Abstract
Destemming fresh chilli fruit (Capsicum) in large productivity is necessary, especially in the Mekong Delta region. Several studies have been done to solve this problem with high applicability, but a certain percentage of the output consisted of cracked fruits, thus reducing the quality of the system. The manual sorting results in high costs and low quality, so it is necessary that automatic grading is performed after destemming. This research focused on developing a method to identify and classify cracked chilli fruits caused by the destemming process. The convolution neural network (CNN) model was built and trained to identify cracks; then, appropriate control signals were sent to the actuator for classification. Image processing operations are supported by the OpenCV library, while the TensorFlow data structure is used as a database and the Keras application programming interface supports the construction and training of neural network models. Experiments were carried out in both the static and working conditions, which, respectively, achieved an accurate identification rate of 97 and 95.3%. In addition, a success rate of 93% was found even when the chilli body is wrinkled due to drying after storage time at 120 hours. Practical results demonstrate that the reliability of the model was useful and acceptable.
Highlights
Chilli (Capsicum) is an important crop and is considered an almost indispensable spice in daily life [1]
To carry out the crack identification process, a linear forward propagation convolution neural network model was chosen for application because its simplicity will help speed up processing in real-time applications
The final was a matrix with 1 column and 4 × 4 × 256 = 1024 rows. This was the input of the neural network
Summary
Chilli (Capsicum) is an important crop and is considered an almost indispensable spice in daily life [1]. The trace elements, minerals, and nutrients in their fruits have good health properties [2,3,4,5]. This crop is grown year-round, seasonally independent [6]; in the Mekong Delta (MD), high-yield species often grow, such as ChanhPhong F1, ChanhPhong F4, DongtienVang, and MuiTen 207. Chilli products on the market are very diverse [7], and their value in Vietnam’s agricultural value chain is enhanced through processing processes [8, 9]. The stem removing systems have been studied with the goal of increasing productivity and automation, reducing the dependence on manual labor [12,13,14,15,16,17,18]
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