In the rice-wheat system, using straw for soil incorporation provides better soil health and improves agricultural production. The experiment was performed in Babaiqiao town, Jiangsu Province, China’s Luhe District, Nanjing City, in June 2024 using a Shichao TG-500 tractor equipped with a Qingxuan 1GKN-180 rotary cultivator. The impacts of the three tillage practices, deep rotary tiller with straw (DRTS), shallow rotary tiller with straw (SRTS), and no-tillage with straw return (NTSR), on the level of soil disturbance were observed in the single-factor and two-factor interaction experiments. Based on the profilometry analysis, it was observed that DRTS had the highest value of soil disturbance while SRTS had a moderate disturbance value and NTSR minimized disturbance. The effects of working depths, forward speed, and rotation speed on the straw return rate have been evaluated by further investigations. The results showed that enhancing straw return rates was significantly impacted by changing the tilling depths and the rotation speeds, especially when using deeper tillage and moderate to high rotary speeds. The investigation found that the forward speed, blade rotation speed, and tillage depth explained the overall rates of straw return, soil breaking, and soil flatness. In the research, the response surface design employed was the Box–Behnken Design (BBD). The optimal operating parameters were 14.23 cm of plowing depth, 297.6 rpm for the rotary blades, and 3.23 km/h for forward speed. Achieved were the following parameters: 94.766% soil breakage rate, 84.97% straw return rates, and 16.36 mm soil flatness. The findings demonstrate the potential to implement strategies through operational parameters to significantly enhance agricultural practices.

The advancement of agricultural mechanization necessitates precise and standardized classification based on technical characteristics to enhance green, efficient, and high-quality development. The current lack of scientific and standardized definitions and classifications for various types of agricultural machinery has become a bottleneck, complicating the machine selection and affecting the compatibility of the machinery with optimized field operations. To address this complexity, we propose a comprehensive classification method that integrates principal component analysis (PCA), cluster analysis, and the qualitative analysis of the functional components for defining and scientifically classifying harvesters. The key functional and technical properties of harvesters were analyzed, and eight primary parameters (machine weight, cutting width, feed rate, rated power, overall machine length, width, height, and working efficiency) were selected, supplemented by nine key functional components (walking mechanism, cutting device, threshing device, separating device, cleaning device, grain collecting device, grain unloading device, cabin, and track size). In the first step, principal component analysis was performed to reduce the dimensionality of the parameters, yielding three principal components with contribution rates of 41.610%, 28.579%, and 15.134%, respectively. One primary parameter from each component was selected for further analysis. In the second stage, Q-type cluster analysis classified the harvesters based on the squared Euclidean distance between the operational parameters, resulting in three classes of harvesters. Finally, functional component analysis provided detailed insights, further refining the classification into four major categories: mini, small, medium, and large harvesters. The results of this work provide a scientific basis for the definition and classification of the harvester products available on the market. This method offers a robust framework for the rational selection and planning of agricultural machinery, promoting sustainable mechanization with a focus on technical parameters and functional attributes.