The rapid advancement of autonomous agricultural systems (AASs) is transforming modern agriculture, where labor shortages, sustainability imperatives, and demands for precision farming are driving the adoption of intelligent agricultural platforms. Agricultural production environments present uniquely challenging conditions for autonomous agricultural systems, including unstructured and dynamically changing terrain, biologically variable targets, unpredictable illumination and weather conditions, and safe human–machine coexistence. This review systematically investigates three cornerstone technologies: multi-source perception, intelligent decision-making, and precision control. Furthermore, typical agricultural operations, including soil tillage, planting, irrigation and drainage, fertilization, plant protection, harvesting, and agricultural product processing, are reviewed to illustrate their applications. Based on representative operational scenarios, the research progress and application characteristics of intelligent equipment in environmental perception, operational optimization, and control execution are summarized. Specifically, multi-source perception is evolving from isolated sensor-based acquisition toward multimodal and deep learning-enabled semantic scene understanding. Intelligent decision-making has evolved from experience-driven approaches toward physics-informed, data-driven, and knowledge-enhanced frameworks for adaptive operational optimization. Precision control has progressed from conventional PID control toward adaptive, learning-based, and digital twin-enabled control strategies, achieving robust high-precision closed-loop regulation. However, several critical challenges persist: limited cross-domain generalization and robustness of perception models under environmental distribution shift, constrained interpretability and trustworthiness of data-driven decision systems, and insufficient adaptability of control architectures under multi-disturbance coupled field conditions. To address these gaps, future research should prioritize multi-source heterogeneous data fusion and standardization, collaborative control frameworks integrating mechanistic knowledge with data-driven learning, and explainable artificial intelligence combined with agricultural domain expertise—advancing toward genuinely autonomous, trustworthy, and resilient agricultural systems.

In deep and ultra-deep drilling, high downhole temperatures cause appreciable thermal drift in the strain-gauge bridge of the bit parameter recorder, degrading the measurement fidelity of weight-on-bit (WOB) and torque. This paper presents a temperature-drift compensation scheme combining electric eel foraging optimization (EEFO) with a physics-informed extreme learning machine (PIELM). Four physical priors are formulated as constraints: trend consistency and regression deviation are embedded into the output-weight solution through an augmented linear system, while proportional relationship and continuity enter the fitness function as penalty terms. EEFO performs the hyperparameter search; enhancement strategies addressing its population-diversity decay and limited final-stage accuracy are examined under an equal function-evaluation budget, and the deployed configuration is determined by the ablation evidence. Under leakage-free nested cross-validation, hyperparameter optimization reduces the WOB RMSE from 0.712 kN with fixed hyperparameters to 0.246 kN, a 2.9-fold improvement, while a ridge-regularized ELM baseline reaches 0.708 kN, indicating that the gain originates from the hyperparameter optimization itself. Equal-budget ablation on 13 classical benchmark functions ranks the dynamic-boundary quasi-opposition-based learning variant as the most effective single strategy, and the deployed configuration attains the lowest mean fitness on the target problem. Downhole evaluation is formulated as a consistency-verification framework, with no downhole load ground truth and the WOB voltage lying outside the training range. The apparent drift elimination under in-sample mapping does not survive leakage-free mapping protocols (the WOB elimination rates are 0.3%, 0.0%, and 5.1% under P2–P4) and is therefore not interpreted as a compensation-accuracy figure.