Artificial Intelligence in Agriculture最新文献_第9页

Fruit ripeness classification: A survey 水果成熟度分类调查

Artificial Intelligence in Agriculture Pub Date : 2023-03-01 DOI: 10.1016/j.aiia.2023.02.004

Matteo Rizzo , Matteo Marcuzzo , Alessandro Zangari , Andrea Gasparetto , Andrea Albarelli

引用次数: 0

Lightweight convolutional neural network models for semantic segmentation of in-field cotton bolls 基于轻量级卷积神经网络模型的大田棉铃语义分割

Artificial Intelligence in Agriculture Pub Date : 2023-03-01 DOI: 10.1016/j.aiia.2023.03.001

Naseeb Singh, V. Tewari, P. Biswas, L. Dhruw

引用次数: 1

Deep learning for the detection of semantic features in tree X-ray CT scans 基于深度学习的树状x射线CT扫描语义特征检测

Artificial Intelligence in Agriculture Pub Date : 2023-03-01 DOI: 10.1016/j.aiia.2022.12.001

Salim Khazem , Antoine Richard , Jeremy Fix , Cédric Pradalier

{"title":"Deep learning for the detection of semantic features in tree X-ray CT scans","authors":"Salim Khazem , Antoine Richard , Jeremy Fix , Cédric Pradalier","doi":"10.1016/j.aiia.2022.12.001","DOIUrl":"10.1016/j.aiia.2022.12.001","url":null,"abstract":"<div><p>According to the industry, the value of wood logs is heavily influenced by their internal structure, particularly the distribution of knots within the trees. Nowadays, CT scanners combined with classical computer vision approach are the most common tool for obtaining reliable and accurate images of the interior structure of trees. Knowing where the tree semantic features, especially knots, contours and centers are within a tree could improve the efficiency of the overall tree industry by minimizing waste and enhancing the quality of wood-log by-products. However, this requires to automatically process the CT-scanner images so as to extract the different elements such as tree centerline, knot localization and log contour, in a robust and efficient manner. In this paper, we propose an effective methodology based on deep learning for performing these different tasks by processing CT-scanner images with deep convolutional neural networks. To meet this objective, three end-to-end trainable pipelines are proposed. The first pipeline is focused on centers detection using CNNs architecture with a regression head, the second and the third one address contour estimation and knot detection as a binary segmentation task based on an Encoder-Decoder architecture. The different architectures are tested on several tree species. With these experiments, we demonstrate that our approaches can be used to extract the different elements of trees in a precise manner while preserving good performances of robustness. The main objective was to demonstrate that methods based on deep learning might be used and have a relevant potential for segmentation and regression on CT-scans of tree trunks.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"7 ","pages":"Pages 13-26"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41754236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Improving the non-destructive maturity classification model for durian fruit using near-infrared spectroscopy 利用近红外光谱技术改进榴莲果实成熟度无损分类模型

Artificial Intelligence in Agriculture Pub Date : 2023-03-01 DOI: 10.1016/j.aiia.2023.02.002

Sirirak Ditcharoen , Panmanas Sirisomboon , Khwantri Saengprachatanarug , Arthit Phuphaphud , Ronnarit Rittiron , Anupun Terdwongworakul , Chayuttapong Malai , Chirawan Saenphon , Lalita Panduangnate , Jetsada Posom

{"title":"Improving the non-destructive maturity classification model for durian fruit using near-infrared spectroscopy","authors":"Sirirak Ditcharoen , Panmanas Sirisomboon , Khwantri Saengprachatanarug , Arthit Phuphaphud , Ronnarit Rittiron , Anupun Terdwongworakul , Chayuttapong Malai , Chirawan Saenphon , Lalita Panduangnate , Jetsada Posom","doi":"10.1016/j.aiia.2023.02.002","DOIUrl":"https://doi.org/10.1016/j.aiia.2023.02.002","url":null,"abstract":"<div><p>The maturity state of durian fruit is a key indicator of quality before trading. This research aims to improve the near-infrared (NIR) model for classifying the maturity stage of durian fruit using a completely non-destructive measurement. Both NIR spectrometers were investigated: the short wavelength NIR (SWNIR) ranging from 450 to 1000 nm and long wavelength NIR (LWNIR) ranging from 860 to 1750 nm. The samples collected for experimentation consisted of four stages: immaturity, prematurity, maturity, and ripe. Each fruit was scanned at the rind position on the main fertile lobe (header, middle, and tail) and stem. The classification models were developed using three supervised machine learning algorithms: linear discriminant analysis (LDA), support vector machine (SVM), and K-Nearest neighbours (KNN). The analysis results revealed that the use of durian rind spectra only obtained between 83.15% and 88.04% accuracy for the LWNIR spectrometer, while the SWNIR spectrometer provided 64.73 to 93.77% accuracy. The performance of model increases when developing with combination between rind and stem spectra. The LDA model developed using a combination of rind and stem spectra provided the greatest efficiency, exhibiting 97.28% and 100% accuracy for LWNIR and SWNIR spectrometers, respectively. The LDA model is therefore recommended for obtaining spectra from smoothing moving average (MA) + baseline of rind position and when used in combination with the MA + standard normal variance (SNV) of stem spectra. The NIR spectroscopy indicated high potential for non-destructive estimation of the durian maturity stage. This process could be used for quality control in the durian export industry to solve the problem of unripe durian being mixed with ripe fruit.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"7 ","pages":"Pages 35-43"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50189221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A fuzzy risk assessment model used for assessing the introduction of African swine fever into Australia from overseas 用于评估非洲猪瘟从海外传入澳大利亚的模糊风险评估模型

Artificial Intelligence in Agriculture Pub Date : 2023-03-01 DOI: 10.1016/j.aiia.2023.02.001

Hongkun Liu , YongLin Ren , Huanhuan Chu , Hu Shan , Kok Wai Wong

{"title":"A fuzzy risk assessment model used for assessing the introduction of African swine fever into Australia from overseas","authors":"Hongkun Liu , YongLin Ren , Huanhuan Chu , Hu Shan , Kok Wai Wong","doi":"10.1016/j.aiia.2023.02.001","DOIUrl":"10.1016/j.aiia.2023.02.001","url":null,"abstract":"<div><p>African swine fever (ASF) is a contagious and lethal hemorrhagic disease with a high case fatality rate. Since 2007, ASF has been spreading into many countries, especially in Europe and Asia. Given that there is no effective vaccine and treatment to deal with ASF, prevention is an important way for a country to avoid the effects of the virus. Australia is currently ASF-free but the disease has been reported in many neighboring countries, such as Indonesia, Timor-Leste, and Papua New Guinea. Therefore, it is necessary for Australia to maintain hyper-vigilance to prevent the ASF introduction. In this paper, we propose the use of fuzzy concepts to establish a fuzzy risk assessment model to predict the ASF introduction risk in Australia. From the analysis, the international passengers (IP) and international import trade (IIT) are concluded as the two main ASF introduction factors based on transmission features and past research. From the established fuzzy risk assessment model based on the analysis of the 2019 and 2020 data, the risks of ASF introduction into Australia are considered to be low. The model further deduced that the Asian region was the major source of potential risks. Finally, in order to validate the effectiveness of the established fuzzy risk assessment model, the qualitative data from the Department for Environment, Food & Rural Affairs of the United Kingdom was used. From the validation results, it has shown that the results were consistent when the same data is adopted, and thus proved that the functionality of the established fuzzy risk assessment model for assessing the risk in Australia.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"7 ","pages":"Pages 27-34"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43993437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

t-SNE: A study on reducing the dimensionality of hyperspectral data for the regression problem of estimating oenological parameters t-SNE:葡萄酒参数估计回归问题的高光谱数据降维研究

Artificial Intelligence in Agriculture Pub Date : 2023-03-01 DOI: 10.1016/j.aiia.2023.02.003

Rui Silva , Pedro Melo-Pinto

{"title":"t-SNE: A study on reducing the dimensionality of hyperspectral data for the regression problem of estimating oenological parameters","authors":"Rui Silva , Pedro Melo-Pinto","doi":"10.1016/j.aiia.2023.02.003","DOIUrl":"10.1016/j.aiia.2023.02.003","url":null,"abstract":"<div><p>In recent years there is a growing importance in using machine learning techniques to improve procedures in precision agriculture: in this work we perform a study on models capable of predicting oenological parameters from hyperspectral images of wine grape berries, a specially relevant topic to boost production tasks for winemakers. Specifically, we explore the capabilities of a novel technique mostly used for visualization, t-Distributed Stochastic Neighbor Embedding (t-SNE), for reducing the dimensionality of the highly complex hyperspectral data and compare its performance with Principal Component Analysis (PCA) method, which despite the introduction of many nonlinear dimensionality reduction techniques over the years, had achieved the best results for real-world data across several studies in literature. Additionally we explore the potential of Kernel t-SNE, an extension to the t-SNE method that allows for the usage of the technique in streaming data or online scenarios. Our results show that, in a direct comparison, t-SNE achieves better metrics than PCA for most of the data sets in this work and that the regressor (Support Vector Regression, SVR) performs better with the t-SNE reduced features as inputs, accomplishing better predictions with lower error rates. Comparing the results with current literature, our shallow learning model paired with t-SNE achieves either better or on par results than those reported, even competing with more advanced models that use deep learning techniques, which should propel the introduction of t-SNE in more studies that require dimensionality reduction.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"7 ","pages":"Pages 58-68"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41572754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Deep learning models for automatic identification of plant-parasitic nematode 植物寄生线虫自动识别的深度学习模型

Artificial Intelligence in Agriculture Pub Date : 2023-03-01 DOI: 10.1016/j.aiia.2022.12.002

Nabila Husna Shabrina , Ryukin Aranta Lika , Siwi Indarti

{"title":"Deep learning models for automatic identification of plant-parasitic nematode","authors":"Nabila Husna Shabrina , Ryukin Aranta Lika , Siwi Indarti","doi":"10.1016/j.aiia.2022.12.002","DOIUrl":"10.1016/j.aiia.2022.12.002","url":null,"abstract":"<div><p>Plant-parasitic nematodes cause various diseases that can be fatal to the infected plants. It causes losses to the agricultural industry, such as crop failure and poor crop quality. Developing an accurate nematode classification system is vital for pest identification and control. Deep learning classification techniques can help speed up Nematode identification as it can perform tasks directly from images. In the present study, four state-of-the-art deep learning models (ResNet101v2, CoAtNet-0, Effi- cientNetV2B0, and EfficientNetV2M) were evaluated in plant-parasitic nematode classification from microscopic image. The models were trained using a combination of three different optimizers (Adam, SGD, dan RMSProp) and several data augmentation with image transformations, such as image flip, blurring, noise addition, brightness, and contrast adjustment. The performance of the trained models was varied. Regarding test accuracy, EfficientNetV2B0 and EfficientNetV2M using RMSProp and brightness augmentation give the best result of 97.94% However, the overall performance of EfficientNetV2M was superior, with 98.66% mean class accuracy, 97.99%F1 score, 98.26% average precision, and 97.94% average recall.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"7 ","pages":"Pages 1-12"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45799752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Review of agricultural IoT technology 农业物联网技术综述

Artificial Intelligence in Agriculture Pub Date : 2022-01-01 DOI: 10.1016/j.aiia.2022.01.001

Jinyuan Xu , Baoxing Gu , Guangzhao Tian

引用次数: 70

Effect and economic benefit of precision seeding and laser land leveling for winter wheat in the middle of China 中部地区冬小麦精准播种激光整地效果及经济效益分析

Artificial Intelligence in Agriculture Pub Date : 2022-01-01 DOI: 10.1016/j.aiia.2021.11.003

Jing Chen , Chunjiang Zhao , Glyn Jones , Hao Yang , Zhenhong Li , Guijun Yang , Liping Chen , Yongchang Wu

{"title":"Effect and economic benefit of precision seeding and laser land leveling for winter wheat in the middle of China","authors":"Jing Chen , Chunjiang Zhao , Glyn Jones , Hao Yang , Zhenhong Li , Guijun Yang , Liping Chen , Yongchang Wu","doi":"10.1016/j.aiia.2021.11.003","DOIUrl":"10.1016/j.aiia.2021.11.003","url":null,"abstract":"<div><p>Rapid socio-economic changes in China, such as land conversion and urbanization, are creating new scopes for the application of precision agriculture (PA).An experiment to assess the economic benefits of two precision agriculture methods was applied for one year – precision seeding and precision seeding with land leveling. Whilst the results for this were positive, of itself it did not provide evidence of longer terms gains. The costs of land leveling are accrued in a single year but the benefits could carry over into subsequent years. Thus, in this case if the PA method provides carry over benefits to future years, the economic assessment would incorrectly assign all the costs to a single year of benefits i.e.the benefit-cost ratio would be underestimated. To gauge whether there was carry over benefits in future years we looked at NDVI and GUI as proxies for future year benefits. For the single year experiment, our results showed that: (1) Winter wheat yield was increased 23.2% through the integration of precision seeding and laser leveling technologies.(2) Both the single technology and the integrated technologies significant reduced the concentration of soil ammonium nitrogen at the depths of 60 cm; (3) The benefit/cost ratio's of the treatments exceeded that of the baseline by approximately 10% which translated to an increase of several hundred US$ per hectare. The NDVI analysis showed that the effect of laser land leveling could last to the next two years. When considering the multi-year impact of land leveling, the benefit/cost ratio of PSLL will increase to 23.5% and 22.9% with and without laser land leveling subsidies. Making clear the economic benefits of using PA technologies will likely promote application of the technologies in the region.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 1-9"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721721000349/pdfft?md5=0c5c4f99c9af286d04dc0a8a2a5766e3&pid=1-s2.0-S2589721721000349-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46124768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Nutrient optimization for plant growth in Aquaponic irrigation using Machine Learning for small training datasets 利用机器学习对小型训练数据集进行水培灌溉中植物生长的养分优化

Artificial Intelligence in Agriculture Pub Date : 2022-01-01 DOI: 10.1016/j.aiia.2022.05.001

Sambandh Bhusan Dhal , Muthukumar Bagavathiannan , Ulisses Braga-Neto , Stavros Kalafatis

{"title":"Nutrient optimization for plant growth in Aquaponic irrigation using Machine Learning for small training datasets","authors":"Sambandh Bhusan Dhal , Muthukumar Bagavathiannan , Ulisses Braga-Neto , Stavros Kalafatis","doi":"10.1016/j.aiia.2022.05.001","DOIUrl":"https://doi.org/10.1016/j.aiia.2022.05.001","url":null,"abstract":"<div><p>With the recent trends in urban agriculture and climate change, there is an emerging need for alternative plant culture techniques where dependence on soil can be eliminated. Hydroponic and aquaponic growth techniques have proven to be viable alternatives, but the lack of efficient and optimal practices for irrigation and nutrient supply limits its applications on a large-scale commercial basis. The main purpose of this research was to develop statistical methods and Machine Learning algorithms to regulate nutrient concentrations in aquaponic irrigation water based on plant needs, for achieving optimal plant growth and promoting broader adoption of aquaponic culture on a commercial scale. One of the key challenges to developing these algorithms is the sparsity of data which requires the use of Bolstered error estimation approaches. In this paper, several linear and non-linear algorithms trained on relatively small datasets using Bolstered error estimation techniques were evaluated, for selecting the best method in making decisions regarding the regulation of nutrients in hydroponic environments. After repeated tests on the dataset, it was decided that Semi-Bolstered Resubstitution Error estimation technique works best in our case using Linear Support Vector Machine as the classifier with the value of penalty parameter set to one. A set of recommended rules have been prescribed as a Decision Support System, using the output of the Machine Learning algorithm, which have been tested against the results of the baseline model. Further, the positive impact of the recommended nutrient concentrationson plant growth in aquaponic environments has been elaborately discussed.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 68-76"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721722000058/pdfft?md5=88b4d1d7657cf1e51d82120cbd2cc4e8&pid=1-s2.0-S2589721722000058-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91954151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0