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[学术文献 ] Occurrence and risk assessment of organochlorine pesticide residues in tea and herbal products in Spain 进入全文
Frontiers in Sustainable Food System 期刊
Organochlorine pesticides (OCP) are persistent pollutants well known for their ability to bioaccumulate. So, food contamination with these compounds is of concern for human health. The levels of nine OCP were determined in 60 samples of black tea and two herbal products (chamomile and linden) commercially available in Spain. The analysis was carried out by gas chromatography coupled with electron capture detector (GC-ECD), and confirmed by gas chromatography-mass spectrometry (GC-MS). The linearity, accuracy, precision, and limits of quantification and detection of the method were validated. OCP residues were detected in 66.7% of the samples at low levels, being always below the European maximum residue limits (MRL). No OCP was found in black tea samples, and only four pesticides were present in linden and chamomile products: 2,4′-DDD was the most frequently OCP detected, followed by aldrin, endrin, and 4,4′-DDD. The health risk assessment indicated that the presence of OCP in black tea and the two herbal products does not pose any risk to consumers.
[学术文献 ] TLDDM: An Enhanced Tea Leaf Pest and Disease Detection Model Based on YOLOv8 进入全文
Agronomy-Basel 期刊
This study proposes an enhanced Tea Leaf Disease Detection Model (TLDDM), an improved model based on YOLOv8 to tackle the challenges. Initially, the C2f-Faster-EMA module is employed to reduce the number of parameters and model complexity while enhancing image feature extraction capabilities. Furthermore, the Deformable Attention mechanism is integrated to improve the model’s adaptability to spatial transformations and irregular data structures. Moreover, the Slimneck structure is incorporated to reduce the model scale. Finally, a novel detection head structure, termed EfficientPHead, is proposed to maintain detection performance while improving computational efficiency and reducing parameters which leads to inference speed acceleration. Experimental results demonstrate that the TLDDM model achieves an AP of 98.0%, which demonstrates a significant performance enhancement compared to the SSD and Faster R-CNN algorithm. Furthermore, the proposed model is not only of great significance in improving the performance in accuracy, but also can provide remarkable advantages in real-time detection applications with an FPS (frames per second) of 98.2.
[学术文献 ] Tea mosquito bug, Helopeltis spp. (Miridae; Hemiptera) an emerging phytotoxic pest: A comprehensive review of its biology and management 进入全文
Plant Science Today 期刊
The Tea Mosquito Bug (TMB), Helopeltis spp. (Miridae: Hemiptera), is a highly destructive pest that significantly threatens plantation and fruit crops across tropical and subtropical regions worldwide. Although chemical control methods especially insecticides have traditionally been used for managing Helopeltis spp., excessive reliance on these methods has led to various challenges. These include the development of pesticide resistance, environmental pollution and concerns about pesticide residues in agricultural products resulting in strict regulations in many developed nations. As a result, there is a pressing need for alternative and sustainable management strategies. Integrated Pest Management (IPM), which incorporates biological control agents, cultural practices, botanical insecticides and selective chemical use has emerged as a promising and environmentally sound approach for controlling Helopeltis spp. in an economically viable way. This review provides an in-depth assessment of the biology, ecology and behavior of Helopeltis spp., along with a comprehensive analysis of their global host plant range. Furthermore, it highlights recent advancements in pest management, particularly focusing on IPM strategies and ongoing research into biological control, such as utilizing natural predators and parasitoids. By reducing dependency on chemical pesticides, these sustainable practices are crucial for minimizing the impact of TMB on global agricultural systems enhancing crop resilience and promoting long-term environmental sustainability.
[相关专利 ] VARIATIONAL RELU MODEL FOR PEST INFECTION PREDICTION IN TEA SAMPLES 进入全文
印度专利
茶叶病害，如赤霉病和褐根腐病可以手动识别，但需要更多的时间，影响产量和生产。为了克服这些问题，本研究提供了基于AI的说明，用于从四个不同茶园收集的茶叶数据样本。AI模型用于快速单相训练对象疾病识别。从茶树中收集了五种茶叶病害，并将其组织成数字图像，生成人工解释和增强的茶叶数据样本。本研究中使用的数据增强方法解决了数据集不足的问题。识别并测量了新方法的识别精度、AP属性、召回率和F1值等验证因素。结果表明，该方法的表达因子分别为93%、93.5%、93.2%和94%。传统技术被证明比系统变异RELU模型（VRM）产生更少的实验输出。因此，该研究增强了对各种茶叶病害的检测和识别，减轻了研究人员的工作负担，从而提高了经济效率。
[会议论文 ] Effective Plantation Management with Crowd-sensing and Data-driven Insights: A Case Study on Tea 进入全文
2020 IEEE GHTC 会议
With effective digital plantation management as an end-objective, we present our work on development of the framework constructs to (a) digitise pest management activities to record crop-stress data along with field operations, and (b) build insights from the data to respond faster to stress incidents with precise control measures. As part of the digitisation, we employed design thinking concepts and a human-centric approach to develop user-friendly interfaces where crowd-sensing with the help of ground staff is used as a foundational activity. Descriptive and diagnostic insights on the gathered data were brought out to correlate incidents with operations based on aggregated patterns, and generate deep insights on crop images with artificial intelligence. Image-based insights include localisation and recognition of symptoms associated with insect pests, diseases, and nutrient deficiencies that were non-trivial to get earlier through manual operations. Such insights were used to generate system recommendations that support experts in issuing effective advisory towards curative action on the field thus sowing the seeds for an Industry 4.0 future for plantations.
[学术文献 ] Teapest: An Expert System for Insect Pest Management in Tea 进入全文
Applied Engineering in Agriculture 期刊
Tea is one of the major crops of India and is grown over a large area. The loss of crop due to insects is one of the productivity barriers. Insect pest management is a challenging problem to tea cultivators. Proper identification of the insect pests, selection of chemical pesticides and their discriminate use, need human expertise, experience, and judgment. But, sufficient number of competent human experts are not available to cover the large area. To mitigate the lack of human expertise and assist the existing experts for improved decision-making, an expert system for insect pest management would be useful. This article presents a rule-based, object-oriented expert system for insect pest management in tea code named 'TEAPEST.' The system identifies major insect pests of tea and suggests appropriate control measures. 'TEAPEST' shows good performance as evident from its performance evaluation.
