Showing papers in "Information Processing in Agriculture in 2022"

TL;DR: In this paper , an analysis of drone technologies and their modifications with time in the agriculture sector in the last decade is presented, where the application of drones in the area of crop monitoring, and pesticide spraying for Precision Agriculture (PA) has been covered.

TL;DR: In this article , the authors proposed an efficient and effective machine vision system based on the state-of-the-art deep learning techniques and stacking ensemble methods to offer a non-destructive and cost-effective solution for automating the visual inspection of fruits' freshness and appearance.

TL;DR: In this article , the authors systematically review the key technologies of agricultural energy Internet for two areas: agriculture and fishery, and provide novel perspectives on the promotion of the development of Agricultural Energy Internet and related technological innovation research.

TL;DR: In this article , a rural energy internet was constructed to study the impact of rural energy development on rural carbon emissions, and the benefits of the internet in practical application, including energy and carbon benefits, were presented through three application cases.

TL;DR: ResTS (Residual Teacher/Student) as discussed by the authors is a tertiary adaptation of the teacher/student architecture for diagnosis of the plant disease, which can yield finer visualizations of symptoms of the disease.

TL;DR: In this article , the authors presented an effective and practical system capable of segmenting and classifying different types of leaf lesions and estimating the severity of stress caused by biotic agents in coffee leaves using convolutional neural networks.

TL;DR: A distributed environmental monitoring system for the combination of hydroponics and aquaculture based on the internet of things technology, which mainly includes the information perception layer, the information transmission layer and the system architecture was developed in this article .

TL;DR: In this article , a gradient boost decision tree (GBDT) model based on the newly developed Light Gradient Boosting Machine algorithm (LightGBM or LGBM) was proposed to model the internal temperature of a greenhouse.

TL;DR: In this paper , the authors proposed a method to generate smooth trajectories for fruit-picking robot manipulators using shortcuts that are constrained in velocity, acceleration and jerk, which is a post-process to trajectory planning.

TL;DR: In this paper , the authors fuse the information extracted from the available resources and optimize it to enhance the expected outcome for better detection of crop disease in a low-dimensional RGB color image.

TL;DR: In this article , a combination of temperature, pH, and electrical conductivity sensors was used to predict crop growth primary macronutrient concentration (nitrate, phosphate, and potassium) in real-time using physical limnological sensors.

TL;DR: In this paper , a comparison of deep learning and transfer learning methods for agricultural disease image recognition is made, which reveals that current agricultural disease data resources make transfer learning the better option.

TL;DR: In this paper , a novel navigation line extraction algorithm based on semantic segmentation is proposed, considering a wheat field as the typical scenario, and the data containing horizontal parallax, height, and grayscale information (HHG) is constructed by combining reencoded depth data and red-green-blue (RGB) data.

TL;DR: In this paper , a fault diagnosis of tractor auxiliary gearbox is presented, where correlation-based feature selection (CFS) method was used to find the best features. And the overall accuracy of RF classifier without using feature selection were 86.25%, at 600 RPM, while the corresponding values of RF trained with the optimal 6 features by using CFS was 92.5%.

TL;DR: A survey of image processing techniques proposed in the literature for extracting key cereal crop growth metrics from high spatial resolution, typically proximal images is presented in this paper , which reveals limitations in image processing methods for cereal crop monitoring such as lack of robustness to lighting conditions, camera position, and self-obstruction.

TL;DR: In this paper , four drying methods, namely, naturally-open sun drying as the control check (CK), hot air drying (HAD), pulsed vacuum drying (PVD), and radio frequency combined hot air (RF-HAD) were employed to dry peanut pods, and their effects on the nutritional quality attributes in terms of protein, fat, fatty acid contents, etc.

TL;DR: In this paper , a multi-sensor data fusion technique based on a sparse representation is introduced to find the most straightforward and complete linear equation to predict and understand a particular variable behavior based on other monitored environmental variables measurements.

TL;DR: In this article , a unified ontology model, AquaONT, is proposed to represent and store the essential knowledge of an aquaponics 4.0 system in the form of an ontology and validated and implemented by considering experimental test cases related to environmental parameters, design configuration and product quality.

TL;DR: In this paper , a segmentation method based on the combination of semantic segmentation and K-means algorithms for the segmentation of crops and weeds in color images was proposed, which provided more accurate segmentation in comparison to other methods.

TL;DR: In this paper , a methodology to estimate coffee prices based on the use of Extreme Learning Machines (ELMs) is introduced, which is a novelty that contributes to the coffee prices forecasting field.

TL;DR: In this article , the authors investigated Deep Learning (DL) models, namely, Long Short Term Memory (LSTM) and Bidirectional LSTM (BLSTM), appropriate for sequential data, from imbalanced data.

TL;DR: In this paper , a computer vision system for the early detection of anthracnose in sugar mango based on Ultraviolet A illumination (UV-A) is presented, which is commonly found in the fruit of sugar mango (Mangifera indica).

TL;DR: In this paper , the application prospects of machine vision in plant factories were analyzed, and the present researches were summarized from the fields of plant growth monitoring, robot operation assistance, and fruit grading.

TL;DR: In this paper , a machine vision-based system equipped with an intelligent modelling approach for in-line sorting of bell peppers into desirable and undesirable samples, with the ability to predict the maturity level and the size of the desirable bell peppers.

TL;DR: In this paper , a perforated pooled circular stepped cascade (PPCSC) aerator was developed, and the geometric and dynamic parameters of the developed aerator were optimized using the hybrid ANN-PSO technique for maximizing its aeration efficiency.

TL;DR: In this paper , the authors used both gene expression programming (GEP) and artificial neural network (ANN) techniques to model reference evapotranspiration (ET0) using the daily meteorological data of the Pantnagar region, India, from 2010 to 2019.

TL;DR: In this article , an ensemble of region-based deep neural networks is used for classifying Tuna species based on their images, and different ensemble methods are subsequently used to combine the three CNN-G2DLBP models.

TL;DR: MobileNetV1 Bottleneck with Expansion (MB-BE) as discussed by the authors was proposed to learn features of the freshness of fish eyes with a Bottleneck Multiplier and Residual Transition to bridge current feature maps and skip connection feature maps to the next convolution block.

TL;DR: Wang et al. as discussed by the authors used Mask R-CNN for cage segmentation and density statistics, which can significantly improve segmentation precision and model's robustness compared with traditional contour extraction method and U-Net based scheme.

TL;DR: In this article , a method of optimizing BP neural network using polynomial decay learning rate was proposed to overcome the shortcomings of traditional dairy cow feed intake assessment model and BP neural networks, and the model is trained and verified by experimental data collected on site.