scholarly journals Convolutional Neural Networks for Image-Based Corn Kernel Detection and Counting

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2721 ◽  
Author(s):  
Saeed Khaki ◽  
Hieu Pham ◽  
Ye Han ◽  
Andy Kuhl ◽  
Wade Kent ◽  
...  
Keyword(s):  
Human Error ◽  
Sliding Window ◽  
Corn Kernel ◽  
Lighting Conditions ◽  
Image Patches ◽  
Detection And Counting ◽  
Grain Marketing ◽  
Window Approach ◽  
Corn Kernels ◽  
Corn Grain

Precise in-season corn grain yield estimates enable farmers to make real-time accurate harvest and grain marketing decisions minimizing possible losses of profitability. A well developed corn ear can have up to 800 kernels, but manually counting the kernels on an ear of corn is labor-intensive, time consuming and prone to human error. From an algorithmic perspective, the detection of the kernels from a single corn ear image is challenging due to the large number of kernels at different angles and very small distance among the kernels. In this paper, we propose a kernel detection and counting method based on a sliding window approach. The proposed method detects and counts all corn kernels in a single corn ear image taken in uncontrolled lighting conditions. The sliding window approach uses a convolutional neural network (CNN) for kernel detection. Then, a non-maximum suppression (NMS) is applied to remove overlapping detections. Finally, windows that are classified as kernel are passed to another CNN regression model for finding the ( x , y ) coordinates of the center of kernel image patches. Our experiments indicate that the proposed method can successfully detect the corn kernels with a low detection error and is also able to detect kernels on a batch of corn ears positioned at different angles.

scholarly journals Sliding-Window Thompson Sampling for Non-Stationary Settings

2020 ◽  
Vol 68 ◽  
pp. 311-364
Author(s):  
Francesco Trovo ◽  
Stefano Paladino ◽  
Marcello Restelli ◽  
Nicola Gatti
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Sliding Window ◽  
Upper Bounds ◽  
Decision Problems ◽  
Sequential Decision ◽  
Thompson Sampling ◽  
The Past ◽  
Real World Applications ◽  
Window Approach

Multi-Armed Bandit (MAB) techniques have been successfully applied to many classes of sequential decision problems in the past decades. However, non-stationary settings -- very common in real-world applications -- received little attention so far, and theoretical guarantees on the regret are known only for some frequentist algorithms. In this paper, we propose an algorithm, namely Sliding-Window Thompson Sampling (SW-TS), for nonstationary stochastic MAB settings. Our algorithm is based on Thompson Sampling and exploits a sliding-window approach to tackle, in a unified fashion, two different forms of non-stationarity studied separately so far: abruptly changing and smoothly changing. In the former, the reward distributions are constant during sequences of rounds, and their change may be arbitrary and happen at unknown rounds, while, in the latter, the reward distributions smoothly evolve over rounds according to unknown dynamics. Under mild assumptions, we provide regret upper bounds on the dynamic pseudo-regret of SW-TS for the abruptly changing environment, for the smoothly changing one, and for the setting in which both the non-stationarity forms are present. Furthermore, we empirically show that SW-TS dramatically outperforms state-of-the-art algorithms even when the forms of non-stationarity are taken separately, as previously studied in the literature.

A sliding-window approach for improved VMAT dose calculation accuracy

2020 ◽  
Vol 45 (3) ◽  
pp. 197-201
Author(s):  
Haitham N Alahmad ◽  
Ji-Yeon Park ◽  
Nicholas J Potter ◽  
Bo Lu ◽  
Guanghua Yan ◽  
...  
Keyword(s):  
Dose Calculation ◽  
Sliding Window ◽  
Window Approach

scholarly journals Reducing False Negative Reads in RFID Data Streams Using an Adaptive Sliding-Window Approach

Sensors ◽  
2012 ◽  
Vol 12 (4) ◽  
pp. 4187-4212 ◽  
Author(s):  
Libe Valentine Massawe ◽  
Johnson D. M. Kinyua ◽  
Herman Vermaak
Keyword(s):  
Data Streams ◽  
False Negative ◽  
Sliding Window ◽  
Rfid Data ◽  
Window Approach

scholarly journals An improved real-time object proposals generation method based on local binary pattern

2017 ◽  
Vol 14 (4) ◽  
pp. 172988141772467 ◽  
Author(s):  
Yanting Jiang ◽  
Jia Yan ◽  
Ci’en Fan ◽  
Wenxuan Shi ◽  
Dexiang Deng
Keyword(s):  
Local Binary Pattern ◽  
Sliding Window ◽  
High Accuracy ◽  
Processing Unit ◽  
Data Set ◽  
Central Processing ◽  
Lighting Conditions ◽  
Occluded Objects ◽  
Object Proposals ◽  
Short Time

Generating a group of category-independent proposals of objects in an image within a very short time is an effective approach to accelerate traditional sliding window search, which has been widely used in preprocessing step of object recognition. In this article, we propose a novel object proposals generation method to produce an order set of candidate windows covering most of object instances. With combination of gradient and local binary pattern, our approach achieves better performance than BING in finding occluded objects and objects in dim lighting conditions. In experiments on the challenging PASCAL VOC 2007 data set, we show that our approach is significantly more accurate than BING. In particular, using 2000 proposals, we achieve 97.6% object detection rate and 69.3% mean average best overlap. Moreover, our proposed method is very efficient and takes only about 0.006 s per image on a laptop central processing unit. The detection speed and high accuracy of proposed method mean that it can be applied to recognizing specific objects in robot visions.

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

Agriculture ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1238
Author(s):  
Xiaoyu Li ◽  
Yuefeng Du ◽  
Lin Yao ◽  
Jun Wu ◽  
Lei Liu
Keyword(s):  
Real Time ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Agricultural Machinery ◽  
Corn Kernel ◽  
Laboratory Results ◽  
Neural Network Algorithm ◽  
Combine Harvesters ◽  
Corn Kernels ◽  
Good Agreement

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.

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