The accurate recognition of maize growth stages is crucial for effective farmland management strategies. In order to overcome the difficulty of quickly obtaining precise information about maize growth stage in complex farmland scenarios, this study proposes a Maize Hybrid Vision Transformer (MaizeHT) that combines a convolutional algorithmic structure with self-attention for maize growth stage recognition. The MaizeHT model utilizes a ResNet34 convolutional neural network to extract image features to self-attention, which are then transformed into sequence vectors (tokens) using Patch Embedding. It simultaneously inserts category information and location information as a token. A Transformer architecture with multi-head self-attention is employed to extract token features and predict maize growth stage categories using a linear layer. In addition, the MaizeHT model is standardized and encapsulated, and a prototype platform for intelligent maize growth stage recognition is developed for deployment on a website. Finally, the performance validation test of MaizeHT was carried out. To be specific, MaizeHT has an accuracy of 97.71% when the input image resolution is 224 × 224 and 98.71% when the input image resolution is 512 × 512 on the self-built dataset, the number of parameters is 15.446 M, and the floating-point operations are 4.148 G. The proposed maize growth stage recognition method could provide computational support for maize farm intelligence.

High moisture content (MC) harms wheat storage quality and readily leads to mold growth. Accurate localization of abnormal/high-moisture regions enables early warning, ensuring proper storage and reducing economic losses. The present study introduces the 2D microwave scanning method and investigates a novel localization method for addressing such a challenge. Both static and scanning experiments were performed on a developed mobile and non-destructive microwave detection system to quantify the MC of wheat and then locate abnormal moisture regions. For quantifying the wheat’s MC, a dual-parameter wheat MC prediction model with the random forest (RF) algorithm was constructed, achieving a high accuracy (R² = 0.9846, MSE = 0.2768, MAE = 0.3986). MC scanning experiments were conducted by synchronized moving waveguides; the maximum absolute error of MC prediction was 0.565%, with a maximum relative error of 3.166%. Furthermore, both one- and two-dimensional localizing methods were proposed for localizing abnormal moisture regions. The one-dimensional method evaluated two approaches—attenuation value and absolute attenuation gradient—using computer simulation technology (CST) modeling and scanning experiments. The experimental results confirmed the superior performance of the absolute gradient method, with a center detection error of less than 12 mm in the anomalous wheat moisture region and a minimum width detection error of 1.4 mm. The study performed two-dimensional antenna scanning and effectively imaged the high-MC regions using phase delay analysis. The imaging results coincide with the actual locations of moisture anomaly regions. This study demonstrated a promising solution for accurately localizing the wheat’s abnormal/high-moisture regions with the use of an emerging microwave transmission method.