A New Classification Method for Drone-Based Crops in Smart Farming

Al-Rami, Bandar; Alheeti, Khattab M. Ali; Aldosari, Waleed; Alshahrani, Saeed Matar; Al-Abrez, Shahad Mahdi

doi:10.3991/ijim.v16i09.30037

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…This study uses several classification techniques. Biological butterfly characterization with a geographical mobile system uses images to recognize, analyze, and process, based on deep learning image processing [23]. Figure 2 illustrates the functional block diagram.…”

Section: Block Diagrammentioning

confidence: 99%

Biological Butterfly Characterization with Mobile System Using Convolutional Neural Network (CNN) Classify Image

Saedan,

Kassim,

Abd Aziz

2024

Int. J. Interact. Mob. Technol.

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This study presents the development of a mobile identification system that detects biological butterfly characteristics through deep learning by capturing images. The challenge identified is that butterfly identification and recognition are difficult tasks because there are too many species, and it is hard to classify the types of butterfly species. Butterflies are also difficult to differentiate from each other, and limited studies are done using computer database referrals for butterflies’ characterization. This study aims to develop an automated computer program to easily identify the species of butterflies. Deep learning in image processing is programmed, which can control the qualification, segmentation, and classification of images and automatically detect butterfly characterization. The design system consists of three stages: capture, feature extraction, and butterfly recognition. Then, multiple recognition clues such as shape, color, texture, and size are extracted and analyzed to analyze and recognize the butterfly. This approach is faster and less complex than the previous approach. The result successfully presents a convolutional neural network (CNN) to classify images after training and characterization. The graphics processing unit (GPU) that trains the image dataset presents 86% image accuracy in the allocated time. This research is significant in such a way that new butterfly species will be automatically collected and stored on the online server. The information could be treasured as a valuable butterfly database.

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Section: Block Diagrammentioning

confidence: 99%

Biological Butterfly Characterization with Mobile System Using Convolutional Neural Network (CNN) Classify Image

Saedan,

Kassim,

Abd Aziz

2024

Int. J. Interact. Mob. Technol.

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“…Furthermore, the research delves into an extensive investigation of various CNN (convolutional neural network) architectures [16][17][18]. The main objective is to investigate different configurations of CNNs, including parameters such as stride, kernel size, and filter selection.…”

Section: Innovations and Breakthroughsmentioning

confidence: 99%

Advancing Non-Cuff Hypertension Detection: Leveraging 1D Convolutional Neural Network and Time Domain Physiological Signals

Nuryani,

Utomo,

Prabowo

et al. 2024

Int. J. Onl. Eng.

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Timely identification of hypertension (HT) is crucial for effectively managing and reducing the potential health consequences, including cardiovascular events such as heart attacks and strokes, as well as the development of kidney disease. Traditional cuff-based devices often discourage regular monitoring because they cause discomfort. Furthermore, the lack of symptoms in HT complicates the early detection of this condition. To address these challenges, our study employs a non-cuff methodology that utilizes unprocessed electrocardiogram (ECG) and photoplethysmogram (PPG) signals. We utilize a customized approach to enhance the features of a one-dimensional convolutional neural network (CNN) specifically tailored to optimize timeseries data. In contrast to previous research, our methodology avoids the need for complex signal extraction or transformation techniques. The main goal is to identify the optimal input signals and fine-tune the critical hyperparameters of CNNs. The clinical data underwent analysis, which revealed that the use of an integrated ECG and PPG approach resulted in the highest level of accuracy for detection. Notably, the F1 score achieved an impressive value of 98.88%. When evaluated separately, ECG outperformed PPG. Our study contributes to the advancement of the field by introducing a new approach that combines comfort and high accuracy in the early detection of HT. This method is practical and ensures a patient-friendly experience.

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“…In Ref. [19], the authors proposed an intelligent method for crop classification that employed a series of images captured by drones and a model based on Convolutional Neural Networks (CNNs). Furthermore, the authors implemented the transfer learning technique to improve the efficiency of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Drone-Assisted Plant Disease Identification Using Artificial Intelligence: A Critical Review

Slimani,

El Mhamdi,

Jilbab

2023

IJCDS

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Artificial intelligence has been incorporated into modern agriculture to increase agricultural output and resource efficiency. Utilizing deep learning, particularly convolutional neural networks, for recognizing and diagnosing plant diseases is tempting. In parallel, drone integration in precision agriculture has accelerated, providing new potential for crop monitoring, map creation, and targeted treatments. This study analyzes over 100 significant research articles published between 2018 and 2023, examining the interaction between drones and artificial intelligence in identifying plant diseases. We begin by explaining the value of sensor and drone technology in identifying plant diseases and carefully mapping the area. The various CNN architectures and drone-based approaches essential for precise illness detection and diagnosis are then highlighted in a thorough research review. Our research highlights how this combination can transform how plant diseases are managed completely. This study emphasizes the conceptual underpinnings of this new fusion, even if fulfilling this promise needs additional investigation. In conclusion, we expect changing research paths to direct improvements in this field and integrate AI, deep learning, drones, and plant pathology into a coherent framework with significant agricultural consequences.

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A New Classification Method for Drone-Based Crops in Smart Farming

Cited by 8 publications

References 17 publications

Biological Butterfly Characterization with Mobile System Using Convolutional Neural Network (CNN) Classify Image

Biological Butterfly Characterization with Mobile System Using Convolutional Neural Network (CNN) Classify Image

Advancing Non-Cuff Hypertension Detection: Leveraging 1D Convolutional Neural Network and Time Domain Physiological Signals

Drone-Assisted Plant Disease Identification Using Artificial Intelligence: A Critical Review

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