Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network

Xiao, Qingxin; Li, Weilu; Kai, Yuanzhong; Zhang, Youzhi; Zhang, Jun; Wang, Bing

doi:10.1186/s12859-019-3262-y

Cited by 49 publications

(33 citation statements)

References 21 publications

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“…In addition, CNN has shown optimal performance when implemented in plant-pathogen interaction, pest, and disease recognition in some studies. These studies include prediction of pests and diseases occurrence in cotton 105 ; rice plant diseases and pests recognition 106 ; rice blast disease prediction 107,108 ; image-based potato tuber disease detection 109 and so on. The CNN model is a high-performing method for detecting plant diseases, and it can be implemented and optimized for practical applications.…”

Section: Phenomicsmentioning

confidence: 99%

Machine learning algorithms: their applications in plant omics and agronomic traits’ improvement

et al. 2022

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Agronomic traits of plants especially those of economic or aesthetic importance are threatened by climatic and environmental factors such as climate change, biotic, and abiotic stresses. These threats are now being mitigated through the analyses of omics data like genomics, transcriptomics, proteomics, metabolomics, and phenomics. The emergence of high-throughput omics technology has led to an avalanche of plant omics data. Plant research demands novel analytical paradigms to extract and harness large plant omics data for plant improvement effectively and efficiently. Machine learning algorithms are well-suited analytical and computational approaches for the integrative analysis of large unstructured, heterogeneous datasets. This study presents an overview of omics approaches to improve plant agronomic traits and crucial curated plant genomic data sources. Furthermore, we summarize machine learning algorithms and software tools/programming packages used in plant omics research. Lastly, we discuss advancements in machine learning algorithms' applications in improving agronomic traits of economically important plants. Extensive application of machine learning would advance plant omics studies. These advancements would consequently help agricultural scientists improve economically important plants’ quality, yield, and tolerance against abiotic and biotic stresses and other plant health-threatening issues.

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Section: Phenomicsmentioning

confidence: 99%

Machine learning algorithms: their applications in plant omics and agronomic traits’ improvement

et al. 2022

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“…In recent years, deep learning has provided advanced and efficient solutions for image processing tasks, such as image classification ( Atila et al, 2021 ), image segmentation ( Kang et al, 2020 ), and object detection ( Janai et al, 2020 ). Its excellent feature extraction ability greatly reduces the workload of image processing tasks ( Shao et al, 2017 ; Xiao Q. et al, 2019 ; Afonso et al, 2020 ). In view of the differences in the application objects, researchers mostly adjust the network structure according to the practical problems ( Buiu et al, 2020 ; Liu et al, 2021 ; Gu et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision

et al. 2022

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The primary task in calculating the tobacco shred blending ratio is identifying the four tobacco shred types: expanded tobacco silk, cut stem, tobacco silk, and reconstituted tobacco shred. The classification precision directly affects the subsequent determination of tobacco shred components. However, the tobacco shred types, especially expanded tobacco silk and tobacco silk, have no apparent differences in macro-scale characteristics. The tobacco shreds have small size and irregular shape characteristics, creating significant challenges in their recognition and classification based on machine vision. This study provides a complete set of solutions aimed at this problem for screening tobacco shred samples, taking images, image preprocessing, establishing datasets, and identifying types. A block threshold binarization method is used for image preprocessing. Parameter setting and method performance are researched to obtain the maximum number of complete samples with acceptable execution time. ResNet50 is used as the primary classification and recognition network structure. By increasing the multi-scale structure and optimizing the number of blocks and loss function, a new tobacco shred image classification method is proposed based on the MS-X-ResNet (Multi-Scale-X-ResNet) network. Specifically, the MS-ResNet network is obtained by fusing the multi-scale Stage 3 low-dimensional and Stage 4 high-dimensional features to reduce the overfitting risk. The number of blocks in Stages 1–4 are adjusted from the original 3:4:6:3 to 3:4:N:3 (A-ResNet) and 3:3:N:3 (B-ResNet) to obtain the X-ResNet network, which improves the model’s classification performance with lower complexity. The focal loss function is selected to reduce the impact of identification difficulty for different sample types on the network and improve its performance. The experimental results show that the final classification accuracy of the network on a tobacco shred dataset is 96.56%. The image recognition of a single tobacco shred requires 103 ms, achieving high classification accuracy and efficiency. The image preprocessing and deep learning algorithms for tobacco shred classification and identification proposed in this study provide a new implementation approach for the actual production and quality detection of tobacco and a new way for online real-time type identification of other agricultural products.

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“…It is worth noting that most related studies use weather data, whereas in fewer cases remote Earth observations are used to capture the changes caused by the pests, but also the favourable vegetation conditions for their occurrence [12]. Recurrent Neural Networks (RNN) have been applied to weather data time-series, accounting for the temporal evolution of features and capturing the cyclical nature of pest abundance [13]. In other words, when trying to predict pest occurrence at a given time instance, one cannot ignore the weather conditions and the vegetation status of previous days [14].…”

Section: Introductionmentioning

confidence: 99%

Pest presence prediction using interpretable machine learning

Ornela¹,

Sitokonstantinou²,

Tsoumas³

et al. 2022

Preprint

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Helicoverpa Armigera, or cotton bollworm, is a serious insect pest of cotton crops that threatens the yield and the quality of lint. The timely knowledge of the presence of the insects in the field is crucial for effective farm interventions. Meteo-climatic and vegetation conditions have been identified as key drivers of crop pest abundance. In this work, we applied an interpretable classifier, i.e., Explainable Boosting Machine, which uses earth observation vegetation indices, numerical weather predictions and insect trap catches to predict the onset of bollworm harmfulness in cotton fields in Greece. The glass-box nature of our approach provides significant insight on the main drivers of the model and the interactions among them. Model interpretability adds to the trustworthiness of our approach and therefore its potential for rapid uptake and context-based implementation in operational farm management scenarios. Our results are satisfactory and the importance of drivers, through our analysis on global and local explainability, is in accordance with the literature.

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Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network

Cited by 49 publications

References 21 publications

Machine learning algorithms: their applications in plant omics and agronomic traits’ improvement

Machine learning algorithms: their applications in plant omics and agronomic traits’ improvement

Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision

Pest presence prediction using interpretable machine learning

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