Artificial intelligence-based automatic systems can reduce time,human error and post-harvest operations.By using such systems,food items can be successfully classified and graded based on defects.For this context,a ma...Artificial intelligence-based automatic systems can reduce time,human error and post-harvest operations.By using such systems,food items can be successfully classified and graded based on defects.For this context,a machine vision system was developed for fruit grading based on defects.The prototype consisted of defective fruit detection and mechanical sorting systems.Image processing algorithms and deep learning frameworks were used for detection of defective fruit.Different image processing algorithms including preprocessing,thresholding,morphological and bitwise operations combined with a deep leaning algorithm,i.e.,convolutional neural network(CNN),were applied to fruit images for the detection of defective fruit.The data set used for training CNN model consisted of fruit images collected from a publiclyavailable data set and captured fruit images:1799 and 1017 for mangoes and tomatoes,respectively.Subsequent to defective fruit detection,the information obtained was communicated to microcontroller that further actuated the mechanical sorting system accordingly.In addition,the system was evaluated experimentally in terms of detection accuracy,sorting accuracy and computational time.For the image processing algorithms scheme,the detection accuracy for mango and tomato was 89% and 92%,respectively,and for CNN architecture used,the validation accuracy for mangoes and tomatoes was 95% and 94%,respectively.展开更多
文摘Artificial intelligence-based automatic systems can reduce time,human error and post-harvest operations.By using such systems,food items can be successfully classified and graded based on defects.For this context,a machine vision system was developed for fruit grading based on defects.The prototype consisted of defective fruit detection and mechanical sorting systems.Image processing algorithms and deep learning frameworks were used for detection of defective fruit.Different image processing algorithms including preprocessing,thresholding,morphological and bitwise operations combined with a deep leaning algorithm,i.e.,convolutional neural network(CNN),were applied to fruit images for the detection of defective fruit.The data set used for training CNN model consisted of fruit images collected from a publiclyavailable data set and captured fruit images:1799 and 1017 for mangoes and tomatoes,respectively.Subsequent to defective fruit detection,the information obtained was communicated to microcontroller that further actuated the mechanical sorting system accordingly.In addition,the system was evaluated experimentally in terms of detection accuracy,sorting accuracy and computational time.For the image processing algorithms scheme,the detection accuracy for mango and tomato was 89% and 92%,respectively,and for CNN architecture used,the validation accuracy for mangoes and tomatoes was 95% and 94%,respectively.
