Design and Experiment of a Broken Corn Kernel Detection Device Based on the Yolov4-Tiny Algorithm

At present, the wide application of the CNN (convolutional neural network) algorithm has greatly improved the intelligence level of agricultural machinery. Accurate and real-time detection for outdoor conditions is necessary for realizing intelligence and automation of corn harvesting. In view of the problems with existing detection methods for judging the integrity of corn kernels, such as low accuracy, poor reliability, and difficulty in adapting to the complicated and changeable harvesting environment, this paper investigates a broken corn kernel detection device for combine harvesters by using the yolov4-tiny model. Hardware construction is first designed to acquire continuous images and processing of corn kernels without overlap. Based on the images collected, the yolov4-tiny model is then utilized for training recognition of the intact and broken corn kernels samples. Next, a broken corn kernel detection algorithm is developed. Finally, the experiments are carried out to verify the effectiveness of the broken corn kernel detection device. The laboratory results show that the accuracy of the yolov4-tiny model is 93.5% for intact kernels and 93.0% for broken kernels, and the value of precision, recall, and F1 score are 92.8%, 93.5%, and 93.11%, respectively. The field experiment results show that the broken kernel rate obtained by the designed detection device are in good agreement with that obtained by the manually calculated statistic, with differentials at only 0.8%. This study provides a technical reference of a real-time method for detecting a broken corn kernel rate.

Model Study on the Combination of Operating Parameters of Corn Kernel Harvesters

This study analyzed the engine operating condition curve of the corn kernel harvester. Field experiments identified the feed rate, concave clearance, and cylinder speed as the main factors affecting operating quality and efficiency. A ternary quadratic regression orthogonal center-of-rotation combined optimization test method was used to determine the feed rate, cylinder speed, and concave clearance as the influencing factors, and the engine speed variation rate, crushing rate, impurity rate, loss rate, and cylinder speed variation rate as the objective functions. A mathematical regression model was developed for the combination of operating quality indicators, efficiency indicators, and operating parameters of the corn kernel harvester. A non-linear optimization method was used to optimize the parameters of each influencing factor. The results showed that with a feed rate of 12 kg/s, a forward speed of 5 km/h, a cylinder speed of 360 r/min, and a concave clearance of 30 mm, the average crushing rate was 3.91%, the average impurity rate was 1.71%, and the kernel loss rate was 3.1%. This model could be used for the design and development of intelligent control systems.

Osmosis dehydration of sweet corn (Zea mays saccharata.) kernel with trehalose solution

Osmosis dehydration is a process of reducing water content by immersing the material in a hypertonic solution. It usually uses a sugar solution likes mono-saccharide or disaccharide. Trehalose is one type of disaccharide that can be used as a solute. Trehalose is able to maintain the nutrition content of food material and the aroma of horticulture products because it maintains and stabilizes complex molecules. Immersing of sweet corn kernel in trehalose solution was expected to maintain kernel sweet corn quality in relation to the next process. The objective of the research is to investigate the effects of the different immersing temperatures and trehalose concentrations on the physical quality of sweet corn. This study used solution with concentrations of 4%, 8%, and 12% trehalose. The variations of immersing temperature were 30°C, 40°C, and 50°C. The experiment was done with a factorial completely randomized design. The first factor was immersing temperature and the second factor was the concentration of trehalose solution. The data was analyzed using Duncan Multiple Range Test (DMRT) method. The temperature treatment of 50°C and trehalose concentration of 12% showed the highest weight reduction (6.18%), solid gain (4.5%), and water loss (10.38%). The lowest water content of corn kernel was also obtained in this treatment i.e 78.7%.

Distance transform-watershed segmentation and multi-layer perceptron algorithm to separate touching orange fruit in digital images

An algorithm to separate touching oranges using a distance transform-watershed segmentation is presented. In this study, there are four classes of oranges, such as class A, B, C, and D, respectively. The size of each class is based on the Indonesian National Standard (SNI), the sample used is 168 oranges of which 140 are for training and 28 oranges are for testing. The image of citrus fruits was captured using Kinect v2 camera with a camera resolution of 1920 × 1080 pixels, the distance from the camera to the background is 23 cm. The images were captured in PNG format. The watersheds were computed based on the distance transformed by orange regions. The corresponding basins were finally used to split the falsely connected corn kernel by intersecting the basins with the corn kernel regions. Experimental results show that the multi-layer perceptrons have classification accuracy rates of 92.85%. The algorithm appears to be robust enough to separate most of the multiple touching scenarios.

Discrete Element Simulation Based on Elastic–Plastic Damping Model of Corn Kernel–Cob Bonding Force for Rotation Speed Optimization of Threshing Component

Current corn kernel-cob bonding mechanics models (LSD models) uniformly consider the bonding force changes during the maize threshing operation as an elastic change, resulting in computational errors of up to 10% or more in discrete element simulations. Due to the inability to perform high-precision discrete element simulation of the mechanics characteristics during the corn threshing operation, the core operating parameters of the corn thresher (rotation speed of the threshing component) rely mainly on empirical settings, resulting in a consistent difficulty in exceeding 85% of the corn ear threshing rate. In this paper, by testing the mechanics characteristics of corn kernels, the bonding force is found to have both elastic and plastic changes during the threshing process. An elastic–plastic (EP) damping model of the corn kernel–cob bonding force was established by introducing a bonding restitution coefficient e to achieve an integrated consideration of the two changes. By testing the relationship between the properties of the corn ear itself and the model parameters, the pattern of the effect of the corn ear moisture content and the loading direction of the ear by force on the EP model parameters was found. By establishing a model of the relationship between the corn cob’s own properties and the model parameters, the EP model parameter values can be determined by simply determining the moisture content of the ear. In this paper, the EP model was established and the high-precision simulation and analysis of the process of bonding force variation between corn kernel and cob is realized on the self-developed AgriDEM software. At the meantime, the optimal values of the threshing component rotation speed under different conditions of moisture content of corn ear were obtained by establishing an optimization model of threshing component rotation speed. The test results showed that the corn ear threshing rate could reach more than 92.40% after adopting the optimized speed value of the threshing component in this paper. Meanwhile, the test results showed that the discrete element simulation results based on the EP model did not significantly differ from the measured results of the thresher. Compared with the most widely used LSD model, the EP model can reduce the computational error by 3.35% to 6.05%.