To improve the mechanization levels of red cluster pepper harvesting in hilly and mountainous regions of southwest China, a crawler-type harvester is developed to suit the local planting and growth characteristics of red cluster pepper and to facilitate mechanized picking, conveying, and collecting processes. The design, supported by theoretical calculations and structural analysis, includes detailed studies of the picking, conveying, and collecting mechanisms, as well as the hydraulic system. Factors affecting picking efficiency were identified. ADAMS simulation was used to determine the optimum rotational speed range for the spring-tooth roller by analyzing its trajectory. A prototype was then built and field tested with forward speed and the spring-tooth roller’s rotational speed as variables to assess impurity, damage, loss, and hanging rates. Data from these tests were analyzed using Design Expert software, which created a mathematical model relating the test indices to the two variables. Optimum parameters were identified, resulting in a harvester configuration that achieved an average productivity of 0.21 ha·h⁻¹ at a forward speed of 1.75 m·s⁻¹ and a roller rotational speed of 181 r·min⁻¹. The impurity rate was 26.7%, the loss rate was 6.1%, the damage rate was 2.3%, and the hanging rate was 4.2%, conforming to the industry standard DG/T 114-2019. This research provides a viable solution for mechanized harvesting of red cluster pepper in hilly and mountainous regions with small planting plots.

To reduce harvest losses of a pepper harvester with a drum of elastic tooth type picking mechanism, this paper proposes an optimization method using AHP (Analytic Hierarchy Process) and RSM (Response Surface Methodology), thereby identifying the optimal harvesting parameters. Based on Hertz’s contact theory and projectile motion theory, dynamic and kinematic models were established for the picking and casting stage. Key parameters influencing harvest loss were identified as drum rotational speed, operating speed, and tooth spacing. A simulation model was constructed, and solved within LS-DYNA of ANSYS Workbench. A Box–Behnken design in RSM was employed to investigate the effects of drum rotational speed, operating speed, and tooth spacing on the picking rate, breakage rate, and loss rate. The optimal parameters, obtained through RSM optimization after AHP weighting, were determined to be a drum rotational speed of 182 r/min, an operating speed of 0.42 m/s, and a tooth spacing of 40 mm. A test bench was designed for validation, with simulation results deviating from experimental results by less than 5%. With optimized parameters, the picking rate increases from 89.73% to 95.13%, the breakage rate decreases from 3.21% to 2.66%, and the loss rate decreases from 5.16% to 3.95%. This study provides a theoretical foundation and practical reference for optimizing the drum with elastic tooth type picking mechanism in pepper harvesters.

The load of the elastic tooth drum-type pepper harvester is a key parameter affecting harvesting efficiency and quality. Real-time analysis and prediction of drum load are crucial for stabilizing harvester operation and optimizing performance. Existing research focuses on either machine vision-based image analysis, which is difficult to collect in the field, or parameter-mapping methods, which suffer from time lag. This study proposes a GARCH-KPCA-ATLSTM method for load prediction, combining the generalized autoregressive conditional heteroskedasticity (GARCH) model, kernel principal component analysis (KPCA), and attention-enhanced long short-term memory (ATLSTM). EMD is first applied to denoise and reconstruct the load signal, removing mechanical vibration and other interferences. Conditional heteroskedasticity is confirmed, and the GARCH series (one symmetric and three asymmetric models) is introduced to extract fluctuation features. KPCA reduces dimensionality, removing redundant information and saving 2.91 s in computation while slightly improving accuracy. Additive attention in LSTM emphasizes critical information, enhancing learning of nonlinear relationships and further improving prediction. Comparative experiments demonstrate the model’s reliability. The method achieves RMSE = 0.911, MAE = 0.682, MBE = −0.025, MAPE = 1.147%, R² = 0.968, with a runtime of 2.023 s, confirming high accuracy and stability. This study provides a theoretical and technical foundation for real-time load prediction of pepper harvesters.