Agriculture is crucial for food production and rural economies, yet pest infestations significantly reduce crop yields. Traditional pest management heavily relies on manual monitoring and expert experience, limiting its automation potential. While recent advances in automated pest detection have improved early identification, integrating expert knowledge to support automated decision-making remains challenging. To address these challenges, this study proposes an intelligent diagnostic framework integrating object detection, retrieval-augmented technology, and Large Language Models (LLMs). In the object detection stage, a customized YOLOv8 model is optimized through data augmentation and an Adaptive Feature Pyramid Network, achieving pest identification and lightweight optimization. Subsequently, automated online retrieval technology extracts relevant pest control information, while a locally deployed DeepSeek LLM analyzes, filters, and summarizes the retrieved content to generate professional recommendations. Experimental results validate the effectiveness of the framework. In the information processing stage, retrieval-augmented LLMs effectively mitigate the "hallucination" phenomenon, significantly enhancing the professionalism and credibility of generated recommendations. Concurrently, the optimized object detection stage achieves remarkable results. On the Pest24 dataset, the improved YOLOv8 model demonstrates a significant performance boost, with [email protected] increasing by 13%, model size reducing by 30%, and an inference speed of 275 FPS. Compared to standalone LLM-based systems, our proposed intelligent diagnostic framework demonstrates enhanced decision-making reliability through multi-stage collaborative optimization, addressing both model hallucination and detection efficiency. This work provides a scalable solution for intelligent pest management.

Pests significantly threaten global agricultural production, which causes severe yield losses through feeding and virus transmission. To mitigate yield losses caused by pests, timely and precise pest management practices are critical. Although previous efforts have advanced automated solutions for real-time environmental monitoring in agriculture, implementing precise pest management decision-making and suggestion generation remains a challenge due to complex reasoning processes in practice. In response, an enhanced pest management system, PEZEGO, is proposed to provide precise management suggestions through multimodal environmental data, a fine-tuned open vocabulary detector (OVD), and large language models (LLMs). Specifically, a mobile application and low-cost Internet of Things (IoT) devices are developed to capture images and environmental information. A hybrid convolutional low-rank adaptation method (HCLoRA) is proposed to fine-tune pretrained OVDs, enabling zero-shot pest detection for converting images to pest species and quantity information. In addition, a structured data-based retrieval augmented generation (SRAG) workflow for LLMs is proposed to provide precise pest management suggestions through automatically extracted agriculture management knowledge and Chain-of-Thought. The effectiveness of PEZEGO is validated in a case study of pest management in the U.K., including pest detection in field scenarios and management suggestion generation. Compared to advanced model fine-tuning methods, HCLoRA for YOLOWorld achieves the highest detection performance with$0.1759~AP^{h}$on pest detection. Additionally, the proposed SRAG workflow demonstrates the ability to support pest management with a 68.7% average F1 score for knowledge extraction and 77.33% accuracy for suggestion generation. Eventually, a mobile application demonstrates the practical effectiveness of the proposed system.

Alzheimer’s disease (AD) is the leading cause of dementia worldwide, characterized by its gradual progression and the subtle variations across disease stages, which pose significant challenges for accurate diagnosis. While deep representation learning algorithms have shown promise in the early detection of AD using MRI data, existing approaches often overlook the meaningful relationships between continuous labels in AD progression, and the learned representations frequently lack interpretability due to the black-box nature of deep learning models. To address these limitations, we propose ICReL (Interpretable Continuous Representation Learning), a novel concept based on coding rate principles that captures continuous representations across AD stages while maintaining a high degree of interpretability. Extensive experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate that ICReL not only outperforms multiple baseline methods using 2D slice or 3D MRI in terms of learning continuous representations, but also exhibits enhanced robustness to label corruption and superior predictive performance. This work offers a new, interpretable approach to representation learning for computer-aided diagnosis of neurodegenerative diseases.

Promoting sustainable agriculture plays a crucial role in reducing greenhouse gas (GHG) emissions, lowering the carbon footprint, and improving farm resilience. Three challenges must be overcome to achieve sustainable agriculture management. Firstly, there is a lack of reliable and sustainable fertiliser solutions to improve fertiliser use efficiency, reduce GHG emissions while maintaining crop production. In addition, how to cost-effectively quantify the response of soil carbon and GHG fluxes to different fertilisation practices. Thirdly, there is a requirement to integrate multi-source farming data and AI models into a farm management information system (FMIS) to support intelligent decisions for farmers. To address these challenges, we developed the ParellelFarm, an AI-enabled sustainable farming management system that integrates multi-source farming data and AI-driven fertiliser and soil carbon models into a multi-tenant cloud platform, to support sustainable farming. It also provides remote field visualisation and management as well as instant messaging via web and mobile clients, supporting fast and accurate labour allocation with fewer resources. It is a potential solution for a cost-effective, highly productive and sustainable modern net-zero farm.

In the rapidly evolving field of artificial intelligence, large language models (LLMs) have attracted much attention from researchers in various fields due to their unexpected text generation and comprehension capabilities. However, the applications of LLMs for sustainable pest management are under-explored due to the heavy reliance on specialized expert knowledge. In addition, evaluating the quality of LLMs' content is another technological challenge for applying LLMs in sustainable pest management. Therefore, we propose an instruction-based prompting method that integrates pest expert knowledge into the prompt, equipping LLMs with the necessary context to generate more accurate and relevant pest management advice. Furthermore, we propose an LLM-based evaluation framework to score the generated content on Coherence, Logical Consistency, Fluency, Relevance, Comprehension, and Exhaustion. Additionally, we integrate an Expert System based on crop threshold data as a baseline to obtain scores for Accuracy on whether pests found in crop fields should take management action. Each model's score is weighted by percentage to get a final score. The results show that GPT-3.5 and GPT-4 outperform the FLAN models in most evaluation dimensions. Furthermore, while using instruction-based prompting containing domain-specific knowledge outperforms other prompting methods with an accuracy of 72%, ongoing refinements and assessments of end-user satisfaction are essential to enhance the LLMs' effectiveness and practical helpfulness in providing pest management advice.

Pest management is an important factor affecting agricultural and food industry products. A large number of insect species and the subtle differences bring a challenge to the accurate recognition of pests. Many studies tackle the challenge of pest recognition through deep neural networks (DNNs) and achieve significant success in terms of accuracy. However, the complex structure and a large number of parameters make DNNs difficult for end users to understand the reasons for the decision of models, which causes distrust in the classification of harmful insects and overuse of insecticides. To address the lack of explainability of DNNs, we propose an explanation generation workflow to generate concept-based explanations for pest recognition. Specifically, the concept extraction method uses a clustering algorithm to extract image segments with meaningful concepts from a portion of the training dataset. Then, concept models are trained to detect the presence of concepts in the image. Finally, the explanation generation method provides concept-based global and local explanations in the form of weighted directed graphs and concept importances, respectively. Through qualitative and quantitative analysis, the proposed workflow extracts meaningful concepts for pest recognition effectively and detects the presence of concepts in images.

Every year, plant parasitic nematodes, one of the major groups of plant pathogens, cause a significant loss of crops worldwide. To mitigate crop yield losses caused by nematodes, an efficient nematode monitoring method is essential for plant and crop disease management. In other respects, efficient nematode detection contributes to medical research and drug discovery, as nematodes are model organisms. With the rapid development of computer technology, computer vision techniques provide a feasible solution for quantifying nematodes or nematode infections. In this paper, we survey and categorise the studies and available datasets on nematode detection through deep-learning models. To stimulate progress in related research, this survey presents the potential state-of-the-art object detection models, training techniques, optimisation techniques, and evaluation metrics for deep learning beginners. Moreover, seven state-of-the-art object detection models are validated on three public datasets and the AgriNema dataset for plant parasitic nematodes to construct a baseline for nematode detection.

With the increasing demand for cost-effective crop pest management solutions, how to achieve effective and efficient automatic pest detection has become the primary research problem. Traditional object detection methods that rely on the quality of handcrafted feature selection are hardly used in pest detection due to the difficulty of designing the features of multiple types of pests. The application of deep learning which presents outstanding performances in object detection tasks faces the following challenges in the field of pest detection. First, the detection difficulties caused by tiny-size pests and protective colouration limit the accuracy of detection. Second, pest detection requires the employment of experts to obtain the annotation of pests for training models, which is costly. Finally, the ability to run on lightweight devices is required due to the limitations of the field environment on networks and equipment. To solve these problems, this paper focuses on a lightweight tiny object detection model, training on limited supervised samples through different data augmentation methods. Different components of object detection models and data augmentation methods are analysed in different sizes of training datasets. Finally, a method based on the Yolo detection model is proposed for pest detection. This pest detection model is evaluated on a real-world aphids data set containing 6k objects. Five sets of data augmentation methods are used on seven sizes of training data sets for analysis. Then the structure of the detection neck of the Yolo model is analysed. Our experimental results show that 54.35% mAP can be achieved by the PAN module and removing the Mosaic data augmentation method for tiny object detection with one hundred samples.