Efficient Data-Driven Crop Pest Identification Based on Edge Distance-Entropy for Sustainable Agriculture

Yang, Jiachen; Ma, Shukun; Li, Yang; Zhang, Zhuo

doi:10.3390/su14137825

Cited by 15 publications

(5 citation statements)

References 29 publications

(32 reference statements)

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“…Finally, the accuracy rate in the mature stage was 86.33%, and that in the early stage was 87.91%. Yang J et al [ 15 ] searched for efficient data from a small amount of data, studied the edge distance-entropy data evaluation method, and got 100% effect when using 60% data, which solved the problem that the algorithm based on deep learning needs a large amount of data to obtain accuracy. From the above research, it can be seen that the meta-learning algorithm can achieve a good detection effect under the condition of small samples, and can realize the detection of diseases and pests in the quality of agricultural products.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Detection of Soybean Pest Based on Hyperspectral Image and Attention-ResNet Meta-Learning Model

Gui

Xu²,

Fei³

2023

Sensors

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Soybean plays an important role in food, medicine, and industry. The quality inspection of soybean is essential for soybean yield and the agricultural economy. However, soybean pest is an important factor that seriously affects soybean yield, among which leguminivora glycinivorella matsumura is the most frequent pest. Aiming at the problem that the traditional detection methods have low accuracy and need a large number of samples to train the model, this paper proposed a detection method for leguminivora glycinivorella matsumura based on an A-ResNet (Attention-ResNet) meta-learning model. In this model, the ResNet network was combined with Attention to obtain the feature vectors that can better express the samples, so as to improve the performance of the model. As well, the classifier was designed as a multi-class support vector machine (SVM) to reduce over-fitting. Furthermore, in order to improve the training stability of the model and the prediction performance on the testing set, the traditional Batch Normalization was replaced by the Layer Normalization, and the Label Smooth method was used to punish the original loss. The experimental results showed that the accuracy of the A-ResNet meta-learning model reached 94.57 ± 0.19%, which can realize rapid and accurate nondestructive detection, and provides theoretical support for the intelligent detection of soybean pests.

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Section: Introductionmentioning

confidence: 99%

Non-Destructive Detection of Soybean Pest Based on Hyperspectral Image and Attention-ResNet Meta-Learning Model

Gui

Xu²,

Fei³

2023

Sensors

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“…In the research of agricultural pest detection, many researchers have used machine learning 21 , 22 or convolutional neural network 23 – 26 and have collected numerous large-scale datasets about pests 27 , 28 . With the rapid development of deep learning, crop pest detection technology based on deep learning has gradually become more automatic, more intelligent, and real time, which can help farmers deal with pests, protect crops in time, and reduce losses to the greatest extent, so it is a great breakthrough in the field of agriculture.…”

Section: Introductionmentioning

confidence: 99%

Data-driven few-shot crop pest detection based on object pyramid for smart agriculture

Xiao

Kumar

et al. 2023

J. Electron. Imag.

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.Farmers are often annoyed by crop pests, which can lead to serious losses if not detected in time. Deep learning has gradually penetrated into the agricultural field due to its proven superior performance, promoting the development of smart agriculture. Its real-time performance and effectiveness can help farmers spot pests in time and reduce losses. However, with the increasing application of deep learning technology, its disadvantage of relying heavily on large amounts of data has gradually been exposed. Before training a network, people need to spend a lot of manpower, time, and money to prepare a complete dataset and make the correct labels. To solve this problem, we introduced a less-shot object detection method in crop pest detection. We provide the first few-shot pest detection dataset (FSPD), which contains 20 categories and 2000 samples for this research. We use a few-shot object detection network based on an object pyramid called multiscale positive sample refinement. Numerous experiments are conducted to prove that, with only few (1/2/3/5/10-shot) samples, the accuracy of our method on FSPD is better than that of traditional deep learning methods, such as YOLO and faster R-CNN. After embedding the object pyramid branch, the network performance improves by an average of 5.9% on three splits compared with that without embedding. With input of only 10-shot novel samples, our method can achieve 76%, 79%, and 82% for the mAP on three splits, respectively. As a research study on the detection of crop diseases and pests with less radiation, our work can give some new insight to the future scholars, which is of great significance to the development of smart agriculture.

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“…In recent years, computer vision 4 has been developing vigorously. Deep learning, 5 image processing, 6 and other technologies have been applied to agricultural activities, accelerating the trend of agricultural intelligence 7 . The intelligent weed identification algorithm based on the convolutional neural network 8 has made some achievements in the production and life of intelligent agriculture and has made contributions to the disinfection and sterilization of weeds.…”

Section: Introductionmentioning

confidence: 99%

High-information data-centric weed images data identification system based on triple filter for smart agriculture

Zhang

2023

J. Electron. Imag.

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Weeds have seriously impacted crop planting and caused serious human losses.The intelligent weed identification algorithm based on the convolutional neural network has made some achievements in intelligent agriculture. However, the current results rely on a large number of labeled weed data, which is costly to obtain. How to effectively identify without big data is a key task in the current field. We designed a high-information data-centric weed images data identification system based on a triple filter. The system consists of three modules: nearest-neighbor core metric, unobserved components model, and outlier detection. Extensive scientific experiments have shown that our system requires only a small amount of data to achieve excellent performance in weed identification tasks. Our system saves 5% to 20% of the data. When using only 80% of the data, our system achieves the model performance obtained with 100% of data training. Compared with other methods, this method improves the accuracy by 4.9% and reaches a state-of-the-art performance. Our work solves the problem of relying on image data in smart agriculture, provides a scheme for weed identification tasks, and provides valuable ideas for future intelligent agricultural research.

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Efficient Data-Driven Crop Pest Identification Based on Edge Distance-Entropy for Sustainable Agriculture

Cited by 15 publications

References 29 publications

Non-Destructive Detection of Soybean Pest Based on Hyperspectral Image and Attention-ResNet Meta-Learning Model

Non-Destructive Detection of Soybean Pest Based on Hyperspectral Image and Attention-ResNet Meta-Learning Model

Data-driven few-shot crop pest detection based on object pyramid for smart agriculture

High-information data-centric weed images data identification system based on triple filter for smart agriculture

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