The introduction of a consortium blockchain-based agricultural machinery scheduling system will help improve the transparency and efficiency of the data flow within the sector. Currently, the traditional agricultural machinery centralized scheduling systems suffer when there is a failure of the single point control system, and it also comes with high cost managing with little transparency, not leaving out the wastage of resources. This paper proposes a consortium blockchain-based agricultural machinery scheduling system for solving the problems of single point of failure, high-cost, low transparency, and waste of resources. The consortium blockchain-based system eliminates the central server in the traditional way, optimizes the matching function and scheduling algorithm in the smart contract, and improves the scheduling efficiency. The data in the system can be traced, which increases transparency and improves the efficiency of decision-making in the process of scheduling. In addition, this system adopts a crowdsourcing scheduling mode, making full use of idle agricultural machinery in the society, which can effectively solve the problem of resource waste. Then, the proposed system implements authentication access mechanisms, and allows only authorized users into the system. It includes transactions based on digital currency and eliminates third-party platform to charge service fees. Moreover, participating organizations have the opportunity to obtain benefits and reduce transaction costs. Finally, the upper layers supervision improves the efficiency and security of consensus algorithm, allows supervisors to block users with malicious motives, and always ensures system security.

Machine learning and deep learning techniques integrated with advanced sensing technologies have revolutionized agricultural engineering, addressing complex challenges in food production, quality assessment, and environmental monitoring. This survey presents a systematic review and meta-analysis of recent developments by examining the peer-reviewed literature from 2015 to 2024. The analysis reveals computational approaches ranging from traditional algorithms like support vector machines and random forests to deep learning architectures, including convolutional and recurrent neural networks. Deep learning models often demonstrate superior performance, showing 5–10% accuracy improvements over traditional methods and achieving 93–99% accuracy in image-based applications. Three primary application domains are identified: agricultural product quality assessment using hyperspectral imaging, crop and field management through precision optimization, and agricultural automation with machine vision systems. Dataset taxonomy shows spectral data predominating at 42.1%, followed by image data at 26.2%, indicating preference for non-destructive approaches. Current challenges include data limitations, model interpretability issues, and computational complexity. Future trends emphasize lightweight model development, ensemble learning, and expanding applications. This analysis provides a comprehensive understanding of current capabilities and future directions for machine learning in agricultural engineering, supporting the development of efficient and sustainable agricultural systems for global food security.