Traditional seed supply chains face several hidden risks. Certain regulatory departments tend to focus primarily on entity circulation while neglecting the origin and accuracy of data in seed quality supervision, resulting in limited precision and low credibility of traceability information related to quality and safety. Blockchain technology offers a systematic solution to key issues such as data source distortion and insufficient regulatory penetration in the seed supply chain by enabling data rights confirmation, tamper-proof traceability, smart contract execution, and multi-node consensus mechanisms. In this study, we developed a system that integrates blockchain and neural networks to provide seed traceability services. When uploading seed traceability information, the neural network models are employed to verify the authenticity of information provided by humans and save the tags on the blockchain. Various neural network architectures, such as Multilayer Perceptron, Recurrent Neural Network, Fully Convolutional Neural Network, and Long Short-term Memory model architectures, have been tested to determine the authenticity of seed traceability information. Among these, the Long Short-term Memory model architecture demonstrated the highest accuracy, with an accuracy rate of 90.65%. The results demonstrated that neural networks have significant research value and potential to assess the authenticity of information in a blockchain. In the application scenario of seed quality traceability, using blockchain and neural networks to determine the authenticity of seed traceability information provides a new solution for seed traceability. This system empowers farmers by providing trustworthy seed quality information, enabling better purchasing decisions and reducing risks from counterfeit or substandard seeds. Furthermore, this mechanism fosters market circulation of certified high-quality seeds, elevates crop yields, and contributes to the sustainable growth of agricultural systems.

Under the dual imperatives of environmental protection and sustainable socio-economic development, the high energy consumption of the manufacturing sector has become a significant barrier to its transition toward industrial ecological transformation. Integrating machine tool energy-saving technologies into engineering education serves as a pivotal strategy for enhancing energy efficiency and reducing operational consumption while fostering professionals who possess both sustainability awareness and technical expertise. Building on the core principles and future applications of energy-efficient machining, this study introduces a targeted curriculum reform. In response to challenges including cross-disciplinary content, heavy theoretical demands, and insufficient experiential learning, a blended teaching framework is designed, integrating virtual instruction with practical factory-based training. Implementation outcomes demonstrate that this reform significantly enhances students’ comprehension of energy-saving mechanisms and their capacity to apply sustainable concepts in practical engineering contexts. This study provides a reference for integrating energy-saving education into higher education under the framework of sustainable development. It also offers educational support for the manufacturing industry’s pursuit of low-carbon transition and industrial ecological development, demonstrating the important role of engineering education in advancing ecological civilization and contributing to the broader environmental governance system.

Clubroot, caused by Plasmodiophora brassicae, is a destructive disease of Chinese cabbage (Brassica rapa ssp. pekinensis) at all growing stages. Early detection of the disease is essential to mitigate the impact of clubroot. Here, we established an optimal algorithm for multispectral imaging combined with machine learning to detect leaf responses of highly susceptible cultivar YoulvNo.3 at different day after inoculation (DAI). Spectral data at 19 wavelengths were collected from leaf multispectral images, and key characteristic wavelengths were further extracted. Principal Component Analysis (PCA) revealed a clear separation between healthy and infected samples at 11 DAI. Four classification algorithms, including Random Forest (RF), Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM) and Extreme Learning Machine (ELM), were employed to construct early detection model for clubroot. SVM achieved over 81% accuracy with full-spectrum data, while ELM based on characteristic wavelengths provided the best performance, accuracy exceeding 84%. Stratified five-fold cross-validation was used to validate the optimal model. An average accuracy of 83.79% (±1.04%) and macro-averaged F1-score of 82.13% (±1.12%) across validation folds were obtained, confirming stable performance. Our findings, for the first time, identified detectable spectral differences between the healthy and infected plants at 11 DAI using leaf multispectral combined with machine learning, providing a potential application for early detection of clubroot and timely control in Chinese cabbage.

The seed-germination percentage is an important indicator of the seed viability and growth potential and has important implications for plant breeding and agricultural production. Thus, to increase the speed and accuracy in measuring the completion of germination in experimental seed batches for precise germination percentage calculation, we evaluated a You-Only-Look-Once (YOLO)–Seed Germination Detection (SGD) algorithm that integrates deep-learning technology and texture feature-extraction mechanisms specific to germinating seeds. The algorithm was built upon YOLOv7-l, and its applicability was optimised based on the results of our germination experiments. In the backbone network, an internal convolution structure was substituted to enhance the spatial specificity of the initial features. Following the output of the main feature-extraction network, an Explicit Visual Centre (EVC) module was introduced to mitigate the interference caused by intertwined primary roots from germinated seeds, which can affect recognition accuracy. Furthermore, a Spatial Context Pyramid (SCP) module was embedded after enhancing the feature-extraction network to improve the model’s accuracy in identifying seeds of different scales, particularly in recognising small target seeds. Our results with cabbage seeds showed that the YOLO–SGD model, with a model size of 45.22 M, achieved an average detection accuracy of 99.6% for large-scale seeds and 96.4% for small-scale seeds. The model also achieved a mean average precision and F1 score of 98.0% and 93.3%, respectively. Compared with manual germination-rate detection, the model maintained an average absolute error of prediction within 1.0%, demonstrating sufficient precision to replace manual methods in laboratory environments and efficiently detect germinated seeds for precise germination percentage assessment.

Eggplant seed vigor is a crucial indicator of its germination rate and seedling growth quality. In response to the need for efficient and nondestructive assessment methods, this study explores the use of hyperspectral imaging combined with advanced feature selection and classification algorithms to evaluate eggplant seed viability. Hyperspectral imaging was employed to collect spectral data from eggplant seeds, covering 360 bands within a wavelength range of 395.24–1008.20 nm. The seeds underwent microwave heating and constant-temperature water bath aging treatments. Data preprocessing involved three techniques: Multiplicative Scatter Correction (MSC), Savitzky–Golay (SG) smoothing, and Standard Normal Variate (SNV) transformation. An Enhanced Information Acquisition Optimization (EIAO) algorithm was proposed for feature selection, which successfully identified a minimal set of 23 key wavelengths. Seed vigor classification models were developed using Extreme Learning Machine (ELM), Random Forest (RF), and Support Vector Machine (SVM).The optimal classification accuracies achieved were 90.0% for ELM, 91.45% for RF, and 90.5% for SVM. The MSC-EIAO-RF model demonstrated the best performance, achieving an accuracy of 91.45%, which is 9.04% higher than the MSC-IAO model (82.41%).Validation on four UCI datasets further confirmed the EIAO algorithm's superiority over conventional feature selection methods. These results verify the robustness and generalizability of hyperspectral imaging combined with EIAO for nondestructive seed viability detection, offering an intelligent and efficient solution for seed quality assessment.

Precise detection of cotton apical buds is the primary step toward achieving intelligent topping operations. Existing object detection models still struggle to accurately recognize dense small targets under complex field conditions. In this study, we propose an improved model, MCA-YOLO, based on YOLOv11n, and optimize it from three aspects: feature extraction, computational efficiency, and multi-scale feature fusion. First, we introduce the MLCA attention mechanism into the PSABlock to construct the C2PSA_MLCA module, enhancing the model’s capability to represent both local and global features. Second, a CSPHet module is reconstructed using heterogeneous convolution (HetConv) combined with a dual-path design to reduce convolutional redundancy and improve feature extraction efficiency. Finally, the original YOLOv11n detection head is replaced with an ASFFHead, enabling adaptive multi-scale feature fusion, thereby improving detection performance for small, dense, and scale-varying targets. Experimental results show that MCA-YOLO achieves Precision, Recall, mAP@0.5, and F1-score of 89.0%, 83.1%, 90.6%, and 85.9%, corresponding to improvements of 3.0, 8.1, 7.1, and 5.8 percentage points over YOLOv11n. Compared with YOLOv11n, the parameters and GFLOPs increase by 50.0% and 31.7%. Even with this increase in model complexity, MCA-YOLO achieves 75 FPS with a model size of 7.76 MB, indicating that it maintains real-time detection capability while improving detection accuracy.