E-AlexNet: quality evaluation of strawberry based on machine learning

Ni, Jiangong; Gao, Jiyue; Li, Juan; Yang, Haoyan; Zheng, Hao; Han, Zhongzhi

doi:10.1007/s11694-021-01010-9

Cited by 24 publications

(16 citation statements)

References 32 publications

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“…Our improvement of the ResNet18 network classifier was based on the older AlexNet network. Experimental results show that advanced models can also learn from older models and in this way, better ones may be created (Ni et al, 2021). The embedding of the attention (SE) module does increase the accuracy of network recognition, but it undeniably increases the time consumption of network training (Wang et al, 2021).…”

Section: Performance Comparison With Other Modelsmentioning

confidence: 99%

Tomato leaf disease recognition based on improved convolutional neural network with attention mechanism

Zhou

Zhao

et al. 2023

Plant Pathology

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Plant diseases are common factors restricting crop yield and quality. For individuals without training, it is still difficult to identify various diseases accurately because many leaf diseases are similar in colour, shape and other aspects. Requiring an expert diagnosis can cause delays and lead to increasing costs. Therefore, this paper proposes an improved convolutional neural network for recognition of tomato leaf diseases. The experimental dataset was from the publicly available dataset PlantVillage. Based on the original ResNet18 model, a new convolutional neural network, TomatoNet, was constructed by adding a squeeze‐and‐excitation module and modifying the classifier structure. The results show that the average recognition accuracy of the TomatoNet network is 99.63%, which is 0.53% higher than the ResNet18 network. In addition, the recognition accuracy improved from 88.97% to 98.35% after the improvement of the AlexNet network. Finally, the superiority of TomatoNet was verified by comparison with other advanced models. This experiment verifies the feasibility of a deep learning algorithm for plant leaf disease recognition, which can provide a more efficient and convenient solution for detecting plant leaf disease.

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Section: Performance Comparison With Other Modelsmentioning

confidence: 99%

Tomato leaf disease recognition based on improved convolutional neural network with attention mechanism

Zhou

Zhao

et al. 2023

Plant Pathology

Self Cite

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“…The quality of strawberries was assessed using an extended Alexnet (E-Alexnet) [9]. Laboratory and field images of strawberries were collected and pre-processed using image augmentation and image balancing.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms

et al. 2022

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Fruit that has reached maturity is ready to be harvested. The prediction of fruit maturity and quality is important not only for farmers or the food industry but also for small retail stores and supermarkets where fruits are sold and purchased. Fruit maturity classification is the process by which fruits are classified according to their maturity in their life cycle. Nowadays, deep learning (DL) has been applied in many applications of smart agriculture such as water and soil management, crop planting, crop disease detection, weed removal, crop distribution, strong fruit counting, crop harvesting, and production forecasting. This study aims to find the best deep learning algorithms which can be used for the prediction of fruit maturity and quality for the shelf life of fruit. In this study, two datasets of banana fruit are used, where we create the first dataset, and the second dataset is taken from Kaggle, named Fruit 360. Our dataset contains 2100 images in 3 categories: ripe, unripe, and over-ripe, each of 700 images. An image augmentation technique is used to maximize the dataset size to 18,900. Convolutional neural networks (CNN) and AlexNet techniques are used for building the model for both datasets. The original dataset achieved an accuracy of 98.25% for the CNN model and 81.75% for the AlexNet model, while the augmented dataset achieved an accuracy of 99.36% for the CNN model and 99.44% for the AlexNet model. The Fruit 360 dataset achieved an accuracy of 81.96% for CNN and 81.75% for the AlexNet model. We concluded that for all three datasets of banana images, the proposed CNN model is the best suitable DL algorithm for bananas’ fruit maturity classification and quality detection.

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“…Compared with conventional methods, CNNs extract different levels of features from input images, achieving accurate target detection through information classification and location regression [ 30 , 31 ]. The well-known CNN architectures, such as AlexNet, ResNet50, and VGG16, can be utilized for the classification and diagnosis of diseases as well as the quality inspection of fruits and vegetables [ 32 , 33 , 34 , 35 ]. Fu et al [ 36 ] used an optimized GoogLeNet model to categorize apples, lemons, oranges, pomegranates, tomatoes, and colored peppers.…”

Section: Related Workmentioning

confidence: 99%

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

Liu

Wang

et al. 2022

Sensors

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Advancements in deep learning and computer vision have led to the discovery of numerous effective solutions to challenging problems in the field of agricultural automation. With the aim to improve the detection precision in the autonomous harvesting process of green asparagus, in this article, we proposed the DA-Mask RCNN model, which utilizes the depth information in the region proposal network. Firstly, the deep residual network and feature pyramid network were combined to form the backbone network. Secondly, the DA-Mask RCNN model added a depth filter to aid the softmax function in anchor classification. Afterwards, the region proposals were further processed by the detection head unit. The training and test images were mainly acquired from different regions in the basin of the Yangtze River. During the capturing process, various weather and illumination conditions were taken into account, including sunny weather, sunny but overshadowed conditions, cloudy weather, and daytime greenhouse conditions as well as nighttime greenhouse conditions. Performance experiments, comparison experiments, and ablation experiments were carried out using the five constructed datasets to verify the effectiveness of the proposed model. Precision, recall, and F1-score values were applied to evaluate the performances of different approaches. The overall experimental results demonstrate that the balance of the precision and speed of the proposed DA-Mask RCNN model outperform those of existing algorithms.

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E-AlexNet: quality evaluation of strawberry based on machine learning

Cited by 24 publications

References 32 publications

Tomato leaf disease recognition based on improved convolutional neural network with attention mechanism

Tomato leaf disease recognition based on improved convolutional neural network with attention mechanism

Prediction of Fruit Maturity, Quality, and Its Life Using Deep Learning Algorithms

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

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