Study On The Key Techniques Of On-site Crop Growth Environmental Information Acquisition Based On WSN

To precisely, real-time, stably and portably obtain all kinds of environmental factor information on crop growth, such as temperature, illumination, relative humidity, soil composition and content, CO2density, wind speed and graphic information of big cropland, plays increasingly important role in crow growth control and even sustainable development of modern agriculture.WSN, Wireless Sensor Networks, is an effective method to realize on-site sensing for environment information on crop growth. But many technical problems arise from recent application. Outstanding contradictions come into being between limited non-renewable WSN energy supply system and long-time continuous on-site monitoring requirement. Data volume from on-site sensing has been rapidly increased with richness and development of sensing subject types, which severely challenges stable operation of on-site sensing business, based on light-level sensors. With lack of wireless channel reference model suitable for big cropland environment, accurate analysis and further optimization based on WSN on-site sensing business will be held up. And it is still in early stage for study and development of easy-to-use and low-cost sensor integration equipment, which is integrated with many sensing methods and urgently needed by basic-level agricultural units. This thesis uses oilseed rape and its growth big cropland as study sample, to further study theoretically on extending life cycle of on-site sensing business, improving data processing ability for on-site sensing, building channel model under big field of crop growth, to explore applicative technology of prototype design and development for low-cost and wireless sensing integration equipment, which provides with low-cost, low-power-consumption and high-efficiency key technical support for WSN-based on-site sensing methods of crop growth environmental information, and constitutes a solid basis to build scientific crop growth control mechanism.How to effectively extend life cycle of WSN-based on-site sensing methods of crop growth environmental information, is core problem to keep on-site sensing business on-going and long-lasting.Collecting activity of environmental factor information on crop growth, is usually a long-term, continuous monitoring process with huge demand for energy consumption. But sensor nods planted in the fields is always sealed, small-sized, energy limited and non-renewable as common features. Moreover, intensive crop stem leaf will bring serious signal path loss and frequency selective fading, speed up nod power to run out earlier, and shorten life cycle of on-site sensing business. To blindly increase nod density, improve antenna gain, or use external energy facilities of solar energy etc, will increase deployment cost, speed up network energy consumption and affect normal field work.To effectively extend WSN life cycle is equal to extend life cycle of on-site sensing business. With exsiting problems, based on new virtual MIMO technology and combined with topology control new method on MAC sub-layer, this thesis brings up a WSN maximum life cycle routing policy, to obtain optimal parameters of digital modulator (b=4), cluster scale (m=9) and virtual antenna array scale (nt=3), which affect WSN energy efficiency.By comparative experiments, MLRV has obvious advantage in three indexes of nod survival, effective package delivery quantity and WSN life cycle, which proves that MLRV extends on-site sensing life cycle of crop growth environmental information effectively.Proper data fusion methods are used to compress increasing data scale, which will keep it running stable for WSN-based on-site sensing business.As sensing data of crop growth environmental information has been increasing, light-level wireless sensor nods cannot dynamically and real-time process these data whether in energy resource or calculation resource. This contradiction will result in nod shutdown and offline and then sensing process will be interrupted. In another way, huge redundant data will waste precious energy and shorten life cycle of sensing process.So this thesis brings up wavelet transform data fusion policy, which dispatches nod status with Markov chain probability model. Using Markov chain model of nod status changing, DCWM builds nod status management system, to keep lower routing interruption probability. And it brings up single layer decomposition and compress sampling theory to lower compression encoding expense and improve compression efficiency, to solve light-level sensor calculating storage and energy expense problems.By simulation test of public data sets and big field comparative experiments, the results is that, with temperture, illumination, relative humidity data and digital graphic as sensing sample, DCWM is better than comparison sample in compression accuracy (2～4.1%), compressibility (when measurement array scale m=400, it is about20%), compression ratio (It is3.71while using haar wavelet. It is4.85while using wbarb.mat), handling capacity and system total energy consumtion index. And it provides with core key technology support for stable operation of on-site sensing business. Modeling wireless channel transmission loss characteristics under crop growth big field environment, is further optimization of on-site sensing method of WSN-based crop growth environmental information, and precondition for further design.Taken oilseed rape and its growth big field as example, according to different growth season, three typical test scenes are chosen. Signal loss value has been tested by wireless sensor nod using two kinds of carrier frequency. Test result has been put in a fit modelling with least square method. It shows that there is a big difference bwtween WSN channel transmission loss characteristics under testing big field environment and traditional mobile channel model, so it is suitable to characterize it with empirical model of multiple slope logarithmic shadow fading. Taking effective transmission distance of nods (dma) and predictable value of path loss as index, comparing to traditional models of Free-space, Cost321, Egli and so on, verifying effectively with fit model, we have found that prediction accuracy of fit model has obvious advantage.With the prediction of dmax within characterization range suitable for single-slope and double-slope fit model, absolute value of predicted deviations of single-slope fit model is less than4.8m, and that of double-slope model is not more than4.5m. With the prediction of path loss value, mean error of single-slope fit model is within2.1dB and that of double-slope is less than2dB. And predicted deviations of780M nod is even less, mean error is1.7dB, better than1.9dB of2.4G.This is a good basic for the testing model to optimize and design on-site sensing business of higher energy efficiceny.With the problems of high cost and bad openness for micro auto weather station and commercial ZigBee products, a low-cost WSN-based data collection system of oilseed rape growth environment has been designed and realized. This thesis trial-produces sensor hardware prototype product and writes supporting upper computer state software. By simulation experiment and big field test, the result is that temperature collecting accuracy comes up to±0.5℃at most, humidity testing accuracy is±3%RH, light intensity collecting range is1～65535lx, concurrent data transfer comes up to36channels, which can satisfy higher measurement requirements of agricultural on-site environmental data. With nominal power supply, actual effective systematic life cycle exceeds227days. Low cost will help large-scale deployment and application, thanks to AT89C51and nRF2401as basic hardware platform.