Regularized Neural Networks Fusion and Genetic Algorithm Based On-Field Nitrogen Status Estimation of Wheat Plants

Sulistyo, Susanto Budi; Woo, Wai Lok; Dlay, S.S.

doi:10.1109/tii.2016.2628439

Cited by 59 publications

(31 citation statements)

References 33 publications

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“…The rapid development of intelligent agriculture and precision agriculture in recent years has led to the widespread use of computer image processing technologies to solve diverse problems within agricultural sciences. For example, these technologies have been used to estimate plant nutrient content [2][3][4], classify plant species [5], and identify plant diseases [6,7]. In particular, deep neural network and genetic algorithms have been used in combination to estimate nitrogen content in wheat leaves [2][3][4], which represents a considerable improvement over other existing methods.…”

Section: Introductionmentioning

confidence: 99%

Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model

2019

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The rapid, recent development of image recognition technologies has led to the widespread use of convolutional neural networks (CNNs) in automated image classification and in the recognition of plant diseases. Aims: The aim of the present study was to develop a deep CNNs to identify tea plant disease types from leaf images. Materials: A CNNs model named LeafNet was developed with different sized feature extractor filters that automatically extract the features of tea plant diseases from images. DSIFT (dense scale-invariant feature transform) features are also extracted and used to construct a bag of visual words (BOVW) model that is then used to classify diseases via support vector machine(SVM) and multi-layer perceptron(MLP) classifiers. The performance of the three classifiers in disease recognition were then individually evaluated. Results: The LeafNet algorithm identified tea leaf diseases most accurately, with an average classification accuracy of 90.16%, while that of the SVM algorithm was 60.62% and that of the MLP algorithm was 70.77%. Conclusions: The LeafNet was clearly superior in the recognition of tea leaf diseases compared to the MLP and SVM algorithms. Consequently, the LeafNet can be used in future applications to improve the efficiency and accuracy of disease diagnoses in tea plants.

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Section: Introductionmentioning

confidence: 99%

Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model

2019

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“…Because of the change in intensity of light, it becomes very challenging to estimate nutrition with inconsistent image. To tackle this problem, Susanto B et al [39], developed combination of neural networks by using color constancy method to normalize different color of images. Neural network-based prediction of soil water is used in the paper [40], for the management of water valves to achieve optimized irrigation .…”

Section: Related Workmentioning

confidence: 99%

Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses

Singh

Aernouts

Meyer

et al. 2020

Sensors

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The technology development in wireless sensor network (WSN) offers a sustainable solution towards precision agriculture (PA) in greenhouses. It helps to effectively use the agricultural resources and management tools and monitors different parameters to attain better quality yield and production. WSN makes use of Low-Power Wide-Area Networks (LPWANs), a wireless technology to transmit data over long distances with minimal power consumption. LoRaWAN is one of the most successful LPWAN technologies despite its low data rate and because of its low deployment and management costs. Greenhouses are susceptible to different types of interference and diversification, demanding an improved WSN design scheme. In this paper, we contemplate the viable challenges for PA in greenhouses and propose the successive steps essential for effectual WSN deployment and facilitation. We performed a real-time, end-to-end deployment of a LoRaWAN-based sensor network in a greenhouse of the ’Proefcentrum Hoogstraten’ research center in Belgium. We have designed a dashboard for better visualization and analysis of the data, analyzed the power consumption for the LoRaWAN communication, and tried three different enclosure types (commercial, simple box and airflow box, respectively). We validated the implications of real-word challenges on the end-to-end deployment and air circulation for the correct sensor readings. We found that temperature and humidity have a larger impact on the sensor readings inside the greenhouse than we initially thought, which we successfully solved through the airflow box design.

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“…Different from approaches using hand-crafted features, deep learning networks use hierarchical structures to automatically extract features. Due to the breakthroughs made by deep learning in an increasing number of image-processing tasks [ 25 , 26 , 27 , 28 ], some research has started to apply deep learning approaches for melanoma recognition. Codella et al proposed a hybrid approach, integrating convolutional neural network (CNN), sparse coding and support vector machines (SVMs) to detect melanoma [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

Shen

2018

Sensors

493

260

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Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. In this paper, we proposed two deep learning methods to address three main tasks emerging in the area of skin lesion image processing, i.e., lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. The proposed deep learning frameworks were evaluated on the ISIC 2017 dataset. Experimental results show the promising accuracies of our frameworks, i.e., 0.753 for task 1, 0.848 for task 2 and 0.912 for task 3 were achieved.

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Regularized Neural Networks Fusion and Genetic Algorithm Based On-Field Nitrogen Status Estimation of Wheat Plants

Cited by 59 publications

References 33 publications

Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model

Visual Tea Leaf Disease Recognition Using a Convolutional Neural Network Model

Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses

Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network

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