Automated object detection of mechanical fasteners using faster region based convolutional neural networks

Mechanical fasteners are widely used in manufacturing of hardware and mechanical components such as automobiles, turbine & power generation and industries. Object detection method play a vital role to make a smart system for the society. Internet of things (IoT) leads to automation based on sensors and actuators not enough to build the systems due to limitations of sensors. Computer vision is the one which makes IoT too much smarter using deep learning techniques. Object detection is used to detect, recognize and localize the object in an image or a real time video. In industry revolution, robot arm is used to fit the fasteners to the automobile components. This system will helps the robot to detect the object of fasteners such as screw and nails accordingly to fit to the vehicle moved in the assembly line. Faster R-CNN deep learning algorithm is used to train the custom dataset and object detection is used to detect the fasteners. Region based convolutional neural networks (Faster R-CNN) uses a region proposed network (RPN) network to train the model efficiently and also with the help of Region of Interest able to localize the screw and nails objects with a mean average precision of 0.72 percent leads to accuracy of 95 percent object detectio

A Concept Study for Feature Extraction and Modeling for Grapevine Yield Prediction

Yield prediction in viticulture is an especially challenging research direction within the field of yield prediction. The characteristics that determine annual grapevine yields are plentiful, difficult to obtain, and must be captured multiple times throughout the year. The processes currently used in grapevine yield prediction are based mainly on manually captured data and rigid statistical measures derived from historical insights. Experts for data acquisition are scarce, and statistical models cannot meet the requirements of a changing environment, especially in times of climate change. This paper contributes a concept on how to overcome those drawbacks, by (1) proposing a deep learning driven approach for feature recognition and (2) explaining how Extreme Gradient Boosting (XGBoost) can be utilized for yield prediction based on those features, while being explainable and computationally inexpensive. The methods developed will be influential for the future of yield prediction in viticulture

Efficient Deep Learning model for de-husked Areca nut classification

Areca nut is a widely used agricultural product in India and even over the globe. Areca nut, a fruit of   areca palm (Areca catechu) is grown widely in the Asia-Pacific region.. Areca nut segregation is of prime importance in the areca nut industry. The quality segregation of peeled/de-husked nuts requires skilled workers. This process of manual segregation is time-consuming and can lead to erroneous classification. Recent deep learning (DL) advances have improved the performance in multi-class problems. The present  work presents the classification of de-husked areca nut among five classes using an efficient deep learning customized Convolutional Neural Network (CNN) and the results of this model were compared with the standard AlexNet architecture. The new CNN model was customized to obtain classification accuracy higher than the existing ones. A dataset of 300 nuts (60 per class) was created using a specially designed instrumentation setup. The areca nut images were then pre-processed and fed to these models to learn the features of the areca nut from different classes. The confusion matrix and Area Under the Curve - Receiver Operating Characteristics (AUC- ROC) were employed to assess the results of these models and cross-validated with 5 and 10-fold. The experimental results show that the CNN outperformed the AlexNet model with an average accuracy of 97.33% and 98.34%, F1 score of 97.48%, and 98.45% for 5 and 10 folds, respectively.  

Apple recognition and picking sequence planning for harvesting robot in the complex environment

In order to improve the efficiency of robots picking apples in challenging orchard environments, a method for precisely detecting apples and planning the picking sequence is proposed. Firstly, the EfficientFormer network serves as the foundation for YOLOV5, which uses the EF-YOLOV5s network to locate apples in difficult situations. Meanwhile, the Soft Non-Maximum Suppression (NMS) algorithm is adopted to achieve accurate identification of overlapping apples. Secondly, the adjacently identified apples are automatically divided into different picking clusters by the improved density-based spatial clustering of applications with noise (DBSCAN). Finally, the order of apple harvest is determined to guide the robot to complete the rapid picking, according to the weight of the Gauss distance weight combined with the significance level. In the experiment, the average precision of this method is 98.84%, which is 4.3% higher than that of YOLOV5s. Meanwhile, the average picking success rate and picking time are 94.8% and 2.86 seconds, respectively. Compared with sequential and random planning, the picking success rate of the proposed method is increased by 6.8% and 13.1%, respectively. The research proves that this method can accurately detect apples in complex environments and improve picking efficiency, which can provide technical support for harvesting robots

Rust Disease Classification Using Deep Learning Based Algorithm: The Case of Wheat

Rusts are plant diseases caused by obligate fungi parasites. They are usually host-specific and cause greater losses of yields in crops, trees, and ornamental plants. Wheat is a staple food crop bearing losses specifically due to three species of rust fungi namely leaf rust (Puccinia triticina), stem rust (Puccinia graminis), and yellow rust (Puccinia striiformis). These diseases are usually inspected manually by a human being but at a large scale, this process is labor-intensive, time-consuming, and prone to human errors. Therefore, there is a need for an effective and efficient system that helps in the identification and classification of these diseases at early stages. In the present study, a deep learning-based CNN (i.e., VGG16) transfer learning model has been utilized for wheat disease classification on the CGIAR image dataset, containing two classes of wheat rust disease (leaf rust and stem rust), and one class of healthy wheat images. The deep learning models produced the best results by tuning the various hyper-parameters such as batch size, number of epochs, and learning rate. The proposed model has reported the best classification accuracy rate of 99.54% on 80 epochs using an initial learning rate from 0.01 and decayed to 0.0001

High-precision apple recognition and localization method based on RGB-D and improved SOLOv2 instance segmentation

Intelligent apple-picking robots can significantly improve the efficiency of apple picking, and the realization of fast and accurate recognition and localization of apples is the prerequisite and foundation for the operation of picking robots. Existing apple recognition and localization methods primarily focus on object detection and semantic segmentation techniques. However, these methods often suffer from localization errors when facing occlusion and overlapping issues. Furthermore, the few instance segmentation methods are also inefficient and heavily dependent on detection results. Therefore, this paper proposes an apple recognition and localization method based on RGB-D and an improved SOLOv2 instance segmentation approach. To improve the efficiency of the instance segmentation network, the EfficientNetV2 is employed as the feature extraction network, known for its high parameter efficiency. To enhance segmentation accuracy when apples are occluded or overlapping, a lightweight spatial attention module is proposed. This module improves the model position sensitivity so that positional features can differentiate between overlapping objects when their semantic features are similar. To accurately determine the apple-picking points, an RGB-D-based apple localization method is introduced. Through comparative experimental analysis, the improved SOLOv2 instance segmentation method has demonstrated remarkable performance. Compared to SOLOv2, the F1 score, mAP, and mIoU on the apple instance segmentation dataset have increased by 2.4, 3.6, and 3.8%, respectively. Additionally, the model’s Params and FLOPs have decreased by 1.94M and 31 GFLOPs, respectively. A total of 60 samples were gathered for the analysis of localization errors. The findings indicate that the proposed method achieves high precision in localization, with errors in the X, Y, and Z axes ranging from 0 to 3.95 mm, 0 to 5.16 mm, and 0 to 1 mm, respectively

RS-Net: robust segmentation of green overlapped apples

Fruit detection and segmentation will be essential for future agronomic management, with applications in yield estimation, growth monitoring, intelligent picking, disease detection and etc. In order to more accurately and efficiently realize the recognition and segmentation of apples in natural orchards, a robust segmentation net framework specially developed for fruit production is proposed. This model was improved for the more challenging problem which segments the overlapped apples from the monochromatic background regardless of various corruptions. The method extends Mask R-CNN by embedding an attention mechanism for focusing more on the informative pixels but also suppressing the noise caused by adverse factors (occlusions, overlaps, etc.), which could be more suitable and robust for operating in complex natural environment. Specifically, the Gaussian non-local attention mechanism is transplanted into Mask R-CNN for refining the semantic features generated continuously by residual network and feature pyramid network, then the model forward processing based on the balanced feature levels and finally segments the regions where the apples are located. Experimental results verify the hypothesis of current work and show that the proposed method outperforms other start-of-the-art detection and segmentation models, the AP box and AP mask metric values have reached 85.6% and 86.2% in a reasonable run time, respectively, which can meet the precision and robustness of vision system in agronomic managemen

InsectCV: a system for insect detection in the lab from trap images.

Advances in artificial intelligence, computer vision, and high-performance computing have enabled the creation of efficient solutions to monitor pests and identify plant diseases. In this context, we present InsectCV, a system for automatic insect detection in the lab from scanned trap images. This study considered the use of Moericke-type traps to capture insects in outdoor environments. Each sample can contain hundreds of insects of interest, such as aphids, parasitoids, thrips, and flies. The presence of debris, superimposed objects, and insects in varied poses is also common. To develop this solution, we used a set of 209 grayscale images containing 17,908 labeled insects. We applied the Mask R-CNN method to generate the model and created three web services for the image inference. The model training contemplated transfer learning and data augmentation techniques. This approach defined two new parameters to adjust the ratio of false positive by class, and change the lengths of the anchor side of the Region Proposal Network, improving the accuracy in the detection of small objects. The model validation used a total of 580 images obtained from field exposed traps located at Coxilha, and Passo Fundo, north of Rio Grande do Sul State, during wheat crop season in 2019 and 2020. Compared to manual counting, the coefficients of determination (R2 = 0.81 for aphids and R2 = 0.78 for parasitoids) show a good-fitting model to identify the fluctuation of population levels for these insects, presenting tiny deviations of the growth curve in the initial phases, and in the maintenance of the curve shape. In samples with hundreds of insects and debris that generate more connections or overlaps, model performance was affected due to the increase in false negatives. Comparative tests between InsectCV and manual counting performed by a specialist suggest that the system is sufficiently accurate to guide warning systems for integrated pest management of aphids. We also discussed the implications of adopting this tool and the gaps that require further development. Keywords: Convolutional neural network; Mask r-cnn; Object detection; Pest detection; Aphids; Warning system

A review on the application of computer vision and machine learning in the tea industry

Tea is rich in polyphenols, vitamins, and protein, which is good for health and tastes great. As a result, tea is very popular and has become the second most popular beverage in the world after water. For this reason, it is essential to improve the yield and quality of tea. In this paper, we review the application of computer vision and machine learning in the tea industry in the last decade, covering three crucial stages: cultivation, harvesting, and processing of tea. We found that many advanced artificial intelligence algorithms and sensor technologies have been used in tea, resulting in some vision-based tea harvesting equipment and disease detection methods. However, these applications focus on the identification of tea buds, the detection of several common diseases, and the classification of tea products. Clearly, the current applications have limitations and are insufficient for the intelligent and sustainable development of the tea field. The current fruitful developments in technologies related to UAVs, vision navigation, soft robotics, and sensors have the potential to provide new opportunities for vision-based tea harvesting machines, intelligent tea garden management, and multimodal-based tea processing monitoring. Therefore, research and development combining computer vision and machine learning is undoubtedly a future trend in the tea industry