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The demand for smart automation systems in postharvest technology, particularly in the postharvest process of garlic production, is high. In this study, we introduce an artificial intelligence (AI)-integrated robot system for grading and sorting of garlics after root trimming. We developed a robot system that automatically grades and sorts root-trimmed garlics based on image analysis using a deep learning model, which is equipped with a deep convolutional neural network (CNN). The robot system consists of three main modules: image acquisition module (IAM), image processing module (IPM), and garlic sorting module (GSM). The IAM captures a live video of garlic roots using an independent light-emitting diode (LED) and a web camera connected to a computer. The IPM consists of garlic image processing software (GIPS) installed on a Windows 10 computer. GIPS imports videos, detects the shape of garlic bulbs, and predicts the garlic class using the deep learning model. When a garlic bulb shape is detected, the image is preprocessed, and the deep learning model is automatically activated to predict the garlic class. The GSM physically separates garlics into three containers for good, bad, and scratched classes based on a previous predicted result. A total of 800 root-trimmed garlics were tested and evaluated in nine experiments. The processing time to predict classes from a garlic image on a regular laptop computer (Intel® core™ i7-8650U 1.9 GHz CPU, with 16 Gb RAM) was 0.19–0.58 s. The median time to trim and sort one garlic was 11 s (in a setting with three prediction replications), and the overall grading accuracy for the three classes was 89%. The individual predicted precision for the good, bad, and scratched classes was 95%, 68%, and 100%, respectively. Given that the robot system was robust and inexpensive, it has the potential to be integrated into a garlic root trimming machine to develop a fully automatic garlic root trimming and sorting robot.

Whole crop rice silage(WCRS) is fed to dairy cows and beef cattle in Japan. The silage contains grains of rice, which are not easily digested because the chaff is indigestible. This paper describes a new type crusher to make chaff digestible. In order to develop a WCRS processing device to decrease indigestible unhulled grain, a chaff removal and grain crushing test using the new device was conducted. Forces required to remove chaff from unhulled rice were measured in three different directions; this test showed that shearing forces were the most effective to remove chaff. Tearing off rice chaff partially by mechanically scratching the unhulled rice surface is adopted to make rice in the chaff digestible. A processing device for WCRS equipped with a new shape rotor was designed, and it could remove chaff from unhulled rice grain continuously without any problem. This machine will be useful for processing better WCRS.

The target of our research is to develop a technology for objectively measuring the density in a silo. In the present paper, we attempted to detect ultrasonic waveforms through shredded raw forage crops in order to obtain foundational data on measuring forage crops’ densities by nondestructive means. A function generator, oscilloscope, transmitting ultrasonic transducer, and receiving ultrasonic transducer were used in these experiments. Forage samples that had been formed to cylindrical cells, were made from shredded alfalfa and whole-crop rice. A well-spread ultrasonic wave could be emitted by ultrasonic transducer when rectangle or pulse waves were generated from the function generator. A 16V amplitude rectangle ultrasonic wave could pass through 75mm thick cylindrical forage samples of 650kg/m3 or greater density.