农业工程学报
農業工程學報
농업공정학보
2014年
23期
207-214
,共8页
李文勇%陈梅香%许树坡%陈信友%钱建平%杜尚丰%李莎%李明
李文勇%陳梅香%許樹坡%陳信友%錢建平%杜尚豐%李莎%李明
리문용%진매향%허수파%진신우%전건평%두상봉%리사%리명
果实%图像处理%形态学%分水岭%凸包理论%直径测量
果實%圖像處理%形態學%分水嶺%凸包理論%直徑測量
과실%도상처리%형태학%분수령%철포이론%직경측량
fruits%image processing%morphology%watershed%convex hull%diameter measurement
针对自然场景下生长期内树上未成熟果实的自动探测与大小计算问题,提出了一种基于改进分水岭和凸包理论的自然场景下未成熟苹果识别与直径计算方法。该方法首先对灰度图像进行形态学重构后进行边缘检测,再利用合并局部极小值点分水岭分割方法从粘连区域中提取目标果实,并结合基于凸包理论的真轮廓提取和圆拟合方法,实现目标果实圆拟合直径的自动测量。计算结果与人工测量结果进行对比试验,结果表明:在不考虑扁平型目标果的情况下,该方法的直径计算均方根误差最小值为1.91 mm,均值为2.27 mm,误差范围在品质评定等级差(5 mm)以内,具有较好的推广应用价值。研究结果为生长期内果实的大小监测提供参考。
針對自然場景下生長期內樹上未成熟果實的自動探測與大小計算問題,提齣瞭一種基于改進分水嶺和凸包理論的自然場景下未成熟蘋果識彆與直徑計算方法。該方法首先對灰度圖像進行形態學重構後進行邊緣檢測,再利用閤併跼部極小值點分水嶺分割方法從粘連區域中提取目標果實,併結閤基于凸包理論的真輪廓提取和圓擬閤方法,實現目標果實圓擬閤直徑的自動測量。計算結果與人工測量結果進行對比試驗,結果錶明:在不攷慮扁平型目標果的情況下,該方法的直徑計算均方根誤差最小值為1.91 mm,均值為2.27 mm,誤差範圍在品質評定等級差(5 mm)以內,具有較好的推廣應用價值。研究結果為生長期內果實的大小鑑測提供參攷。
침대자연장경하생장기내수상미성숙과실적자동탐측여대소계산문제,제출료일충기우개진분수령화철포이론적자연장경하미성숙평과식별여직경계산방법。해방법수선대회도도상진행형태학중구후진행변연검측,재이용합병국부겁소치점분수령분할방법종점련구역중제취목표과실,병결합기우철포이론적진륜곽제취화원의합방법,실현목표과실원의합직경적자동측량。계산결과여인공측량결과진행대비시험,결과표명:재불고필편평형목표과적정황하,해방법적직경계산균방근오차최소치위1.91 mm,균치위2.27 mm,오차범위재품질평정등급차(5 mm)이내,구유교호적추엄응용개치。연구결과위생장기내과실적대소감측제공삼고。
In agriculture field, the knowledge about the size of immature fruit in its growing period could be helpful to the aspects such as precision fertilization and irrigation, training and pruning, yield estimation and harvest stage determination. Thus, it can improve the fruit yield and quality. However, the most important premise is the fruit detection and the size calculation in natural condition. The color of fruits on tree is very similar with their surrounding objects, so that the recognition and size calculation is very difficult in natural condition. This paper proposed an algorithm of diameter measurement for immature apple based on the morphological reconstruction, the watershed and convex hull theory. Firstly, the images were acquired using the JV205 cameras which were installed in front of the apple trees with the distance of about 1-2 m, whose resolution was 4 608 pixels × 3456 pixels; and acquisition time was from June 1th, 2014 to September 30th, 2014. Before preprocessing, the images were cropped into 1400 pixels × 1100 pixels to get the scope of the target apples. In order to enhance the visibility of the target area, the morphological reconstruction operation was used before the edge detection. Secondly, the rough contour of target fruit was extracted by the dilation and erosion operation using micro-disk structure element with a radius of two pixels. After the above image preprocessing, excessive local minimum points, which were caused by occluded leaves and uneven illumination, were merged using the bresenham algorithm. And then the overlapping target fruits were segmented by distance transform and improved watershed algorithm. Later, the contours of segmented target region were tracked and the continuous smooth contour curve was extracted using convex hull on the edge of the segmentation images. Finally, the entire circle was reconstructed using the three different points on the reserved true target contour based on the circle feature principle. Thus, the center and radius parameters were estimated and the precise detection of target fruit was realized. In order to verify the algorithm accuracy, a total of 4 typical target apples and 96 images were selected in the experiments. The reference object, table tennis ball was 200 pixels and its actual size was 40 mm. So the calibration coefficient was 0.2 mm/pixel. The result of preprocessing showed that the image morphological reconstruction could not only weaken the brightness of the background, but also maintain the edge of target fruit. After the edge detection and structure expansion with a radius of two pixels, the contour of the fruit had been acquired, but there were some defects due to the fact that some fruits were shaded by leaves. At last, the calculated results were compared with the manual measurement ones. The experiment results showed that the minimum root mean square error (RMSE) and the mean value were 1.91 mm and 2.27 mm, respectively. In addition, it was found that the calculation result of the flat apples had larger error and the reason was that the radius of fitting circle was less than the actual one. The size class of fruit in market is mostly expressed in millimetre and the change from class size to another is often 5 mm or 0.5 cm. So without considering the flat apples, this method can meet the market fruit’s grading requirement. In conclusion, the paper proposed a new approach to deal with segmentation for overlapping and occluded apples and to realize size detection and precise measurement of immature fruits in natural condition. And the results were consistent with the manual measurement, which could meet the producers’ demand to precision management during the fruit growth period and had a good practicality.