Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network

Rana, Saleem M.; Bashir, Rab Nawaz; Faheem, Muhammad; Haq, Mohd Anul; Alhussen, Ahmed; Alzamil, Zamil S.; Khan, Shakir

doi:10.1109/access.2023.3301504

Cited by 8 publications

(3 citation statements)

References 41 publications

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“…The majority vote determines which category will be chosen. Deep feature level fusion has great potential to improve classification performance 57 , 58 since it combines many feature sets produced from different feature extractors. When many perspectives (in‐depth features obtained from multiple CNNs) must be represented, feature‐level fusion often involves concatenating numerous normalized feature subsets into a single feature vector.…”

Section: Methodsmentioning

confidence: 99%

Lyme rashes disease classification using deep feature fusion technique

Ali,

Anwar,

Nauman

et al. 2023

Skin Research and Technology

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Automatic classification of Lyme disease rashes on the skin helps clinicians and dermatologists’ probe and investigate Lyme skin rashes effectively. This paper proposes a new in‐depth features fusion system to classify Lyme disease rashes. The proposed method consists of two main steps. First, three different deep learning models, Densenet201, InceptionV3, and Exception, were trained independently to extract the deep features from the erythema migrans (EM) images. Second, a deep feature fusion mechanism (meta classifier) is developed to integrate the deep features before the final classification output layer. The meta classifier is a basic deep convolutional neural network trained on original images and features extracted from base level three deep learning models. In the feature fusion mechanism, the last three layers of base models are dropped out and connected to the meta classifier. The proposed deep feature fusion method significantly improved the classification process, where the classification accuracy was 98.97%, which is particularly impressive than the other state‐of‐the‐art models.

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Section: Methodsmentioning

confidence: 99%

Lyme rashes disease classification using deep feature fusion technique

Ali,

Anwar,

Nauman

et al. 2023

Skin Research and Technology

Self Cite

View full text Add to dashboard Cite

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“…This approach marked a significant advancement in capturing the dynamic nature of pest populations, but the study still grappled with the challenge of achieving high accuracy across diverse pest species. Saleem et al (2023) [32] further expanded the predictive capabilities by utilizing a DNN model within an IoT framework to achieve a weekly pest prediction with an impressive accuracy of 94%. However, the generalizability of the model to different crops and pest types remains an area for further exploration.…”

Section: Discussionmentioning

confidence: 99%

Predictive Study on the Occurrence of Wheat Blossom Midges Based on Gene Expression Programming with Support Vector Machines

Li,

Lv,

Guo

et al. 2024

Insects

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This study addresses the challenges in plant pest and disease prediction within the context of smart agriculture, highlighting the need for efficient data processing techniques. In response to the limitations of existing models, which are characterized by slow training speeds and a low prediction accuracy, we introduce an innovative prediction method that integrates gene expression programming (GEP) with support vector machines (SVM). Our approach, the gene expression programming—support vector machine (GEP-SVM) model, begins with encoding and fitness function determination, progressing through cycles of selection, crossover, mutation, and the application of a convergence criterion. This method uniquely employs individual gene values as parameters for SVM, optimizing them through a grid search technique to refine genetic parameters. We tested this model using historical data on wheat blossom midges in Shaanxi Province, spanning from 1933 to 2010, and compared its performance against traditional methods, such as GEP, SVM, naive Bayes, K-nearest neighbor, and BP neural networks. Our findings reveal that the GEP-SVM model achieves a leading back-generation accuracy rate of 90.83%, demonstrating superior generalization and fitting capabilities. These results not only enhance the computational efficiency of pest and disease prediction in agriculture but also provide a scientific foundation for future predictive endeavors, contributing significantly to the optimization of agricultural production strategies.

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“…These technologies are paving the way for agriculture to evolve into a data-driven, intelligent, agile, and autonomous connected system of systems [14][15][16][17][18][19]. The sector has already observed the benefits of machine learning in a variety of different areas, including pest prediction and prevention [20,21].…”

Section: Related Workmentioning

confidence: 99%

A Multi-Farm Global-to-Local Expert-Informed Machine Learning System for Strawberry Yield Forecasting

Beddows,

Leontidis

2024

Agriculture

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The importance of forecasting crop yields in agriculture cannot be overstated. The effects of yield forecasting are observed in all the aspects of the supply chain from staffing to supplier demand, food waste, and other business decisions. However, the process is often inaccurate and far from perfect. This paper explores the potential of using expert forecasts to enhance the crop yield predictions of our global-to-local XGBoost machine learning system. Additionally, it investigates the ERA5 climate model’s viability as an alternative data source for crop yield forecasting in the absence of on-farm weather data. We find that, by combining both the expert’s pre-season forecasts and the ERA5 climate model with the machine learning model, we can—in most cases—obtain better forecasts that outperform the growers’ pre-season forecasts and the machine learning-only models. Our expert-informed model attains yield forecasts for 4 weeks ahead with an average RMSE of 0.0855 across all the plots and an RMSE of 0.0872 with the ERA5 climate data included.

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Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network

Cited by 8 publications

References 41 publications

Lyme rashes disease classification using deep feature fusion technique

Lyme rashes disease classification using deep feature fusion technique

Predictive Study on the Occurrence of Wheat Blossom Midges Based on Gene Expression Programming with Support Vector Machines

A Multi-Farm Global-to-Local Expert-Informed Machine Learning System for Strawberry Yield Forecasting

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