CAJ | 학술논문

利用普通数码相机获取成熟期苹果树图像进行产量估测,具有成本低、操作简单等特点,其关键是估测模型的建立.该文分别按东南和西北2个方向获取富士苹果成熟期的40株果树的80幅图像,通过果实特征提取,获取东南方向识别出的图斑数量(参数1)、西北方向识别出的图斑数量(参数2)、东南方向识别出的图斑像素面积(参数3)、西北识别出的图斑像素面积(参数4),分别以识别出的4个参数及双方向图斑数量之和(参数5)、双方向图斑像素面积之和(参数6)共6个参数为自变量,以获取的单株产量信息为因变量,以奇数组20株果树为建模数据集建立线性回归模型.结果表明以参数5构建的产量估测模型的决定系数R2最高为0.81,相对均方根差(NRMSE)值最低为0.11,说明以该参数构建的模型其估测效果最好;进一步利用以参数5构建的估测模型对偶数组20株果树进行验证,其NRMSE值为0.16,估测结果较好,但也存在估测产量较大波动的情况.深入讨论引起估测偏差的情况,后期研究应重点提高逆光、弱光照条件下的成熟期苹果的识别率,及解决由于单果因遮挡被分离而被识别为多果的情况和多果因重叠被识别为单果的情况,以提高识别效果,进而提高产量模型估测效果.
이용보통수마상궤획취성숙기평과수도상진행산량고측,구유성본저、조작간단등특점,기관건시고측모형적건립.해문분별안동남화서북2개방향획취부사평과성숙기적40주과수적80폭도상,통과과실특정제취,획취동남방향식별출적도반수량(삼수1)、서북방향식별출적도반수량(삼수2)、동남방향식별출적도반상소면적(삼수3)、서북식별출적도반상소면적(삼수4),분별이식별출적4개삼수급쌍방향도반수량지화(삼수5)、쌍방향도반상소면적지화(삼수6)공6개삼수위자변량,이획취적단주산량신식위인변량,이기수조20주과수위건모수거집건립선성회귀모형.결과표명이삼수5구건적산량고측모형적결정계수R2최고위0.81,상대균방근차(NRMSE)치최저위0.11,설명이해삼수구건적모형기고측효과최호;진일보이용이삼수5구건적고측모형대우수조20주과수진행험증,기NRMSE치위0.16,고측결과교호,단야존재고측산량교대파동적정황.심입토론인기고측편차적정황,후기연구응중점제고역광、약광조조건하적성숙기평과적식별솔,급해결유우단과인차당피분리이피식별위다과적정황화다과인중첩피식별위단과적정황,이제고식별효과,진이제고산량모형고측효과.
Apples yield estimation with a common digital camera to get mature fruits, has the advantages of low lost, simple operation and other characteristics. Key to the estimation is the establishment of an estimation model. In this paper, 80 images from 40 Fuji trees were acquired from the southeast and northwest directions using a Cannon G7 camera. By fruit feature extraction, 4 parameters were identified, which were identification patch number from southeast direction (parameter 1), identification patch number from northwest direction (parameter 2), patch pixels area from southeast direction (parameter 3) and patch pixels area from northwest direction (parameter 4). A total of 6 parameters, including the above-mention 4 parameters along with the sum of patch number from two directions (parameter 5) and the sum of patch pixels area from two directions (parameter 6) acted as independent variables and single tree yield information acted as the dependent variable. With 20 fruit trees used as the modeling data set, the linear regression model was constructed based on the independent variables and dependent variable. The results showed that the yield estimation model with parameter 5 had the best effects with the highest R2 of 0.81 and the lowest NRMSE (Normal Root Mean Squared Error) value of 0.43. Further, additional 20 fruit trees were verified using the yield estimation model with parameter 5. The estimation result was good with a NRMSE value of 0.59, but there were also fluctuations between estimation yield and actual yield. In the verified 20 fruit trees, there were 10 trees whose estimated yield was higher than the actual yield, and the deviation value of No. 2 tree was maximum of 14.02. There were also 10 trees whose estimated yield was lower than the actual yield, and the deviation value of No. 30 was maximum of 17.79. The reason of estimation errors was discussed. Later studies should focus on improving mature apple recognition rates in conditions of backlighting and weak light, and solve the error recognition in conditions of single apple occlusion and multi apples overlapping. The research will help improve recognition effects and then improve model estimation effects.