Fruit Detection from Digital Images Using CenterNet

Zhao, Kun; Yan, Wei Qi

doi:10.1007/978-3-030-72073-5_24

Cited by 19 publications

(4 citation statements)

References 36 publications

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“…For example, the MobileNet model has been applied to detect plant disease on rice [32] and apple [33]. The CenterNet model has been applied to extract weeds from vegetable plants [34] and detect fruits from digital images [35]. The assessment of these two models in plant phenotyping, such as cotton stand counting, can provide valuable information about selecting the appropriate deep learning tools for the right tasks.…”

Section: Introductionmentioning

confidence: 99%

Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models

Lin

Guo

2021

Remote Sensing

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An accurate stand count is a prerequisite to determining the emergence rate, assessing seedling vigor, and facilitating site-specific management for optimal crop production. Traditional manual counting methods in stand assessment are labor intensive and time consuming for large-scale breeding programs or production field operations. This study aimed to apply two deep learning models, the MobileNet and CenterNet, to detect and count cotton plants at the seedling stage with unmanned aerial system (UAS) images. These models were trained with two datasets containing 400 and 900 images with variations in plant size and soil background brightness. The performance of these models was assessed with two testing datasets of different dimensions, testing dataset 1 with 300 by 400 pixels and testing dataset 2 with 250 by 1200 pixels. The model validation results showed that the mean average precision (mAP) and average recall (AR) were 79% and 73% for the CenterNet model, and 86% and 72% for the MobileNet model with 900 training images. The accuracy of cotton plant detection and counting was higher with testing dataset 1 for both CenterNet and MobileNet models. The results showed that the CenterNet model had a better overall performance for cotton plant detection and counting with 900 training images. The results also indicated that more training images are required when applying object detection models on images with different dimensions from training datasets. The mean absolute percentage error (MAPE), coefficient of determination (R2), and the root mean squared error (RMSE) values of the cotton plant counting were 0.07%, 0.98 and 0.37, respectively, with testing dataset 1 for the CenterNet model with 900 training images. Both MobileNet and CenterNet models have the potential to accurately and timely detect and count cotton plants based on high-resolution UAS images at the seedling stage. This study provides valuable information for selecting the right deep learning tools and the appropriate number of training images for object detection projects in agricultural applications.

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Section: Introductionmentioning

confidence: 99%

Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models

Lin

Guo

2021

Remote Sensing

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“…ResNet [38] was embedded into the backbone of CenterNet and attained an accuracy rate 78.6%. The loss function of ResNet can assist CenterNet to better complete the target detection task.…”

Section: Canternet Modelmentioning

confidence: 99%

Fruit ripeness identification using YOLOv8 model

Xiao,

Nguyen,

Yan

2023

Multimed Tools Appl

Self Cite

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Deep learning-based visual object detection is a fundamental aspect of computer vision. These models not only locate and classify multiple objects within an image, but they also identify bounding boxes. The focus of this paper's research work is to classify fruits as ripe or overripe using digital images. Our proposed model extracts visual features from fruit images and analyzes fruit peel characteristics to predict the fruit's class. We utilize our own datasets to train two "anchor-free" models: YOLOv8 and CenterNet, aiming to produce accurate predictions. The CenterNet network primarily incorporates ResNet-50 and employs the deconvolution module DeConv for feature map upsampling. The final three branches of convolutional neural networks are applied to predict the heatmap. The YOLOv8 model leverages CSP and C2f modules for lightweight processing. After analyzing and comparing the two models, we found that the C2f module of the YOLOv8 model significantly enhances classification results, achieving an impressive accuracy rate of 99.5%.

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“…Jia et al [122] described the CenterNet and CornerNet-Lite lightweight real-time object detection systems. Zhao et al [123] used this system for fruit detection from digital images. As a result, this system showed the best results compared with ResNet-18, DLA-34, and HourglassNet.…”

Section: Models and Architecturesmentioning

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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Fruit Detection from Digital Images Using CenterNet

Cited by 19 publications

References 36 publications

Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models

Cotton Stand Counting from Unmanned Aerial System Imagery Using MobileNet and CenterNet Deep Learning Models

Fruit ripeness identification using YOLOv8 model

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

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