Abstract: Using real-time video to capture farmland digitization regulation is an important step in the protection of essential farmland, but problems exist such as a large area of bright sky background and extreme weather i.e. image degradation caused by rain or fog. Generally, we run the defogging process which is used for image processing to clarify the hazy image in the service. However, the cost is high and the process is in unreal time so that it is not suitable for the storage of video data and real-time alarm. With the innovation of computer hardware, it is possible now to defog in real-time under the haze weather. The Langley research center of the national aeronautics and space administration transplanted the algorithm which is based on Retinex algorithm to DSP(Digital signal process) enhancement system that meets the real-time requirements to deal with 256×256 gray-scale image. ClaireVue’s team from Tsinghua University developed a real-time system on the iPhone 4 to defog 192×144 video image. Cai Zixing’s team from Central South University put forwarded an algorithm based on the mist theory to achieve theoretical efficiency of real-time processing. In this paper, we aim at defogging the basic farmland video surveillance images in real-time. We achieved the MDCP(modified dark channel prior) algorithm which was improved on the basis of dark channel prior defogging algorithm with the combination of the dividing and merging of human visual perception to hazing. We built up a system which can clarify the basic farmland video surveillance image by using the subsample of transmittance, adjacent pixels completion, application processing block and the front-end hardware layered method to defog. In order to objectively demonstrate the effectiveness of MDCP algorithm, we used no reference evaluation model to evaluate MDCP algorithm, dark channel prior algorithm and multi-scale Rentinex algorithm and we gained the objective assessment of clarifying the hazy image. The data showed that MDCP algorithm was more prominent in intensity, tone reproduction and information of structural aspects than the other two algorithms in clarification. MDCP algorithm had the highest comprehensive evaluation indicators following by DCP(dark channel prior), and Retinex was the lowest among the three. The defogging system which was used for basic farmland video surveillance included video input device(camera), real-time image processing device(DSP hardware system) and monitor. We adopted SONY SSC-G103 CCD(Charge-couple Device) camera as video input device. DSP hardware system which is made up by the TMS320DM642 platform helped to complete the acquisition of video codec, to format conversion, and to clarify processing. Our test which was based on video image processing and algorithm complexity showed that the system improved the strength of hazy image, tone reproduction and structural information indicator and it kept the good real-time and fluency. In summary, the basic farmland video monitoring front-end of defogging system had some advantages of low cost, low power consumption and processing in real-time while comparing to traditional system which enhanced image in services. It achieved the goal that defogging the video surveillance image in front-end and real-time.