In crop mapping, deep learning--by virtue of its powerful feature-extraction and pattern-recognition capabilities--has markedly improved both the accuracy and efficiency of crop classification when processing large-scale, high-frequency remote-sensing data [13; 14]. For example, [PERSON] et al. [15] developed a deep-learning framework designed to enhance the accuracy of crop-type recognition from remote-sensing imagery. In the realm of single-crop identification, significant advances have also been reported. [PERSON] et al. [16] employed an improved DenseNet model to detect cotton-crop fields, delineate their spatial distribution within the study region, and monitor area changes; [PERSON] et al. [17] proposed a DCM architecture incorporating an attention-mechanism LSTM network to generate high-resolution soybean-crop maps. Despite the excellent performance of single-crop models under controlled conditions, real-world agricultural monitoring must contend with multiple coexisting crops, overlapping phenological stages, and complex planting structures. Consequently, a growing body of research has shifted toward multi-crop recognition. [PERSON] et al. [18] extracted temporal and spectral features to automatically learn discriminative signatures of maize, soybean, and other crops; [PERSON] et al. [19] combined Sentinel-1 radar data with multi-spectral time-series imagery to characterize cropping patterns and accurately classify paddy rice, wheat, maize, and other species. Although these studies employ advanced deep-learning techniques, most neglect the stage-specific phenological variability intrinsic to different crops. Crops exhibit pronounced spectral and temporal dynamics throughout their growth cycles, and these phase-dependent signatures are critical for accurate classification. Traditional approaches typically treat crops as static classes, thereby overlooking phenological transitions--especially during overlapping or rapid developmental stages--which can degrade classification performance [20]. For instance, [PERSON] et al. [21] presented an effective model for multi-crop classification using high-resolution temporal sequences, yet they noted that integrating meteorological data and phenological indicators with high-spatial-resolution Sentinel-1 and Sentinel-2 imagery could further enhance classification precision and reliability. To address this gap, recent work has begun to incorporate phenology explicitly. [PERSON] et al. [20] demonstrated that mapping rice cultivation effectively requires selecting temporal windows corresponding to key phenological events, thereby supporting rice-planting monitoring and sustainable regional agro-ecosystem management; [PERSON] et al. [22] introduced PSeqNet, a flexible multi-source integration network that explicitly models phenological correlations, achieving outstanding phenology-detection performance. Nevertheless, studies focusing on non-food crops remain relatively scarce, and existing multi-crop methods that incorporate phenology often rely on fixed time windows or feature averaging [23]. Non-food crops, such as rapeseed, cotton and tea, cover less than 20% of the arable land in our study region yet contribute more than 45% of its farm-gate income (Ministry of Agriculture, 2023) [24]. Their fields are typically smaller, more fragmented and phenologically overlapped with natural vegetation, making them markedly harder to discriminate in remote-sensing imagery than well-studied staple crops. Mapping these high-value but under-represented classes is therefore critical for precision-agriculture management and rural economic planning, and is the specific focus of this work [25]. In light of the above challenges, this study proposes a novel Phenology-Aware Vision Transformer Model (PVM), a phenology-aware semantic segmentation model for non-food crop classification. The model is built upon a Vision Transformer (VIT) and incorporates a Phenology-Aware Module (PAM) that leverages crop growth calendars to guide temporal attention toward phenologically significant stages.