Smart agriculture practices in potato production

Ünal, Zeynep; Kızıldeniz, Tefide

doi:10.1016/b978-0-12-822925-5.00010-4

Cited by 6 publications

(3 citation statements)

References 42 publications

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“…Similar to the tomato, the potato is one of the five most important products grown in the world. Ünal et al [2] believed that the potato has high application potential in intelligent agriculture and discussed the potential of intelligent agriculture technology in potato production, which inspired the research on the application of the tomato in intelligent agriculture. Moreover, tomato fruits are susceptible to damage, resulting in damage rates of up to 10% [3].…”

Section: Introductionmentioning

confidence: 99%

A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

et al. 2023

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A tomato automatic detection method based on an improved YOLOv8s model is proposed to address the low automation level in tomato harvesting in agriculture. The proposed method provides technical support for the automatic harvesting and classification of tomatoes in agricultural production activities. The proposed method has three key components. Firstly, the depthwise separable convolution (DSConv) technique replaces the ordinary convolution, which reduces the computational complexity by generating a large number of feature maps with a small amount of calculation. Secondly, the dual-path attention gate module (DPAG) is designed to improve the model’s detection precision in complex environments by enhancing the network’s ability to distinguish between tomatoes and the background. Thirdly, the feature enhancement module (FEM) is added to highlight the target details, prevent the loss of effective features, and improve detection precision. We built, trained, and tested the tomato dataset, which included 3098 images and 3 classes. The proposed algorithm’s performance was evaluated by comparison with the SSD, faster R-CNN, YOLOv4, YOLOv5, and YOLOv7 algorithms. Precision, recall rate, and mAP (mean average precision) were used for evaluation. The test results show that the improved YOLOv8s network has a lower loss and 93.4% mAP on this dataset. This improvement is a 1.5% increase compared to before the improvement. The precision increased by 2%, and the recall rate increased by 0.8%. Moreover, the proposed algorithm significantly reduced the model size from 22 M to 16 M, while achieving a detection speed of 138.8 FPS, which satisfies the real-time detection requirement. The proposed method strikes a balance between model size and detection precision, enabling it to meet agriculture’s tomato detection requirements. The research model in this paper will provide technical support for a tomato picking robot to ensure the fast and accurate operation of the picking robot.

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Section: Introductionmentioning

confidence: 99%

A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

et al. 2023

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“…Automated methods for fruit quality assessment are being developed using cutting-edge technology like computer vision and machine learning [2]. Deep learning, which is a subset of machine learning, is successfully applied in agricultural applications [3,4] as well as in many areas [5][6][7]. However, detecting bruises in dark-skinned fruits such as plums is challenging even for deep learning because their appearance remains unchangeable for several hours.…”

Section: Introductionmentioning

confidence: 99%

Image preprocessing techniques applied on NIR images for fruit bruise detection

Ünal

2024

BIO Web Conf.

Self Cite

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This study investigates the transformative potential of image preprocessing techniques when applied to near-infrared (NIR) images for early bruise detection. It emphasizes the nuanced selection of filters to retain essential image features while accentuating bruise characteristics. Filters as noise-reduction tools, rendering bruises more visible without erasing critical details. Subsequently, the limitations of conventional edge detection filters were examined such as Sobel, Prewitt, and Canny, which excel in outlining fruit edges but fall short in delineating bruises. Adaptive thresholding methods were introduced, exemplified by Otsu’s, showcasing their capacity to distinguish objects from backgrounds while acknowledging their challenge in preserving crucial edge pixels. Image enhancement techniques, such as Histogram Equalization, Contrast Stretching, and Sigmoid Correction, enhance fruit edge visibility and elevate bruise detection. In the frequency domain, filters such as Ideal Lowpass, Bandpass, and Highpass were harnessed to accentuate diverse bruise types. The Butterworth filter was introduced, capable of concurrently highlighting all relevant features, a pivotal innovation in comprehensive bruise detection. Through extensive experimentation and analysis of NIR images of various fruit varieties, including plums, peaches, and apples, our findings underscore the significance of tailored preprocessing techniques for optimal fruit bruise detection. These insights offer promise for agricultural industries and quality control processes seeking to enhance fruit quality assessment.

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“…They summarized image capture, preprocessing, and classification techniques, providing important guidance for disease detection and management in agriculture through an in-depth analysis of the existing literature. In addition, Ünal et al provided an overview of smart agriculture practices specifically in potato production, which contributed to the development of intelligent solutions for potato production [19].…”

Section: Introductionmentioning

confidence: 99%

Maize Nitrogen Grading Estimation Method Based on UAV Images and an Improved Shufflenet Network

Sun

Zhang

2023

Agronomy

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Maize is a vital crop in China for both food and industry. The nitrogen content plays a crucial role in its growth and yield. Previous researchers have conducted numerous studies on the issue of the nitrogen content in single maize plants from a regression perspective; however, partition management techniques of precision agriculture require plants to be divided by zones and classes. Therefore, in this study, the focus is shifted to the problems of plot classification and graded nitrogen estimation in maize plots performed based on various machine learning and deep learning methods. Firstly, the panoramic unmanned aerial vehicle (UAV) images of maize farmland are collected by UAV and preprocessed to obtain UAV images of each maize plot to construct the required datasets. The dataset includes three classes—low nitrogen, medium nitrogen, and high nitrogen, with 154, 94, and 46 sets of UAV images, respectively, in each class. The training set accounts for eighty percent of the entire dataset and the test set accounts for the other twenty percent. Then, the dataset is used to train models based on machine learning and convolutional neural network algorithms and subsequently the models are evaluated. Comparisons are made between five machine learning classifiers and four convolutional neural networks to assess their respective performances, followed by a separate assessment of the most optimal machine learning classifier and convolutional neural networks. Finally, the ShuffleNet network is enhanced by incorporating SENet and improving the kernel size of the Depthwise separable convolution. The findings demonstrate that the enhanced ShuffleNet network has the highest performance; its classification accuracy, precision, recall, and F1 scores were 96.8%, 97.0%, 97.1%, and 97.0%, respectively. The RegNet, the optimal model among deep learning models, achieved accuracy, precision, recall, and F1 scores of 96.4%, 96.9%, 96.5%, and 96.6%, respectively. In comparison, logistic regression, the optimal model among the machine learning classifiers, attained accuracy of 77.6%, precision of 79.5%, recall of 77.6%, and an F1 score of 72.6%. Notably, the logistic regression exhibited significant enhancements of 19.2% in accuracy, 17.5% in precision, 19.5% in recall, and 24.4% in the F1 score. In contrast, RegNet demonstrated modest improvements of 0.4% in accuracy, 0.1% in precision, 0.6% in recall, and 0.4% in the F1 score. Moreover, ShuffleNet-improvement boasted a substantially lower loss rate of 0.117, which was 0.039 lower than that of RegNet (0.156). The results indicated the significance of ShuffleNet-improvement in the nitrogen classification of maize plots, providing strong support for agricultural zoning management and precise fertilization.

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Smart agriculture practices in potato production

Cited by 6 publications

References 42 publications

A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention

Image preprocessing techniques applied on NIR images for fruit bruise detection

Maize Nitrogen Grading Estimation Method Based on UAV Images and an Improved Shufflenet Network

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