Vol. 83, No. 4 Abstract

 

Correlation between Cutting Load and Crop Amount in Combine for Multi - Crops
――Consideration Based on the Power Consumption of the Quasi-Static Electric Cutting Knife Mechanism――

Daichi HIROTA, Yuko UEKA, Shuhei OHATA, Yoshinori DOI, Seiichi ARIMA, Masami MATSUI

[Keywords: combine for multi-crops, electric reciprocating knife, amount of crops, cutting energy, power consumption, machine learning]

 

 Owing to the expansion of target crops of multi-cropping, problems arise in work accuracy; and improvement in versatility can be done by making each work part electric and tweakable. We aimed to build a more optimal control system by detecting the crop amounts in the cutting section and using them as feedback for the control parameters. We created an extremely low-speed reciprocating electric knife. The cutting load was determined based on the feedback system and the power consumption waveform showing the correlation between the cutting load and the crop information under quasi-static conditions. As a result, four parameters were extracted based on the correlation between power consumption and cutting force and the results are discussed.

Development of Pear Cultivation Management Technology Using Information and Communications Technology for the Next Generation of Farmers (Part 1)
――Development of a Tree Extraction Method Applying 3D Laser Scanning――

Jaehwan LEE, Tsuyoshi YOSHIDA, Kazuyoshi NONAMI, Ichizen MATSUMURA, Akira YANO, Eiji MORIMOTO

[Keywords: 3D laser scanner, point cloud data, tree extraction, reflective intensity, radius outlier removal]

 

 We built a prototype measurement jig for installing 3D laser scanners at multiple points in pear orchard; further we developed methods for collecting point cloud data and “tree extraction,” which enable to identify pear trees in the point cloud. The proposed tree extraction algorithm consists of four steps: Create grid; Remove points that lie on the ground surface; Remove points that lie on trellis wires using reflective intensity threshold; and Remove micro-noise via radius outlier removal. We measured 14 pear trees and successfully integrated 13,843,644 points into point cloud. The result obtained via tree extraction method demonstrate that the removal ratios for ground, wire, and micro-noise components were 100 %, 99.6 %, and 87.6 %, respectively, and the tree extraction accuracy was 94.5 %.

Estimating the Sugar Content Distribution of the Japanese Pear Using Infrared Hyperspectral Imaging

Hayato SEKI, Masaru KASHIWAZAKI

[Keywords: hyperspectral imaging, near-infrared spectroscopy, Japanese pears, sugar content distribution, PLS regression]

 

 Near-infrared (900-1700 nm) hyperspectral imaging was used to visualize the sugar content distribution in cross-sections of the Japanese pear “Nikkori.” The average absorbance spectrum (approximately 11×11 mm) and sugar content for each block of the fruit cross section were measured using a grid gauge. A sugar content estimation model with high prediction accuracy was successfully developed using partial-least-squares (PLS) regression analysis. The prediction coefficient of determination (R2 p) between the predicted and measured sugar content was 0.73, with a root mean square error of prediction (RMSEP) of 0.68. The coefficient of determination between the mean value of the sugar-content distribution mapped by this model and the mean value of the measured sugar-content distribution was 0.91.

Evaluation of Individual Detection Performance of Broiler Chickens Based on Object Detection Algorithm by Deep Learning

Shigeru ICHIURA, Tomohiro MORI, Tong MENG, Hiroki MATSUYAMA, Kenichi HORIGUCHI, Mitsuhiko KATAHIRA

[Keywords: accuracy evaluation, activity amount, weight gain rate, transfer learning, object detection, chicken, broiler, YOLOv4, re-learning]

 

 This study was conducted to develop a labor-saving individual detection method for broiler (meat chicken) breeding management. After individual detection using an object-detection algorithm based on deep learning technology, we evaluated the accuracy of the object detection model. Using a surveillance camera installed on the breeding facility ceiling, a new object detection model was created and operated continuously for about 1 week with regular re-learning using recorded growth images. Consequently, a method for individual detection was configured. Results show that continuous detection for 5-week-old individuals can be achieved by re-learning for this amount of activity and for a weight gain rate of 20 % or less.

Prediction of Oil Content in Avocado (Persea americana) Using Fluorescence Excitation Emission Matrix

Yoshito SAITO, Yuuka MIWA, Makoto KURAMOTO, Keiji KONAGAYA, Atsuhiro YAMAMOTO, Shintaro HASHIGUCHI, Tetsuhito SUZUKI, Naoshi KONDO

[Keywords: avocado, oil content, excitation emission matrix, cuticle, wax]

Tractor Implement Length Required to Pull in a Drainpipe from inside the Field over the Paddy Levees

Naoya KAWARADA

[Keywords: agricultural tractor, drainpipe, retractor, tractor implement length, paddy levees]

Development of Drainpipe Retractable Implement with Agricultural Tractor for New Outlet and Main Drain

Naoya KAWARADA, Hiroyuki OKA, Tadakazu YAMAGUCHI

[Keywords: outlet, main drain, agricultural tractor, drainpipe, retractor, tractor implement]