is proven by experiments to meet the expectations in evaluating efficiency, speed, and convenience of the system in Society 5.0’s context.

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“…Finally, 12 anchors with different sizes are generated. We follow the design of anchor scales over different layers, which ensure that anchors of different sizes can have the same density on the image [27]. In this study, the largest IoU values and the samples with IoU > 0.5 are selected as positive samples.…”

Section: B Multi-layer Input Rpn Networkmentioning

confidence: 99%

Northern Maize Leaf Blight Detection Under Complex Field Environment Based on Deep Learning

et al. 2020

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Northern maize leaf blight is one of the major diseases that endanger the health of maize. The complex background of the field and different light intensity make the detection of diseases more difficult. A multi-scale feature fusion instance detection method, based on convolutional neural network, is proposed to detect maize leaf blight. The proposed technique incorporates three major steps of data set preprocessing part, fine-tuning network and detection module. In the first step, the improved retinex is used to process data sets, which successfully solves the problem of poor detection effects caused by high-intensity light. In the second step, the improved RPN is utilized to adjust the anchor box of diseased leaves. The improved RPN network identifies and deletes negative anchors, which reduces the search space of the classifier and provides better initial information for the detection network. In this paper, a transmission module is designed to connect the fine-tuning network with the detection module. On the one hand, the transmission module fuses the features of the low-level and high-level to improve the detection accuracy of small target diseases. On the other hand, the transmission module converts the feature map associated with the fine-tuning network to the detection module, thus realizing the feature sharing between the detection module and the fine-tuning network. In the third step, the detection module takes the optimized anchor as input, focuses on detecting the diseased leaves. By sharing the features of the transmission module, the time-consuming process of using candidate regions layer by layer to detect is eliminated. Therefore, the efficiency of the whole model has reached the efficiency of the one-stage model. In order to further optimize the detection effect of the model, we replace the loss function with generalized intersection over union (GIoU). After 60000 iterations, the highest mean average precision (mAP) reaches 91.83%. The experimental results indicate that the improved model outperforms several existing methods in terms of greater precision and frames per second (FPS). INDEX TERMS Northern maize leaf blight, disease detection, transmission module, retinex, single shot multiBox detector (SSD). I. INTRODUCTION Maize is one of the major food crops in the world. The planting area and output of maize in the world are only lower than that of wheat and rice [1]. In addition to be an excellent feed for animal husbandry, maize is also an important raw material for the development of light industrial products. The associate editor coordinating the review of this manuscript and approving it for publication was Liandong Zhu.

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Cucumber Fruits Detection in Greenhouses Based on Instance Segmentation

Cited by 83 publications

References 25 publications

Cherry Tomato Production in Intelligent Greenhouses—Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality

Cherry Tomato Production in Intelligent Greenhouses—Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality

An Artificial Intelligence of Things-Based Picking Algorithm for Online Shop in the Society 5.0’s Context

Northern Maize Leaf Blight Detection Under Complex Field Environment Based on Deep Learning

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