Occurrence prediction of cotton pests and diseases by bidirectional long short-term memory networks with climate and atmosphere circulation

Chen, Peng; Xiao, Qingxin; Zhang, Jun; Xie, Chengjun; Wang, Bing

doi:10.1016/j.compag.2020.105612

Cited by 50 publications

(17 citation statements)

References 16 publications

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“…The researchers examined two supervised learning algorithms (SVM and Random Forest) and an unsupervised learning strategy (K-means clustering). SVM, Random Forest, and K-means all had test accuracy of 99.3%, 99.6%, and 99.5 [28] proposed a Bi-LSTM model that is used to predict the occurrence of cotton pests and diseases based on climate variables. Bi-LSTM performs well in predicting the occurrence of pests and diseases in cotton fields, with an Area Under the Curve (AUC) of 0.95.…”

Section: B Internet Of Things For Crop Disease Detectionmentioning

confidence: 99%

Rice-Fusion: A Multimodality Data Fusion Framework for Rice Disease Diagnosis

2022

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Rice leaf infections are a common hazard to rice production, affecting many farmers all over the world. Early detection and treatment of rice leaf infection are critical for promoting healthy rice plant growth and ensuring adequate supply for the fast-growing population. Computer-assisted rice leaf disease diagnoses are hampered due to strong image backgrounds. Popular Convolutional Neural Network (CNN) architecture extracts the features from images and diagnoses the disease to address the issues above. However, this method is best suitable for segmented images and gives low accuracy with real-time images. In this case, the Internet of Things is a paradigm shift that collects agro-meteorological information that effectively helps diagnose rice diseases. Motivated by the usefulness of CNN models and agricultural IoT, a novel multimodal data fusion framework named Rice-Fusion is proposed to diagnose rice disease. Rice disease diagnosis based on a single modality may not be accurate, and hence the fusion of heterogeneous modalities is essential for robust and reliable disease diagnosis. This gives a new dimension to the domain of rice disease diagnosis. The dataset was collected manually with 3200 rice health category samples using two modalities, namely agro-meteorological sensors and a camera. The Rice-Fusion framework initially extracts the numerical features from agro-meteorological data collected from sensors. Next, it extracts the visual features from the captured rice images. These extracted features are further fused using a concatenation layer followed by a dense layer, which provides single output for diagnosing the rice disease. The testing accuracy of Rice-Fusion is 95.31% as opposed to other unimodal framework accuracies of 82.03% and 91.25% based on CNN and Multi-Layer Perceptron (MLP) architectures, respectively. Experimental results analysis demonstrates that the proposed Rice-Fusion multimodal data fusion framework outperforms the outcome of unimodal frameworks.

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Section: B Internet Of Things For Crop Disease Detectionmentioning

confidence: 99%

Rice-Fusion: A Multimodality Data Fusion Framework for Rice Disease Diagnosis

2022

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“…Deep learning: In [108], the authors developed an approach for prediction of cotton disease and pests occurrence. The approach was designed based on weather and atmospheric circulation time series collected from six different zones in India.…”

Section: Internet Of Things Sensorsmentioning

confidence: 99%

Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research

et al. 2021

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Crop diseases constitute a serious issue in agriculture, affecting both quality and quantity of agriculture production. Disease control has been a research object in many scientific and technologic domains. Technological advances in sensors, data storage, computing resources and artificial intelligence have shown enormous potential to control diseases effectively. A growing body of literature recognizes the importance of using data from different types of sensors and machine learning approaches to build models for detection, prediction, analysis, assessment, etc. However, the increasing number and diversity of research studies requires a literature review for further developments and contributions in this area. This paper reviews state-of-the-art machine learning methods that use different data sources, applied to plant disease detection. It lists traditional and deep learning methods associated with the main data acquisition modalities, namely IoT, ground imaging, unmanned aerial vehicle imaging and satellite imaging. In addition, this study examines the role of data fusion for ongoing research in the context of disease detection. It highlights the advantage of intelligent data fusion techniques, from heterogeneous data sources, to improve plant health status prediction and presents the main challenges facing this field. The study concludes with a discussion of several current issues and research trends.

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“…Globally, about 150 countries are involved in the cotton industry, with an annual output value of more than USD 60 billion [ 1 , 2 ]. Outbreaks of cotton pests have directly or indirectly (transmission of plant diseases) caused 15–30% economic losses during production [ 3 , 4 , 5 ]. An attempt to reduce the damage has led to the overuse and misuse of chemical insecticides, leading to serious health and ecological problems, including harm to human and animal health, insecticidal resistance, pest resurgence, and environmental pollution [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Managing Practical Resistance of Lepidopteran Pests to Bt Cotton in China

Quan

2023

Insects

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China is one of the major cotton producers globally with small farmers. Lepidopteran pests have always been the main factor affecting cotton production. To reduce the occurrence of and damage caused by lepidopteran pests, China has employed a pest control method focused on planting Bt (Cry1Ac) cotton since 1997. Chinese resistance management tactics for the main target pests, the cotton bollworm and pink bollworm, were also implemented. For polyphagous (multiple hosts) and migratory pests such as the cotton bollworm (Helicoverpa armigera), the “natural refuge” strategy, consisting of non-Bt crops such as corn, soybean, vegetables, peanuts, and other host crops, was adopted in the Yellow River Region (YRR) and Northwest Region (NR). For a single host and weak migration ability pest, such as the pink bollworm (Pectinophora gossypiella), the seed mix refuge strategy yields a random mixture within fields of 25% non-Bt cotton by sowing second-generation (F2) seeds. According to field monitoring results for more than 20 years in China, practical resistance (Bt cotton failure) of target pests was avoided, and there were no cases of Bt (Cry1Ac) failure of pest control in cotton production. This indicated that this Chinese resistance management strategy was very successful. The Chinese government has decided to commercialize Bt corn, which will inevitably reduce the role of natural refuges; therefore, this paper also discusses adjustments and future directions of cotton pest resistance management strategies.

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Occurrence prediction of cotton pests and diseases by bidirectional long short-term memory networks with climate and atmosphere circulation

Cited by 50 publications

References 16 publications

Rice-Fusion: A Multimodality Data Fusion Framework for Rice Disease Diagnosis

Rice-Fusion: A Multimodality Data Fusion Framework for Rice Disease Diagnosis

Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research

Managing Practical Resistance of Lepidopteran Pests to Bt Cotton in China

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