Artificial Intelligence in Agriculture最新文献_第10页

Low-cost livestock sorting information management system based on deep learning 基于深度学习的低成本牲畜分拣信息管理系统

Artificial Intelligence in Agriculture Pub Date : 2023-09-01 DOI: 10.1016/j.aiia.2023.08.007

Yuanzhi Pan , Yuzhen Zhang , Xiaoping Wang , Xiang Xiang Gao , Zhongyu Hou

{"title":"Low-cost livestock sorting information management system based on deep learning","authors":"Yuanzhi Pan , Yuzhen Zhang , Xiaoping Wang , Xiang Xiang Gao , Zhongyu Hou","doi":"10.1016/j.aiia.2023.08.007","DOIUrl":"10.1016/j.aiia.2023.08.007","url":null,"abstract":"<div><p>Modern pig farming leaves much to be desired in terms of efficiency, as these systems rely mainly on electromechanical controls and can only categorize pigs according to their weight. This method is not only inefficient but also escalates labor expenses and heightens the threat of zoonotic diseases. Furthermore, confining pigs in large groups can exacerbate the spread of infections and complicate the monitoring and care of ill pigs. This research executed an experiment to construct a deep-learning sorting mechanism, leveraging a dataset infused with pivotal metrics and breeding imagery gathered over 24 months. This research integrated a Kalman filter-based algorithm to augment the precision of the dynamic sorting operation. This research experiment unveiled a pioneering machine vision sorting system powered by deep learning, adept at handling live imagery for multifaceted recognition objectives. The Individual recognition model based on Residual Neural Network (ResNet) monitors livestock weight for sustained data forecasting, whereas the Wasserstein Generative Adversarial Nets (WGAN) image enhancement algorithm bolsters recognition in distinct settings, fortifying the model's resilience. Notably, system can pinpoint livestock exhibiting signs of potential illness via irregular body appearances and isolate them for safety. Experimental outcomes validate the superiority of this proposed system over traditional counterparts. It not only minimizes manual interventions and data upkeep expenses but also heightens the accuracy of livestock identification and optimizes data usage. This findings reflect an 89% success rate in livestock ID recognition, a 32% surge in obscured image recognition, a 95% leap in livestock categorization accuracy, and a remarkable 98% success rate in discerning images of unwell pigs. In essence, this research augments identification efficiency, curtails operational expenses, and provides enhanced tools for disease monitoring.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"9 ","pages":"Pages 110-126"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48035348","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

Corn kernel classification from few training samples 基于少量训练样本的玉米籽粒分类

Artificial Intelligence in Agriculture Pub Date : 2023-09-01 DOI: 10.1016/j.aiia.2023.08.006

Patricia L. Suárez , Henry O. Velesaca , Dario Carpio , Angel D. Sappa

{"title":"Corn kernel classification from few training samples","authors":"Patricia L. Suárez , Henry O. Velesaca , Dario Carpio , Angel D. Sappa","doi":"10.1016/j.aiia.2023.08.006","DOIUrl":"10.1016/j.aiia.2023.08.006","url":null,"abstract":"<div><p>This article presents an efficient approach to classify a set of corn kernels in contact, which may contain good, or defective kernels along with impurities. The proposed approach consists of two stages, the first one is a next-generation segmentation network, trained by using a set of synthesized images that is applied to divide the given image into a set of individual instances. An ad-hoc lightweight CNN architecture is then proposed to classify each instance into one of three categories (ie good, defective, and impurities). The segmentation network is trained using a strategy that avoids the time-consuming and human-error-prone task of manual data annotation. Regarding the classification stage, the proposed ad-hoc network is designed with only a few sets of layers to result in a lightweight architecture capable of being used in integrated solutions. Experimental results and comparisons with previous approaches showing both the improvement in accuracy and the reduction in time are provided. Finally, the segmentation and classification approach proposed can be easily adapted for use with other cereal types.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"9 ","pages":"Pages 89-99"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43946230","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

Estimation of morphological traits of foliage and effective plant spacing in NFT-based aquaponics system 基于NFT的水培系统中叶片形态特征和有效株距的估计

Artificial Intelligence in Agriculture Pub Date : 2023-09-01 DOI: 10.1016/j.aiia.2023.08.004

R. Abbasi , P. Martinez , R. Ahmad

{"title":"Estimation of morphological traits of foliage and effective plant spacing in NFT-based aquaponics system","authors":"R. Abbasi , P. Martinez , R. Ahmad","doi":"10.1016/j.aiia.2023.08.004","DOIUrl":"10.1016/j.aiia.2023.08.004","url":null,"abstract":"<div><p>Deep learning and computer vision techniques have gained significant attention in the agriculture sector due to their non-destructive and contactless features. These techniques are also being integrated into modern farming systems, such as aquaponics, to address the challenges hindering its commercialization and large-scale implementation. Aquaponics is a farming technology that combines a recirculating aquaculture system and soilless hydroponics agriculture, that promises to address food security issues. To complement the current research efforts, a methodology is proposed to automatically measure the morphological traits of crops such as width, length and area and estimate the effective plant spacing between grow channels. Plant spacing is one of the key design parameters that are dependent on crop type and its morphological traits and hence needs to be monitored to ensure high crop yield and quality which can be impacted due to foliage occlusion or overlapping as the crop grows. The proposed approach uses Mask-RCNN to estimate the size of the crops and a mathematical model to determine plant spacing for a self-adaptive aquaponics farm. For common little <em>gem</em> romaine lettuce, the growth is estimated within 2 cm of error for both length and width. The final model is deployed on a cloud-based application and integrated with an ontology model containing domain knowledge of the aquaponics system. The relevant knowledge about crop characteristics and optimal plant spacing is extracted from ontology and compared with results obtained from the final model to suggest further actions. The proposed application finds its significance as a decision support system that can pave the way for intelligent system monitoring and control.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"9 ","pages":"Pages 76-88"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45096541","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

Detecting broiler chickens on litter floor with the YOLOv5-CBAM deep learning model 基于YOLOv5-CBAM深度学习模型的窝地板肉鸡检测

Artificial Intelligence in Agriculture Pub Date : 2023-09-01 DOI: 10.1016/j.aiia.2023.08.002

Yangyang Guo , Samuel E. Aggrey , Xiao Yang , Adelumola Oladeinde , Yongliang Qiao , Lilong Chai

{"title":"Detecting broiler chickens on litter floor with the YOLOv5-CBAM deep learning model","authors":"Yangyang Guo , Samuel E. Aggrey , Xiao Yang , Adelumola Oladeinde , Yongliang Qiao , Lilong Chai","doi":"10.1016/j.aiia.2023.08.002","DOIUrl":"10.1016/j.aiia.2023.08.002","url":null,"abstract":"<div><p>For commercial broiler production, about 20,000–30,000 birds are raised in each confined house, which has caused growing public concerns on animal welfare. Currently, daily evaluation of broiler wellbeing and growth is conducted manually, which is labor-intensive and subjectively subject to human error. Therefore, there is a need for an automatic tool to detect and analyze the behaviors of chickens and predict their welfare status. In this study, we developed a YOLOv5-CBAM-broiler model and tested its performance for detecting broilers on litter floor. The proposed model consisted of two parts: (1) basic YOLOv5 model for bird or broiler feature extraction and object detection; and (2) the convolutional block attention module (CBAM) to improve the feature extraction capability of the network and the problem of missed detection of occluded targets and small targets. A complex dataset of broiler chicken images at different ages, multiple pens and scenes (fresh litter versus reused litter) was constructed to evaluate the effectiveness of the new model. In addition, the model was compared to the Faster R-CNN, SSD, YOLOv3, EfficientDet and YOLOv5 models. The results demonstrate that the precision, recall, F1 score and an [email protected] of the proposed method were 97.3%, 92.3%, 94.7%, and 96.5%, which were superior to the comparison models. In addition, comparing the detection effects in different scenes, the YOLOv5-CBAM model was still better than the comparison method. Overall, the proposed YOLOv5-CBAM-broiler model can achieve real-time accurate and fast target detection and provide technical support for the management and monitoring of birds in commercial broiler houses.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"9 ","pages":"Pages 36-45"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46430631","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

Machine learning in nutrient management: A review 机器学习在营养管理中的应用综述

Artificial Intelligence in Agriculture Pub Date : 2023-09-01 DOI: 10.1016/j.aiia.2023.06.001

Oumnia Ennaji , Leonardus Vergütz , Achraf El Allali

引用次数: 0

CactiViT: Image-based smartphone application and transformer network for diagnosis of cactus cochineal CactiViT:基于图像的智能手机应用和变压器网络，用于仙人掌胭脂虫的诊断

Artificial Intelligence in Agriculture Pub Date : 2023-09-01 DOI: 10.1016/j.aiia.2023.07.002

Anas Berka , Adel Hafiane , Youssef Es-Saady , Mohamed El Hajji , Raphaël Canals , Rachid Bouharroud

{"title":"CactiViT: Image-based smartphone application and transformer network for diagnosis of cactus cochineal","authors":"Anas Berka , Adel Hafiane , Youssef Es-Saady , Mohamed El Hajji , Raphaël Canals , Rachid Bouharroud","doi":"10.1016/j.aiia.2023.07.002","DOIUrl":"10.1016/j.aiia.2023.07.002","url":null,"abstract":"<div><p>The cactus is a plant that grows in many rural areas, widely used as a hedge, and has multiple benefits through the manufacture of various cosmetics and other products. However, this crop has been suffering for some time from the attack of the carmine scale <em>Dactylopius opuntia</em> (Hemiptera: Dactylopiidae). The infestation can spread rapidly if not treated in the early stage. Current solutions consist of regular field checks by the naked eyes carried out by experts. The major difficulty is the lack of experts to check all fields, especially in remote areas. In addition, this requires time and resources. Hence the need for a system that can categorize the health level of cacti remotely. To date, deep learning models used to categorize plant diseases from images have not addressed the mealy bug infestation of cacti because computer vision has not sufficiently addressed this disease. Since there is no public dataset and smartphones are commonly used as tools to take pictures, it might then be conceivable for farmers to use them to categorize the infection level of their crops. In this work, we developed a system called CactiVIT that instantly determines the health status of cacti using the Visual image Transformer (ViT) model. We also provided a new image dataset of cochineal infested cacti.<span><sup>1</sup></span> Finally, we developed a mobile application that delivers the classification results directly to farmers about the infestation in their fields by showing the probabilities related to each class. This study compares the existing models on the new dataset and presents the results obtained. The VIT-B-16 model reveals an approved performance in the literature and in our experiments, in which it achieved 88.73% overall accuracy with an average of +2.61% compared to other convolutional neural network (CNN) models that we evaluated under similar conditions.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"9 ","pages":"Pages 12-21"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43334508","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

Rice disease identification method based on improved CNN-BiGRU 基于改进CNN-BiGRU的水稻病害识别方法

Artificial Intelligence in Agriculture Pub Date : 2023-09-01 DOI: 10.1016/j.aiia.2023.08.005

Yang Lu , Xiaoxiao Wu , Pengfei Liu , Hang Li , Wanting Liu

引用次数: 0

Lightweight convolutional neural network models for semantic segmentation of in-field cotton bolls 田间棉铃语义分割的轻量级卷积神经网络模型

Artificial Intelligence in Agriculture Pub Date : 2023-06-01 DOI: 10.1016/j.aiia.2023.03.001

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

{"title":"Lightweight convolutional neural network models for semantic segmentation of in-field cotton bolls","authors":"Naseeb Singh , V.K. Tewari , P.K. Biswas , L.K. Dhruw","doi":"10.1016/j.aiia.2023.03.001","DOIUrl":"https://doi.org/10.1016/j.aiia.2023.03.001","url":null,"abstract":"<div><p>Robotic harvesting of cotton bolls will incorporate the benefits of manual picking as well as mechanical harvesting. For robotic harvesting, in-field cotton segmentation with minimal errors is desirable which is a challenging task. In the present study, three lightweight fully convolutional neural network models were developed for the semantic segmentation of in-field cotton bolls. Model 1 does not include any residual or skip connections, while model 2 consists of residual connections to tackle the vanishing gradient problem and skip connections for feature concatenation. Model 3 along with residual and skip connections, consists of filters of multiple sizes. The effects of filter size and the dropout rate were studied. All proposed models segment the cotton bolls successfully with the cotton-IoU (intersection-over-union) value of above 88.0%. The highest cotton-IoU of 91.03% was achieved by model 2. The proposed models achieved F1-score and pixel accuracy values greater than 95.0% and 98.0%, respectively. The developed models were compared with existing state-of-the-art networks namely VGG19, ResNet18, EfficientNet-B1, and InceptionV3. Despite having a limited number of trainable parameters, the proposed models achieved mean-IoU (mean intersection-over-union) of 93.84%, 94.15%, and 94.65% against the mean-IoU values of 95.39%, 96.54%, 96.40%, and 96.37% obtained using state-of-the-art networks. The segmentation time for the developed models was reduced up to 52.0% compared to state-of-the-art networks. The developed lightweight models segmented the in-field cotton bolls comparatively faster and with greater accuracy. Hence, developed models can be deployed to cotton harvesting robots for real-time recognition of in-field cotton bolls for harvesting.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"8 ","pages":"Pages 1-19"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50193228","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

Feature aggregation for nutrient deficiency identification in chili based on machine learning 基于机器学习的辣椒营养缺乏识别特征聚合

Artificial Intelligence in Agriculture Pub Date : 2023-06-01 DOI: 10.1016/j.aiia.2023.04.001

Deffa Rahadiyan , Sri Hartati , Wahyono , Andri Prima Nugroho

{"title":"Feature aggregation for nutrient deficiency identification in chili based on machine learning","authors":"Deffa Rahadiyan , Sri Hartati , Wahyono , Andri Prima Nugroho","doi":"10.1016/j.aiia.2023.04.001","DOIUrl":"10.1016/j.aiia.2023.04.001","url":null,"abstract":"<div><p>Macronutrient deficiency inhibits the growth and development of chili plants. One of the non-destructive methods that plays a role in processing plant image data based on specific characteristics is computer vision. This study uses 5166 image data after augmentation process for six plant health conditions. But the analysis of one feature cannot represent plant health condition. Therefore, a careful combination of features is required. This study combines three types of features with HSV and RGB for color, GLCM and LBP for texture, and Hu moments and centroid distance for shapes. Each feature and its combination are trained and tested using the same MLP architecture. The combination of RGB, GLCM, Hu moments, and Distance of centroid features results the best performance. In addition, this study compares the MLP architecture used with previous studies such as SVM, Random Forest Technique, Naive Bayes, and CNN. CNN produced the best performance, followed by SVM and MLP, with accuracy reaching 97.76%, 90.55% and 89.70%, respectively. Although MLP has lower accuracy than CNN, the model for identifying plant health conditions has a reasonably good success rate to be applied in a simple agricultural environment.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"8 ","pages":"Pages 77-90"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43991546","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

GxENet: Novel fully connected neural network based approaches to incorporate GxE for predicting wheat yield GxENet:基于全连接神经网络的小麦产量预测方法

Artificial Intelligence in Agriculture Pub Date : 2023-06-01 DOI: 10.1016/j.aiia.2023.05.001

Sheikh Jubair , Olivier Tremblay-Savard , Mike Domaratzki

{"title":"GxENet: Novel fully connected neural network based approaches to incorporate GxE for predicting wheat yield","authors":"Sheikh Jubair , Olivier Tremblay-Savard , Mike Domaratzki","doi":"10.1016/j.aiia.2023.05.001","DOIUrl":"10.1016/j.aiia.2023.05.001","url":null,"abstract":"<div><p>The expression of quantitative traits of a line of a crop depends on its genetics, the environment where it is sown and the interaction between the genetic information and the environment known as GxE. Thus to maximize food production, new varieties are developed by selecting superior lines of seeds suitable for a specific environment. Genomic selection is a computational technique for developing a new variety that uses whole genome molecular markers to identify top lines of a crop. A large number of statistical and machine learning models are employed for single environment trials, where it is assumed that the environment does not have any effect on the quantitative traits. However, it is essential to consider both genomic and environmental data to develop a new variety, as these strong assumptions may lead to failing to select top lines for an environment. Here we devised three novel deep learning frameworks incorporating GxE within the deep learning model and predicted line-specific yield for an environment. In the process, we also developed a new technique for identifying environment-specific markers that can be useful in many applications of environment-specific genomic selection. The result demonstrates that our best framework obtains 1.75 to 1.95 times better correlation coefficients than other deep learning models that incorporate environmental data depending on the test scenario. Furthermore, the feature importance analysis shows that environmental information, followed by genomic information, is the driving factor in predicting environment-specific yield for a line. We also demonstrate a way to extend our framework for new data types, such as text or soil data. The extended model also shows the potential to be useful in genomic selection.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"8 ","pages":"Pages 60-76"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47674501","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