Vol. 82, No. 6 Abstract

 

Biosensing Method of Growth Diagnosis in Forcing Culture of Strawberry
――Biological Indicators for Evaluating Growth Inhibition under Low Temperatures――

Shogo TSUBOTA, Kazuhiko NAMBA, Yasunaga IWASAKI, Tokihiro FUKATSU, Hiroki NAITO, Tomohiko OTA

[Keywords: strawberry, forcing culture, low temperature, growth inhibition, growth evaluation, biological information]

 

 To develop a labor-saving growth diagnosis system for forcing culture of strawberry, a biosensing method is required. In this study, biological indicators suitable for evaluating growth inhibition in a low temperature period were determined from weekly measurements. Growth differences that may have been caused by physical states appeared in the newer leaves. Not only the third-leaf petiole length, which is used by producers, but also the third-leaf area and height indicated growth inhibition. Moreover, earlier estimation was enabled by using the second leaf. The first leaf, which was expected to be a much earlier indicator, was not suitable because of weekly fluctuations in the petiole length information. Leaf development frequency and height were proposed as indicators instead.

Performance Evaluation of Current Models for Development of Advanced Potato Harvester

Kunihiro FUNABIKI, Tadatoshi SATOW, Atsuru FUJIMOTO

[Keywords: potato harvester,potato,damage rate,working efficiency,soil tare]

 

 Potato harvesters are being increasingly required to improve operational accuracy and efficiency in response to the increase in potato production areas. We conduct operation accuracy and working efficiency tests for five current models. By evaluating the results, we consider the performance requirements of advance harvesters. The results clearly demonstrate that a domestic offset-type harvester is more accurate and efficient than an old model inline-type harvester for edible potatoes. Next, an inline-type harvester for starch production is developed specifically for efficiency. The new domestic offset-type harvester is superior to the overseas offset-type by 4.0 points in the potato damage rate, and the work efficiency of the overseas harvester is 1.3 times higher than that of the new domestic offset harvester.

Development of an Autonomous Driving Control System for a Robotic Mower Using a Low-Cost Single-Frequency GNSS Compass

Sho IGARASHI, Yutaka KAIZU, Toshio TSUTSUMI, Kenji, IMOU

[Keywords: autonomous driving, mower, GNSS, GPS, GNSS compass, low-cost sensor, ROS, MAVLink]

 

 We developed an autonomous driving control system for a robotic mower using a low-cost single-frequency GNSS compass and a low-cost single-frequency RTK-GNSS. Basis the result of the autonomous driving test, the cross-track error was 2.4 cm RMS and heading error was 2.1\_p° \/RMS. The effectiveness of the autonomous driving control system was confirmed, because no additional cutting was needed when the error was within 10 cm. The suggestion was made that a low-cost single-frequency GNSS compass was a promising sensor for azimuth measurements. The results of this study indicate the possibility of adding the autonomous driving system to a self-propelled machine at low cost.

Development of High-Speed Ridge Forming Seeder for Soybeans, Adapted to Moist Soil Conditions
――Seeding Performance of Prototype Seeder――

Kenta SHIGEMATSU, Satoshi ONO, Sadayuki TAKAYAMA, Jun ENDO, Kazuhiko NAMBA

[Keywords: soybeans, seeder, ridge forming seeding, speeding up, moist soil conditions]

 

 This study aimed to develop a prototype seeder for soybeans. Existing seeder which consists up-cut rotary cultivation, ridging, and seeding has been developed. This seeder was working at a slow speed of 0.3 to 0.6 m/s and kneading occurred on wet soil. Testing the developed prototype seeder working speed was 1.6 m/s on dry soil. The prototype seeder was able to work at 1.2 m/s twice as fast as the existing seeder when tested on wet soil with a soil liquidity index larger than 1. The germination rate of the existing seeder decreased because of the crust on the soil surface, whereas that of the prototype seeder was more than 90 %. Thus, the prototype seeder was found to be more adaptable to wet soil conditions than the existing seeder.

Prediction of Potato Yield Using Unmanned Aerial Vehicle and Convolutional Neural Network

Dai TANABE, Shigeru ICHIURA, Ayumi NAKATSUBO, Takashi KOBAYASHI, Mitsuhiko KATAHIRA

[Keywords: deep learning, information and communication technology (ICT), normalized difference vegetation index (NDVI), potato, unmanned aerial vehicles (UAV)]

 

 We used UAV as a remote sensing device in potato cultivation and assessed yield prediction by CNN with aerial image and yield data. The experiment was performed in a potato field with six fertilization conditions, aerial images were acquired during the flowering period, and growth and yield were investigated. Yield prediction models were constructed by regression analysis and image regression method by CNN. In the aerial image, the difference in coverage was confirmed depending on the treatment section, and the treatment section difference occurred in the plant height as the growth progressed. Regarding yield prediction, the yield prediction model by image regression method by CNN was able to predict the potato yield with high accuracy than regression analysis.

Exploring Freshness Markers of Postharvest Spinach by Volatile Emission Profiling

Ayaka SOGA, Makoto YOSHIDA, Shinichiro KUROKI , Mizuki TSUTA, Nobutaka NAKAMURA, Teppei IMAIZUMI, Manasikan THAMMAWONG, Kohei NAKANO

[Keywords: cumulative storage temperature, freshness marker, gas chromatography-mass spectrometry, metabolomics, spinach, volatile compounds]

 

 A gas chromatography-mass spectrometry (GC-MS) based metabolomics approach was used to identify specific volatile compounds as an indicator for assessing the freshness degree of harvested spinach leaves during storage. Volatiles produced from the leaves stored at 5 ℃, 15 ℃, and 25 ℃were collected periodically in a gas adsorption tube and subsequently withdrawn into GC-MS by a thermal desorption method. Partial least squares regression for predicting the cumulative storage temperature based on the detected volatiles has selected ten substances as important volatiles that can explain the degrees of freshness, which belong to terpene, alcohol, aldehyde, hydrocarbon, and unknown ones. A hierarchical cluster analysis revealed that the composition ratios of these compounds allowed estimating the cumulative storage temperature of the samples in three levels. These results suggest that the profiling of volatile compounds emitted from stored spinach could probably be a practical method for assessing the degrees of freshness.

Optimization of the Lighting Environment in Sawdust-Based Shiitake Cultivation
――Effect of Light Timing on Fruit Body Development During the Culture Stage――

Koji OKADA, Kazuhiko NAMBA, Mitsuji MONTA, Yasuaki KASHINO

[Keywords: shiitake, sawdust-based cultivation, fruit body development, lighting timing, harvested number]

 

 Currently, shiitake (Lentinula edodes) is mainly produced by sawdust-based cultivation in a controlled environment. Lighting is only known as a necessity for culture and fruit body development. In this study, we focused on the timing of lighting during in the culture stage and examined its effects on shiitake development. When timing was delayed, a smaller number of fruit bodies developed, but they had a larger fresh weight and diameter. The results suggested the possibility of controlling fruit body size by timing of lighting in the primary culture. However, there were no differences when shiitake were grown under the optimum temperature. Total environmental control, including during development and other stages, would be required to utilize the effect of the timing of lighting.

Learning System for Estimating the Area Ratio of Flowers-fruit and Leaves-stems from Tomato Plant Images Using Semi-supervised Learning

Seiji MATSUO, Hiroki UMEDA, Yasunaga IWASAKI

[Keywords: plant vigour, tomato cultivation, deep learning, semantic segmentation, image recognition, semi-supervised learning, annotation]

 

 The authors constructed a learning system for estimating the area ratio of flowers-fruit and leaves-stems from tomato-cultivation images as one of the indices for diagnosing the plant vigor of cultivation. The annotation work for object labeling, which is performed as an image analysis preprocessing, incurs a massive computation cost. Therefore, this study proposed a semi-supervised learning method that combines classification using unsupervised learning along with a backpropagation and segmentation model through object detection using supervised learning. As a result, the area ratio of leaves-stems to flowers-fruit is recognized with a high recognition rate, suggesting the effectiveness of the proposed system with a reduced computational cost.