Discriminant model of banana fruit maturity based on genetic algorithm and SVM

Abstract:【Objective】Harvest maturity is the key factor affecting storage performance of banana fruits. The existing discriminant methods of banana fruit maturity, such as hardness sensor, odor sensor and spectral technology, cannot be applied to the picking site of banana fruits, and the discriminant method based on the pigment composition of banana fruit skin is only suitable for commercial trade. At present, in the process of banana fruit harvesting, experienced farmers still need to observe the fullness of fruit body by experience and judge whether banana fruits should be harvested according to the length of storage time. This method not only is inefficient but also may lead to wrong judgment. The purpose of this study is to explore the relationship between the appearance characteristics and maturity of ba-nana fruits, so as to build a banana fruit maturity discrimination model depending upon the edge and corner characteristics of banana fruits as input vector, and propose a banana fruit maturity discrimina-tion method based on genetic algorithm and SVM.【Methods】In this experiment, 11 banana plants were selected, and the maturity of their banana fruits were set to level 5 maturity. The test samples were selected from the third row on a banana hand from top to bottom, and two hands of bananas were ran-domly cut from each plant. Each group of samples was collected at an interval of two days, and the col-lected samples of bananas were labeled, and the judgment of banana farmer on the maturity of the col-lected samples was asked and recorded. Samples were collected from level 5 maturity to level 8 maturi ty of banana fruits. During the experiment, a total of 176 banana fruits were collected from 8 sets of da-ta. In this paper, the maturity of banana fruit samples was divided into four levels (level 5, level 6, level7 and level 8). In this method, 176 banana fruit samples of the same species with different maturity were cut evenly according to the length of fruit body by three cuts. The transverse sections of the top,middle and bottom parts of the banana fruits were obtained, and the obtained transverse sections of the banana fruits were covered with the white paper. After drawing the transverse section contour line of ba-nana fruits on paper, the edges and corners in the transverse section contour line of banana fruits were measured by manual measurement. In order to reduce the error due to manual measurement, the average of the angles measured by five people was used as the final result. The angles of each corner on the transverse section contour line of banana fruits were measured as the characteristic of maturity discrimi-nation. The banana fruits collected in the experiment generally had only three or four edges and corners when their maturity levels were less than the level 8 maturity. In the experiment, the banana fruits with level 8 maturity had five edges and corners. Each transverse section had 5 eigenvalues, and the vacancy angle of less than five edges and corners was represented by 0. Firstly, the data of banana fruits were preprocessed, that is, normalized to improve the convergence speed and accuracy of the model, and then the parameters of classical SVM model were optimized by genetic algorithm. In the process of ge-netic algorithm optimization, the population size was set to 50, the range of penalty parameter C was [0 100], the range of kernel parameter g was [0 100], and the termination algebra was 200. The 50% cross validation was carried out in the training set, and the mutation probability was 0.9.【Results】In the evo-lutionary process of genetic algorithm, the optimal fitness of each generation in the training set in-creased with the increase of evolutionary algebra, and reached a stable value after the 10th generations.At this time, the optimal fitness was 76.77%. With the increase of evolutionary algebra, the average fit-ness of each generation increased and the oscillation amplitude decreased. After the 20th generation, the average fitness reached a relatively stable value, that is, it fluctuated slightly around 76%. After optimiz-ing the parameters by genetic algorithm, the optimal value of penalty parameter C was 14.84 and the op-timal value of kernel parameter g was 73.93. The experimental results showed that among the 35 sam-ples with level 5 maturity, six were incorrectly predicted as level 6 maturity. The discrimination accura-cy was 80.00%. Among the 32 samples with level 6 maturity, four were incorrectly predicted as level 5 maturity. The discrimination accuracy was 87.50%. Among the 33 samples with level 7 maturity, one was incorrectly predicted as level 5 maturity, two were incorrectly predicted as level 6 maturity, and one was incorrectly predicted as level 8 maturity. The discrimination accuracy was 87.88%. Among the 32 samples with level 8 maturity, one was incorrectly predicted as level 6 maturity and one was incorrectly predicted as level 8 maturity. The discrimination accuracy was 93.75%. A total of 17 samples were pre-dicted incorrectly in this prediction, and the accuracy rate was 87.12%. The whole model training and prediction took about 147.29 s. The proposed model made 10 predictions for 132 samples in the test set,and the accuracy of the average was 86.20%. The experimental results showed that the accuracy of ba-nana fruit maturity discrimination model based on SVM and genetic algorithm was more than 86%. The results also showed that this method can distinguish banana fruits with different maturity. In the subse-quent research, it is necessary to further study the nondestructive acquisition method dealing with ba-nana fruit shape and angle information, and further explore the nondestructive judgment technology of banana fruit maturity.【Conclusion】The banana fruit maturity discrimination model established in this study is a banana fruit maturity discrimination method based on the angular information of banana fruit shape. Compared with the common methods to judge the maturity of banana fruits based on the color, hardness and smell of banana fruits, the proposed method starts with the plumpness of banana fruits and uses genetic algorithm to optimize the parameters of SVM model. It not only avoids the blind selection of parameters, but also is simpler than other optimization algorithms.