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To achieve accurate prediction of rotary tillage quality based on tractor multi-sensor load data, a tractor rotary tillage quality identification model based on GAF-DenseNet was proposed, rotary tillage quality grading standard was designed, and field tests of rotary tillage were carried out, and model accuracy verification and performance analysis were conducted. The Gramian angular field (GAF) algorithm uniquely encoded the time series data while preserving the time dependence of the original load sequence. The DenseNet network deeply mined the load information embedded in the image array, and significantly improved the computing efficiency of this network while ensuring the depth of feature extraction through feature reuse, model compression, and other technical aspects. The analysis results showed that the model performance was reduced by either too large or too small a resampling sliding window size and the experimental effect of Gramian angular difference field (GADF) was stronger than Gramian angular summation field (GASF), and the experimental data showed that the model performance was optimal when the resampling sliding window size was 250 and the GADF algorithm was selected. The growth rate k tended to be positively correlated with the overall performance of the model, but too large a value of k reduced the real-time performance of the model and had limited improvement in accuracy, and the growth rate k was set to 24 in the experimental scenario to better meet the actual demand. The GAF-DenseNet model achieved accuracy and F1 value of 96.816% and 96.136%, respectively. It had good performance in real-time capability, and the interfence time can be as low as 16s. The overall performance of this model was better than the control group analysis results in the comparison tests with other intelligent algorithms.