Tailored Deep Learning based Architecture for Smart Agriculture

Boukhris, Louay; Abderrazak, Jihene Ben; Besbes, Hichem

doi:10.1109/iwcmc48107.2020.9148182

Cited by 20 publications

(11 citation statements)

References 10 publications

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“…To deal with the limited processing and storing capabilities, most literature chooses to send data to a central cloud designed to manage a huge amount of data. Thing speak [89,134,137,[203][204][205][206], Ubidots [94], Blynk [207,208], OneNet [209], Amazon web services [100], GoogleColab [210,211]are examples of free IoT cloud services. The advantage of using those cloud services is the ease of integration.…”

Section: Managing Big Datamentioning

confidence: 99%

“…The high computation power and long training time of NNs shape a burden. To overcome the, papers integrates transfer learning, a technique that adjusts a model trained for a different task instead of training from scratch [211,244,253,256,257]. Transfer learning proved to obtain effective results at reduced cost using the pretrained model.…”

Section: ) Challengesmentioning

confidence: 99%

“…Images captured onsite [92,234] During-plantation Detect animals' CNN Images captured onsite [251] Detect and/or diagnose diseases NN and SVM Images generated by drones and satellites [247] Quantized CNN Online database [248] Residual Network and CNN Images captured onsite [305] CNN and TL Images captured onsite [211] TL Images captured onsite [256] Science-based None [306]…”

Section: Determine Plants' Quantity Cnnmentioning

confidence: 99%

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Technologies Driving the Shift to Smart Farming: A Review

Elbeheiry

Balog

2023

IEEE Sensors J.

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As today's agriculture industry is facing numerous challenges, including climate changes, encroachment of the urban environment and lack of qualified farmers, there is a need for new practices to ensure sustainable agriculture and food supply. Consequently, there is an emphasis on upgrading the farming practices by shifting towards Smart Farming (SF) -utilizing advanced information and communication technologies to improve the quantity and quality of the crop with minimal labor interference. SF has gained lots of interest in recent years utilizing a variety of technological innovations in the field, which imposes a challenge on farmers and technology integrators to identify the suitable technologies and best practices for a particular application. This paper provides a survey of the most recent SF scientific literature to identify common practices toward technology integration, challenges, and solutions. The survey was conducted on 588 papers published on the IEEE database following Cochrane methods to ensure appropriate analysis and interpretation of results. The papers' contributions were analyzed to identify necessary technologies that constitute SF, and consequently, research themes were identified. The identified themes are sensors, communication, big data, actuators and machines, and data analysis. Besides presenting an in-depth analysis of each identified theme, the paper discusses integrating more than one technology in systems to achieve independency. The most common SF systems are remote monitoring, autonomous, and intelligent decision-making systems.

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Section: Managing Big Datamentioning

confidence: 99%

Section: ) Challengesmentioning

confidence: 99%

Section: Determine Plants' Quantity Cnnmentioning

confidence: 99%

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Technologies Driving the Shift to Smart Farming: A Review

Elbeheiry

Balog

2023

IEEE Sensors J.

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“…Rather than the classification algorithms that merely offer each defect a class type, object detection is conducted to locate and classify the objects among the predefined classes using rectangular bounding boxes (BBs) as well as confidence scores (CSs). In recent studies, object detection technology has been increasingly applied in several fields, such as intelligent transportation [ 75 , 76 , 77 ], smart agriculture [ 78 , 79 , 80 ], and autonomous construction [ 81 , 82 , 83 ]. The generic object detection consists of the one-stage approaches and the two-stage approaches.…”

Section: Defect Inspectionmentioning

confidence: 99%

Vision-Based Defect Inspection and Condition Assessment for Sewer Pipes: A Comprehensive Survey

Wang

Dang

et al. 2022

Sensors

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Due to the advantages of economics, safety, and efficiency, vision-based analysis techniques have recently gained conspicuous advancements, enabling them to be extensively applied for autonomous constructions. Although numerous studies regarding the defect inspection and condition assessment in underground sewer pipelines have presently emerged, we still lack a thorough and comprehensive survey of the latest developments. This survey presents a systematical taxonomy of diverse sewer inspection algorithms, which are sorted into three categories that include defect classification, defect detection, and defect segmentation. After reviewing the related sewer defect inspection studies for the past 22 years, the main research trends are organized and discussed in detail according to the proposed technical taxonomy. In addition, different datasets and the evaluation metrics used in the cited literature are described and explained. Furthermore, the performances of the state-of-the-art methods are reported from the aspects of processing accuracy and speed.

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“…It was concluded that segmentation is especially useful for identifying objects in dense clusters and correctly calculating the gripping position. Finally, Boukhris et al [146] trained Mask-RCNN to automatically detect small lesions on leaves and fruits, locate them, classify their severity, and visualize them.…”

Section: Detection Of Small Objectsmentioning

confidence: 99%

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

2021

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Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pre-trained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized.

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Tailored Deep Learning based Architecture for Smart Agriculture

Cited by 20 publications

References 10 publications

Technologies Driving the Shift to Smart Farming: A Review

Technologies Driving the Shift to Smart Farming: A Review

Vision-Based Defect Inspection and Condition Assessment for Sewer Pipes: A Comprehensive Survey

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

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