Study Of Crop And Weed Recognition Based On Vis/NIR Spectroscopy And Multi-spectral Digital Image Processing

Agriculture has developed greatly in our country, but it is facing advent problems now: such as theenvironmental pollution caused by over dosage of herbicide and pesticide, and the low level ofmechanization and efficiency. Precision farming is the inevitable trend of China agriculture. It is anintegration of electronics, computer techniques, information techniques and intelligent mechanics.Machine vision is the principle technique, which makes the agriculture machines can see things andobtain intelligence. To realize the need of early management of crop seedling, weed control applyingmachine vision or other method is one important task. Based on the different method and principle,these kinds of researches may be categorized into five classed: 1) using the location information torecognize weed and crop; 2) using the spectroscopy of weed and crop; 3) using the shape information; 4)using the color information; 5) using the texture information. The main content and result of thisresearch is as following:(1) Using Vis/NIR spectroscopy techniques differentiated the soybean seedling, Goose grass,Alligator Alternanthera and Emarginate amaranth, which are often living together in southernChina. By using a hand held type of spectrometer: FieldSpec Pro FR to record the reflectanceof the leaves in 325-1075 nm band. Then using DB12 wavelet analysis to reduce one samplefrom 751 numbers to 114 numbers. All these 360 samples were drawn in two terms. Selecting250 from them to build a radial basis neural network, and using the left 110 samples tovalidating the model. The result showed that only 3 Goose grass samples were classifiedincorrectly. So, using spectroscopy to recognize crop and weed is a high speed and efficientmethod.(2) Making research on the translation of multi color space. The three channels of multi-spectralimager are Gn, Ir and Rd, which were in the 400-1100nm band. So, translate the multi-spectralimages into these color spaces such as HSV, OHTA,CIE XYZ, CIEL~*A~*B~* and CIEL~*U~*V~*can obtain some advantages, including the enhanced difference or intensity, or the histogramquality. This demonstrated that the color space translation is a good method to process themulti-spectral images.(3) As regards the enhancement of image quality, this work was divided into two parts. Firstlyusing space domain enhancement methods, such as average, winner filter, median value filterand contrast enhancement filter. In frequency domain enhancement, this research used IIR andFIR digital high pass, low pass filters based on MATLAB to process the multi-spectral images,and compared the result with the traditional frequency space enhancement. The result showedthat these kinds of methods own many merits. The frequency and phase response could beobtained as wanted. And the processing quality is higher than the commonly used way.Therefore, digital filters could be used as a highly efficient preprocessing method.(4) Based on the combination of threshold segment, morphological operations and image analysis,guided by experimental knowledge, this paper raised new method to identify soybean seedlingsand Goose grass and Alligator Alternanthera. Using threshold of the histogram to divide theplant object and soil background. Then, using continuous dilation and erosion morphologicaloperations to extract the small size weeds(Goose grass and Alligator Alternanthera) from thelarge sized soybean seedlings. Then, using the image analysis tool to compute the long axis,short axis, the centroid, the eccentricity and other characters of the objects in the imagecontained only two weeds. Guided by two experimental rules, these objects could be divided into two classes. The result showed that 90% of the weed blocks could be identified correctly.So, this method is simple and efficient to identify soybean and other two weeds.(5) Making research on the edge extraction and object segmentation problem. Comparison of resultbetween different operators, such as Roberts, Prewitt, Sobel and Laplacian was made. Also, thesegmentation result of watershed model and neighbor area were compared.(6) Making statistics on multi-spectral images and images taken by common digital camera. Theresult showed that the pixel in infrared channel image is largely different from that of other twochannels. Its average value is medium and with larger RMSE, which means that its contrast andclarity are better than images from other channels. So, this new devices owns advantage toidentify crop and weed.