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The binocular camera-based target localization system has the advantages of short starting distance, high accuracy, and low cost, which is suitable for strawberry target identification and localization applications in space-constrained greenhouse strawberry production environments. Accurate target matching is the guarantee of the effectiveness of binocular camera measurements, but the surface brightness and shadow areas of strawberries vary greatly in natural environments, and it is difficult to obtain stable and accurate matching results with the binocular matching method based on local features. A binocular strawberry target matching method was investigated based on image semantic features, which can maintain the stability of the target description under the conditions of large illumination changes, rich image texture, fruit occlusion, image blurring, etc., and therefore can improve the accuracy of binocular strawberry target matching. The semantic feature extraction method of the strawberry target region in the image was firstly designed, and secondly the strawberry target similarity calculation method was designed based on the semantic features and the geometric constraints of the binocular structure, and finally the binocular strawberry target matching in the greenhouse environment was realized. The experimental results showed that the correct rate of strawberry target matching applied to the greenhouse environment by the method was 96.3%, which can provide good target matching results for the strawberry target binocular localization system under the actual picking environment.