Conventional chemical weeding has seriously threatened the sustainable orchard in recent years, such as soil ecosystem degradation, water contamination, and herbicide-resistant weeds. In this study, a cutter-front weeding robot was designed, optimized, and evaluated for efficient, environmentally friendly, and reliable mechanical weeding. A robotic platform was also provided for the weeding practices. An extended-range hybrid system (lithium iron phosphate battery + diesel generator) was provided for the 8-12 h powertrain during operation. A crawler system with the optimal parameters was utilized for the low ground pressure and gradability. An intelligent overload protection was also featured for the weeding motor and Y-shaped flailing blades. A virtual model was developed to simulate the blade-soil-weed interactions using EDEM software. A three-factor and three-level response surface experiment was conducted to investigate the blade arrangement, cutter speed, and height versus the missed cutting rate. The analysis of variance (ANOVA) was finally evaluated on the performance under the optimal parameters. The results demonstrated that the rotational speed of the cutter shaft was the statistically most significant influencing factor on the missed cutting rate, followed by the cutter shaft height and the blade arrangement pattern. The optimal combination of the parameters was achieved to minimize the missed cutting rate: a double-helix blade arrangement, a rotational speed of 2 286.246 r/min, and a cutter shaft height of 189.823 mm. Field experiments were conducted to validate the exceptional accuracy of the model under the optimal combination. The average actual missed cutting rate was recorded as only 4.77% during field trials. There was a very close agreement with the prediction, indicating a better overall optimization after simulation. Beyond the primary metrics, the operational performance of the robot was exceptional over the rest indicators. The stubble height stability coefficient consistently exceeded 90%, indicating a highly uniform cutting height over undulating terrain. Similarly, the average cutting width utilization coefficient was measured to be greater than 90%, indicating the effective working width despite ground irregularities. The crawler chassis provided stable and reliable traction during experiments, even on the loose and uneven orchard surfaces. The intelligent overload protection system performed best during tests, such as the sudden load increase, due to the entanglement with the dense vegetation or impact with concealed solid obstacles. Current sensor data was filtered to successfully discriminate between transient fluctuations and genuine overload conditions, thus triggering the electro-hydraulic lift mechanism to raise the cutter head promptly and then prevent motor stall or damage. The robustness and autonomy of the system were significantly enhanced after optimization. Lastly, the Y-shaped blade cutting mechanism was operated with high efficiency. The clean cuts of various weed species were achieved for minimal soil disturbance, thereby effectively preserving the topsoil structure for the less damage to tree roots. In conclusion, the cutter-front orchard weeding robot demonstrated as a high-performance and ecologically sound alternative to conventional chemical weeding. There was high weeding efficiency, minimal environmental impact, and reliable operation under orchard environments. As such, the mechanical optimization, numerical simulation, and field experimental validation can be expected to develop the intelligent equipment in modern agriculture. Subsequently, a closed-loop control system can also be developed to enhance the adaptability for the very short weeds in the real-time terrain. The cutting consistency can also be improved to integrate the advanced autonomous navigation and obstacle avoidance, in order to achieve full operational autonomy.

Variable fertilization is an important technical approach in implementing precision agriculture. The method of external groove wheel-type variable fertilization with dual regulation of speed and aperture is a typical operation method for crop production (planting) in rice-wheat rotation areas. In response to current issues with variable fertilizer applicators such as slow control system response, inaccurate prediction models, large fertilizer amount errors, and insignificant effectiveness, this study, based on a self-developed dual-variable precision fertilizer applicator for rice and wheat, proposed a method for constructing a fertilizer amount prediction model based on a multilayer perceptron artificial neural network using mathematical statistics and machine learning methods, and verified its effectiveness and applicability. By analyzing the algorithm mechanisms of the levy flight algorithm (LFA), particle swarm optimization (PSO), and multilayer perceptron (MLP) neural network models, and combining the dual-variable fertilization method of aperture-speed, a fertilizer amount prediction model based on LFA-PSO-MLP (LPM) was constructed. The model incorporated the aperture-speed-fertilizer amount relationship, improved algorithm structure through normalization, regularization, etc., conducted parameter optimization and model training, and compared the MLP and PSO-MLP models to obtain the optimal LFA-PSO-MLP fertilizer amount prediction model. Furthermore, an inverse LFA-PSO-MLP (ILPM) prediction model was constructed to quickly calculate the required aperture and speed based on the target fertilizer amount. Experimental results showed that the LFA-PSO-MLP model converged in about 50 iterations, with an R² value of 0.999 after 500 iterations and a average relative error (ARE) of 1.83%, which was better than the other two models. Validation tests of the LPM model yielded an average relative error of 2.47% between predicted and validation values, while field experiments showed an average relative error of 3.49% between predicted and measured values. For the ILPM model, the average relative error for rotation speed prediction was 1.82%, and in field experiments, the maximum relative error between target and actual fertilization rates was 7.26%, with an average relative error of 6.09%. This indicated that the fertilizer applicator equipped with the ILPM model performed well in fertilizer application. The study demonstrated that the proposed model construction method can ensure the accuracy of fertilizer amount prediction while improving computational efficiency, achieving fast, precise, and efficient variable fertilization, and improving ecological and economic benefits.