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The typical characteristics of agricultural tires include large load fluctuations, special pattern shapes, harsh working environments, and significant tire body vibration. These features make it difficult to accurately obtain the vertical load of the tire in practical operations. However, vertical load has a significant impact on the performance of agricultural machinery and is a key factor in evaluating and optimizing the efficiency and stability of agricultural machinery operations. A state estimation method for agricultural tires based on sidewall bending strain is proposed to address the difficulties in obtaining vertical loads and the low estimation accuracy of traditional models. A tire state estimation system that integrated high-precision sidewall bending strain sensors, tire temperature and pressure sensors was designed based on the bending strain law of the tire sidewall under vertical load. A bending strain information collection experimental platform was established and various typical working condition testing experiments were conducted through the platform. Strain signals of tire sidewall under different tire pressures, speeds, and loads during the rolling process of non-road tires were obtained. A dataset was established for the bending strain, tire temperature, and tire pressure of the wheel rim sidewall during its rolling process. After denoising, screening, and feature extraction, the periodic strain curve and periodic features were extracted from the strain signal. Furthermore, a multi feature weighted vertical load prediction network （MVL-Net） and speed prediction network based on deep neural network （SDNN） were constructed to accurately and realtime estimate the vertical load and speed of the tire. A dataset of strain signals and tire temperature and pressure was established, and a multi-feature weighted vertical load prediction network （MVLNet） and speed prediction network based on deep neural network （SDNN） were constructed. The prediction results showed that the mean relative error （MRE） of the MVL-Net was 1.26%, and the root mean square error （RMSE） was 18.42 kg, which was 27.17% and 26.32% lower than that of the BP network, respectively. The MRE of the SDNN was 1.16%, and the RMSE was 0.10 km/h, which was 24.18% and 16.67% lower than that of the BP network, respectively. Ten-fold cross validation experiments were conducted, and the results showed that the MVL-Net and SDNN had good generalization ability. Research result showed that the proposed state estimation method of agricultural intelligent tire based on sidewall bending strain can achieve accurate prediction of state information such as vertical load and rotational speed of agricultural tires.