Search Results (4)

Aphids are the main agricultural pests that affect the quality and yield of peppers in the greenhouse. Efficient early prediction of aphid occurrence is of great significance for the development of digitization and information technology in intelligent agriculture. Forecasting accuracy could be improved by the incorporation of feature interactions into pest forecasting. This study integrates multiple environmental factors to efficiently predict the number of aphids and the aphid strain rate in the greenhouse. We propose a two-level distributed multi-source information fusion approach, which integrates a one-dimensional convolutional neural network (1D CNN) and Long Short-Term Memory (LSTM). To enhance the accuracy of regional environmental parameters, a weighted average algorithm employs environmental sensor data in the first level of fusion. In the second fusion level, a heterogeneous sensor fusion algorithm allows for the integration of multi-source data to model the connection between environmental factors and aphid dynamics. Finally, the improved 1D CNN-LSTM fusion model and other models were tested to verify the effectiveness and robustness of the proposed model. The experimental results show that the total root mean square error of the proposed model is 1.503, which is obviously better than the other networks. In the test set, the total root mean square error of the model for predicting the aphid number and strain rate is 1.378 and 0.337, respectively, compared with existing network models such as 1D CNN, LSTM, and back propagation (BP). The experimental results show that the proposed model has obvious advantages for predicting the aphid number and strain rate. It provides a promising step forward in pest management, offering precise, environmentally friendly solutions that enhance crop yield and quality. Full article

Citrus tristeza virus (CTV) is a vector-borne virus that poses a significant threat to citrus production worldwide, inducing a variety of symptoms. Therefore, a detailed knowledge of local aphids, identification of viruliferous species, and the development of new monitoring tools are necessary to improve CTV control strategies. Herein, a 2-year survey was conducted to assess the frequency of aphid species infesting several citrus pilot orchards. Plot findings based on morphological and molecular identification revealed Aphis spiraecola (ranged from 44–100%) as the most abundant aphid species, followed by A. gossypii (<50%). Toxoptera aurantii, Myzus persicae, and A. craccivora were present in low numbers, and A. citricidus was not detected. Due to the absence of CTV detection in aphids and citrus trees from the pilot orchards, a complementary survey was conducted in CTV-infected fields. Three aphid species were identified as CTV-positive by RT-PCR, suggesting that they may be viruliferous, with A. spiraecola as predominant, followed by A. gossypii and T. aurantii. Additionally, we developed a non-invasive procedure for identifying aphid species using wingbeat analysis. This method provides a faster alternative to traditional identification techniques by taxonomic keys based on morphological features or PCR, although its accuracy is lower (approximately 95% for the two species tested). Overall, this work provides a detailed study of aphid species composition in citrus orchards, identifies the predominant local putative CTV vector, and introduces a novel sensor for aphid monitoring, contributing to improved epidemic forecasting and sustainable disease management strategies. Full article

Insecticide use in agriculture has significantly increased over the past decades, reaching 774 thousand metric tons in 2022. This widespread reliance on chemical insecticides has substantial economic, environmental, and human health consequences, highlighting the urgent need for sustainable pest management strategies. Early detection, insect monitoring, and population forecasting through Artificial Intelligence (AI)-based methods, can enable swift responsiveness, allowing for reduced but more effective insecticide use, mitigating traditional labor-intensive and error prone solutions. The main challenge is creating AI models that perform with speed and accuracy, enabling immediate farmer action. This study highlights the innovating potential of such an approach, focusing on the detection and prediction of black aphids under state-of-the-art Deep Learning (DL) models. A dataset of 220 sticky paper images was captured. The detection system employs a YOLOv10 DL model that achieved an accuracy of 89.1% (mAP₅₀). For insect population prediction, random forests, gradient boosting, LSTM, and the ARIMA, ARIMAX, and SARIMAX models were evaluated. The ARIMAX model performed best with a Mean Square Error (MSE) of 75.61, corresponding to an average deviation of 8.61 insects per day between predicted and actual insect counts. For the visualization of the detection results, the DL model was embedded to a mobile application. This holistic approach supports early intervention strategies and sustainable pest management while offering a scalable solution for smart-agriculture environments. Full article

The ecological plasticity of aphid populations is determined by their clonal and morphotypic diversity. Clones will be successful when the development of their component morphotypes is optimized. The purpose of this work was to reveal the peculiarities of clonal composition and the developmental characteristics of different summer morphotypes for the rose-grass aphid, Metopolophium dirhodum (Walk.), which is an important host-alternating cereal pest and a useful model species. During the experiments, aphids were kept under ambient conditions on wheat seedlings at natural temperatures and humidity levels. An analysis of the reproduction of summer morphotypes and the resulting composition of offspring found that variation among the clones and morphotypes, as well as generational effects and an influence of sexual reproduction (and interactions between all factors) influenced the population structure of M. dirhodum. The reproduction of emigrants was less among the clones than that of the apterous or alate exules. The number of offspring produced by apterous exules differed throughout the growing season (generational effects) and between years, with different clones exhibiting different responses. There were dispersing aphids only among the offspring of apterous exules. These results can contribute to future advances in the forecasting and monitoring of aphid populations. Full article