A wedge-shaped no-tillage opener consisting of sharp front knife edge and pedestal with lateral wings has been designed. The front knife is connected with the pedestal by means of inner bolts and can be conveniently changed when it is abraded.Tests of resistance and power consumption are carried out in the field covered with maize straw.Test results show that the wedged-shaped no-tillage opener can reduce the surface soil disturbance to between 15% and 25%,and reduce the straw coverage rate by 10%～20%, so it can satisfy the requirement of no-tillage sowing.In comparison to the tine opener,it has the advantages that the soil bulk density is decreased significantly,the resistance and the fuel consumption are reduced by 13.2% and 15% separately when the depth of furrow opening is 10 cm,thus reducing the power consumption of tractor.So the wedged-shaped no-tillage opener solves the problem of high resistance and power consumption when the depth of furrow opening is 10 cm,and creates the loose seedbed,and which has important significance to the popularization and application of no-tillage seeders in China.

Design and Experiment on a Wedge Shaped No-tillage Opener

Detecting sugar beetroot crops with mechanical damage using machine learning methods is necessary for fine-tuning beet harvester units. The Agrifac HEXX TRAXX harvester with an installed computer vision system was investigated. A video camera (24 fps) was installed above the turbine, which receives the dug-out beets after the digger and is connected to a single-board computer. At the preprocessing stage, static and insignificant image details were revealed. Canny edge detector and excess green minus excess red (ExGR) method were used. The identified areas were excluded from the image. The remaining areas were glued with similar areas of another image. As a result, the number of images entering the second stage of preprocessing was reduced by half. Then Otsu’s binarization was used. The main stage of image processing is divided into two sub-stages: detection and classification. The improved YOLOv4-tiny method was chosen for root crop detection using a single-board computer (SBC). This method allows processing up to 14 images of 416 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times416$ </tex-math></inline-formula> pixels with 86% precision and 91% recall. To classify root crop damage, we considered two algorithms as candidates: 1. bag of visual words (BoVW) with a support vector machine (SVM) classifier using histogram of oriented gradients (HOG) and scale-invariant feature transform (SIFT) descriptors; 2. convolutional neural networks (CNN). Under normal lighting conditions, CNN showed the best accuracy, which was 99%. The implemented methods were used to detect and classify blurred images of sugar beetroots, which were previously rejected. For improved YOLOv4-tiny precision was 74% and recall was 70%. CNN classification accuracy was 92.6%. 

Identification and Classification of Mechanical Damage During Continuous Harvesting of Root Crops Using Computer Vision Methods

In precision agriculture, effective monitoring of corn pest regions is crucial to developing early scientific prevention strategies and reducing yield losses. However, complex backgrounds and small objects in real farmland bring challenges to accurate detection. In this paper, we propose an improved model based on YOLOv4 that uses contextual information and attention mechanism. Firstly, a context priming module with simple architecture is designed, where effective features of different layers are fused as additional context features to augment pest region feature representation. Secondly, we propose a multi-scale mixed attention mechanism (MSMAM) with more focus on pest regions and reduction of noise interference. Finally, the mixed attention feature-fusion module (MAFF) with MSMAM as the kernel is applied to selectively fuse effective information from additional features of different scales and alleviate the inconsistencies in their fusion. Experimental results show that the improved model performs better in different growth cycles and backgrounds of corn, such as corn in vegetative 12th, the vegetative tasseling stage, and the overall dataset. Compared with the baseline model (YOLOv4), our model achieves better average precision (AP) by 6.23%, 6.08%, and 7.2%, respectively. In addition, several comparative experiments were conducted on datasets with different corn growth cycles and backgrounds, and the results verified the effectiveness and usability of the proposed method for such tasks, providing technical reference and theoretical research for the automatic identification and control of pests.

https://www.mdpi.com/2077-0472/12/8/1104/pdf?version=1658927956

Pest Region Detection in Complex Backgrounds via Contextual Information and Multi-Scale Mixed Attention Mechanism

In present-day mechanized garlic seeding, high missing rates and low qualified percentages of single seeds are common problems; thus, a finger clip plate garlic seed-metering device was designed in this study. First, the structure and working principle of the seed-metering device were studied. Subsequently, the critical component parameters of the seed-metering device were determined using theoretical calculations; then, EDEM software was used in single-factor simulation experiments to analyze the effects of opening the diameter of the seed-collecting spoon, the operating speed of the seeding tray, and the population number on the seed-filling performance. Finally, a Box–Behnken center combination experiment was conducted with the population, opening the diameter of the seed-collecting spoon, and rotating the speed of the seeding tray as experiment factors, with the single-seed filling rate, qualified percent, and missing rate as evaluation indicators. A three-factor and three-level orthogonal test was conducted to establish the mathematic regression model of the experiment factors and evaluation indicators, as well as to realize the parameter optimization. After rounding, the laboratory validation test was carried out with 240~280 seeds, a 26 mm seed scoop-opening diameter, and a 28 r/min operating speed. The average qualified rate, missing rate, and reply rate of single seeds were 91.86%, 2.71%, and 5.43%, respectively, which is basically consistent with the forecast results of regression model.

/pdf/design-and-parameter-optimization-of-a-finger-clip-plate-3jbcl9ix.pdf

Design and Parameter Optimization of a Finger Clip Plate Garlic Seed-Metering Device Based on EDEM

Fruit and vegetable inspection aids robotic harvesting in modern agricultural production. For rapid and accurate detection of fresh shiitake mushrooms, picking robots must overcome the complex conditions of the growing environment, diverse morphology, dense shading, and changing field of view. The current work focuses on improving inspection accuracy at the expense of timeliness. This paper proposes a lightweight shiitake mushroom detection model called Mushroom You Only Look Once (MYOLO) based on You Only Look Once (YOLO) v3. To reduce the complexity of the network structure and computation and improve real-time detection, a lightweight GhostNet16 was built instead of DarkNet53 as the backbone network. Spatial pyramid pooling was introduced at the end of the backbone network to achieve multiscale local feature fusion and improve the detection accuracy. Furthermore, a neck network called shuffle adaptive spatial feature pyramid network (ASA-FPN) was designed to improve fresh shiitake mushroom detection, including that of densely shaded mushrooms, as well as the localization accuracy. Finally, the Complete Intersection over Union (CIoU) loss function was used to optimize the model and improve its convergence efficiency. MYOLO achieved a mean average precision (mAP) of 97.03%, 29.8M parameters, and a detection speed of 19.78 ms, showing excellent timeliness and detectability with a 2.04% higher mAP and 2.08 times fewer parameters than the original model. Thus, it provides an important theoretical basis for automatic picking of fresh shiitake mushrooms.

https://www.mdpi.com/2077-0472/13/2/392/pdf?version=1676885084

MYOLO: A Lightweight Fresh Shiitake Mushroom Detection Model Based on YOLOv3

Design and experiment of adjustment device based on machine vision for garlic clove direction

A consistent orientation of ginger shoots when sowing ginger is more conducive to high yields and later harvesting. However, current ginger sowing mainly relies on manual methods, seriously hindering the ginger industry’s development. Existing ginger seeders still require manual assistance in placing ginger seeds to achieve consistent ginger shoot orientation. To address the problem that existing ginger seeders have difficulty in automating seeding and ensuring consistent ginger shoot orientation, this study applies object detection techniques in deep learning to the detection of ginger and proposes a ginger recognition network based on YOLOv4-LITE, which, first, uses MobileNetv2 as the backbone network of the model and, second, adds coordinate attention to MobileNetv2 and uses Do-Conv convolution to replace part of the traditional convolution. After completing the prediction of ginger and ginger shoots, this paper determines ginger shoot orientation by calculating the relative positions of the largest ginger shoot and the ginger. The mean average precision, Params, and giga Flops of the proposed YOLOv4-LITE in the test set reached 98.73%, 47.99 M, and 8.74, respectively. The experimental results show that YOLOv4-LITE achieved ginger seed detection and ginger shoot orientation calculation, and that it provides a technical guarantee for automated ginger seeding.

Ginger Seeding Detection and Shoot Orientation Discrimination Using an Improved YOLOv4-LITE Network

Consistent ginger shoot orientation helps to ensure consistent ginger emergence and meet shading requirements. YOLO v3 is used to recognize ginger images in response to the current ginger seeder’s difficulty in meeting the above agronomic problems. However, it is not suitable for direct application on edge computing devices due to its high computational cost. To make the network more compact and to address the problems of low detection accuracy and long inference time, this study proposes an improved YOLO v3 model, in which some redundant channels and network layers are pruned to achieve real-time determination of ginger shoots and seeds. The test results showed that the pruned model reduced its model size by 87.2% and improved the detection speed by 85%. Meanwhile, its mean average precision (mAP) reached 98.0% for ginger shoots and seeds, only 0.1% lower than the model before pruning. Moreover, after deploying the model to the Jetson Nano, the test results showed that its mAP was 97.94%, the recognition accuracy could reach 96.7%, and detection speed could reach 20 frames·s−1. The results showed that the proposed method was feasible for real-time and accurate detection of ginger images, providing a solid foundation for automatic and accurate ginger seeding.

Using Channel and Network Layer Pruning Based on Deep Learning for Real-Time Detection of Ginger Images

Variable transmission ratio racks show great potential in rice transplanters as a key component of variable transmission ratio steering to balance steering portability and sensitivity. The objective of this study was to develop a novel geometrical design method to achieve quick, high-quality modeling of the free curvilinear tooth profile of a variable transmission ratio rack. First, a discrete envelope motion 3D model was established between the pinion-sector and the variable transmission ratio rack blank based on the mapping relationship between the rotation angle of the pinion-sector and the displacement of the rack, according to the variable transmission ratio function. Based on the loop Boolean subtraction operation, which removed the pinion-sector from the rack blank during all moments of the discrete motion process, the final complex changing tooth shape of the variable transmission ratio rack was enveloped. Then, since Boolean cutting residues made the variable ratio tooth surface fluctuant and eventually affected the precision of the model, this study proposed a modification method for establishing a smooth and continuous tooth profile. First, a novel fitting algorithm used approximate variable ratio tooth profile points extracted from the Boolean cutting marks and generated a series of variable ratio tooth profiles by utilizing B-spline with different orders. Next, based on a transmission stability simulation, the variable ratio tooth profile with optimal dynamic performance was selected as the final design. Finally, tests contrasting the transmission stability of the machining samples of the initial variable ratio tooth profile and the final variable ratio tooth profile were conducted. The results indicated that the final variable ratio tooth profile is more effective than the initial variable ratio tooth profile. Therefore, the proposed variable ratio tooth profile modeling and modification method for eliminating Boolean cutting residues and improving surface accuracy is proved to be feasible.
Keywords: rice transplanter, steering, variable ratio tooth profile, variable ratio curve, Boolean subtraction operation, transmission stability
DOI: 10.25165/j.ijabe.20201305.4884

Citation: Niu Z R, Li J L, Xin S, Zou L L, Li Y H, Hou J L, et al. Geometrical design of variable ratio tooth profile based on Boolean subtraction operation and a novel modification method. Int J Agric & Biol Eng, 2020; 13(5): 125–133.

Geometrical design of variable ratio tooth profile based on Boolean subtraction operation and a novel modification method

The utility model relates to a self -propelled multirow garlic combine harvester, including the limit dark and hold up standing grain mechanism, engagement type power excavation dish mechanism, centregripping conveyor, go to native alignment mechanism, cut seedling mechanism, the rice transplanting device, the root must resection device and head of garlic collecting box, centre gripping conveyorinstalls in the chassis top, engagement type power excavation dish mechanism and centre gripping conveyor's from the driven pulleys hub connection, the limit is dark and hold up standing grain mechanism to install the front end at centre gripping conveyor's centre gripping carriage, go to native alignment mechanism to install in centre gripping conveyor middle part below, cutting seedling mechanism and installing at centre gripping conveyor's from the driven pulleys epaxially, the rear end at centre gripping conveyor must a resection device be installed to the root, the terminal below at rootpalpus resection device is installed to the garlic collecting box. The utility model discloses adopting the combining in the results mode, can once only holding up operations such as the standing grain excavates, the separation of native garlic, the clear soil of secondary, the excision of garlic seedling, fibrous root excision, head of garlic collection to the garlic, the multirow is gathered inthe crops simultaneously, and the operating efficiency is high.

Li Yuhua

Papers

Design and experiment of adjustment device based on machine vision for garlic clove direction

Ginger Seeding Detection and Shoot Orientation Discrimination Using an Improved YOLOv4-LITE Network

Using Channel and Network Layer Pruning Based on Deep Learning for Real-Time Detection of Ginger Images

Geometrical design of variable ratio tooth profile based on Boolean subtraction operation and a novel modification method

Self -propelled multirow garlic combine harvester