Artificial Intelligence in Agriculture最新文献

{"title":"A novel elemental composition based prediction model for biochar aromaticity derived from machine learning","authors":"Hongliang Cao ,&nbsp;Yaime Jefferson Milan ,&nbsp;Sohrab Haghighi Mood ,&nbsp;Michael Ayiania ,&nbsp;Shu Zhang ,&nbsp;Xuzhong Gong ,&nbsp;Electo Eduardo Silva Lora ,&nbsp;Qiaoxia Yuan ,&nbsp;Manuel Garcia-Perez","doi":"10.1016/j.aiia.2021.06.002","DOIUrl":"10.1016/j.aiia.2021.06.002","url":null,"abstract":"<div><p>The measurement of aromaticity in biochars is generally conducted using solid state ¹³C nuclear magnetic resonance spectroscopy, which is expensive, time-consuming, and only accessible in a small number of research-intensive universities. Mathematical modelling could be a viable alternative to predict biochar aromaticity from other much easier accessible parameters (e.g. elemental composition). In this research, Genetic Programming (GP), an advanced machine learning method, is used to develop new prediction models. In order to identify and evaluate the performance of prediction models, an experimental data set with 98 biochar samples collected from the literature was utilized. Due to the benefits of the intelligence iteration and learning of GP algorithm, a kind of underlying exponential relationship between the elemental compositions and the aromaticity of biochars is disclosed clearly. The exponential relationship is clearer and simpler than the polynomial mapping relationships implicated by Maroto-Valer, Mazumdar, and Mazumdar-Wang models. In this case, a novel exponential model is proposed for the prediction of biochar aromaticity. The proposed exponential model appears better prediction accuracy and generalization ability than existing polynomial models during the statistical parameter evaluation.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiia.2021.06.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54191073","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}

{"title":"Reducing deep learning network structure through variable reduction methods in crop modeling","authors":"Babak Saravi ,&nbsp;A. Pouyan Nejadhashemi ,&nbsp;Prakash Jha ,&nbsp;Bo Tang","doi":"10.1016/j.aiia.2021.09.001","DOIUrl":"10.1016/j.aiia.2021.09.001","url":null,"abstract":"<div><p>Crop models are widely used to predict plant growth, water input requirements, and yield. However, existing models are very complex and require hundreds of variables to perform accurately. Due to these shortcomings, large-scale applications of crop models are limited. In order to address these limitations, reliable crop models were developed using a deep neural network (DNN) – a new approach for predicting crop yields. In addition, the number of required input variables was reduced using three common variable selection techniques: namely Bayesian variable selection, Spearman's rank correlation, and Principal Component Analysis Feature Extraction. The reduced-variable DNN models were capable of estimating future crop yields for 10,000,000 different weather and irrigation scenarios while maintaining comparable accuracy levels to the original model that used all input variables. To establish clear superiority of the methodology, the results were also compared with a very recent feature selection algorithm called min-redundancy max-relevance (mRMR). The results of this study showed that the Bayesian variable selection was the best method for achieving the aforementioned goals. Specifically, the final Bayesian-based DNN model with a structure of 10 neurons in 5 layers performed very similarly (78.6% accuracy) to the original DNN crop model with 400 neurons in 10 layers, even though the size of the neural network was reduced by 80-fold. This effort can help promote sustainable agricultural intensifications through the large-scale application of crop models.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721721000271/pdfft?md5=33b6742cab8fc2c67973faaac5cf0f20&pid=1-s2.0-S2589721721000271-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54191298","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}

{"title":"Erratum regarding missing Declaration of Competing Interest statements in previously published articles","authors":"","doi":"10.1016/j.aiia.2021.01.001","DOIUrl":"https://doi.org/10.1016/j.aiia.2021.01.001","url":null,"abstract":"","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiia.2021.01.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72247150","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}

{"title":"Development, evaluation, and optimization of an automated device for quality detection and separation of cowpea seeds","authors":"J. Audu ,&nbsp;A.K. Aremu","doi":"10.1016/j.aiia.2021.10.003","DOIUrl":"10.1016/j.aiia.2021.10.003","url":null,"abstract":"<div><p>Automation and Artificial intelligence has been used to solve the world’'s most complex problems. The goal of this study is to develop, evaluate and optimize cowpea seeds quality detection and separating device to meet international export standards. The design of the device was divided into metering, automation, and conveyor belt outlet unit. An evaluation was done using samples made up of good and bad (impurity) portions. Response surface methodology was used to evaluate, model and optimize the device performance. The optimized results were validated using regression and prediction interval (PI) analysis test. The separating efficiency, throughput, maximum capacity, and actual utilization obtained; range from 68.966 ‐ –94.118%, 0.5 – –3 kg/hr, 6–36 kg/12 h, 0.083–0.083(8.3%) respectively. These evaluating parameters were significantly affected by the operational factors at <em>P</em> &lt; 0.05. Optimum values obtained are 92%, 2.689 kg/h, 32.781 kg/12 h for impurity separating: efficiency, throughput, and maximum capacity respectively. The prediction interval test shows that the validation experimental mean result lies within calculated prediction intervals. Regression analysis shows a 0.9(90%) coefficient of determination between the model predictions and the validation experimental results. The developed device was recommended to always operate at a metering speed of 20 rpm for optimum performance.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721721000301/pdfft?md5=77f9fe6787ea3f49681c4698c1d3bdc7&pid=1-s2.0-S2589721721000301-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43473340","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}

{"title":"Identifying associations between epidemiological entities in news data for animal disease surveillance","authors":"Sarah Valentin ,&nbsp;Renaud Lancelot ,&nbsp;Mathieu Roche","doi":"10.1016/j.aiia.2021.07.003","DOIUrl":"10.1016/j.aiia.2021.07.003","url":null,"abstract":"<div><p>Event-based surveillance systems are at the crossroads of human and animal (and plant and ecosystem) health, epidemiology, statistics, and informatics. Thus, their deployment faces many challenges specific to each domain and their intersections, such as relations among automation, artificial intelligence, and expertise. In this context, our work pertins to the extraction of epidemiological events in textual data (i.e. news) by unsupervised methods. We define the event extraction task as detecting pairs of epidemiological entities (e.g. a disease name and location). The quality of the ranked lists of pairs was evaluated using specific ranking evaluation metrics. We used a publicly available annotated corpus of 438 documents (i.e. news articles) related to animal disease events. The statistical approach was able to detect event-related pairs of epidemiological features with a good trade-off between precision and recall. Our results showed that using a window of words outperformed document-based and sentence-based approaches, while reducing the probability of detecting false pairs. Our results indicated that Mutual Information was less adapted than the Dice coefficient for ranking pairs of features in the event extraction framework. We believe that Mutual Information would be more relevant for rare pair detection (i.e. weak signals), but requires higher manual curation to avoid false positive extraction pairs. Moreover, generalising the country-level spatial features enabled better discrimination (i.e. ranking) of relevant disease-location pairs for event extraction.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721721000246/pdfft?md5=15681f12038098304a7352c6b2235917&pid=1-s2.0-S2589721721000246-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54191195","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}

{"title":"Prediction and data mining of burned areas of forest fires: Optimized data matching and mining algorithm provides valuable insight","authors":"David A. Wood","doi":"10.1016/j.aiia.2021.01.004","DOIUrl":"10.1016/j.aiia.2021.01.004","url":null,"abstract":"<div><p>An optimized data-matching machine learning algorithm is developed to provide high-prediction accuracy of total burned areas for specific wildfire incidents. It is applied to a well-studied forest-fire dataset from Portugal Montesinho Natural Park considering 13 input variables. The total burned area distribution of the 517 burn events in that dataset is highly positively skewed. The model is transparent and avoids regressions and hidden layers. This increases its detailed data mining capabilities. It matches the highest burned-area prediction accuracy achieved for this dataset with a wide range of traditional machine learning algorithms. The two-stage prediction process provides informative feature selection that establishes the relative influences of the input variables on burned-area predictions. Optimizing with mean absolute error (MAE) and root mean square error (RMSE) as separate objective functions provides complementary information with which to data mine each total burned-area incident. Such insight offers potential agricultural, ecological, environmental and forestry benefits by improving the understanding of the key influences associated with each burn event. Data mining the differential trends of cumulative absolute error and squared error also provides detailed insight with which to determine the suitability of each optimized solution to accurately predict burned-areas events of specific types. Such prediction accuracy and insight leads to confidence in how each prediction is derived. It provides knowledge to make appropriate responses and mitigate specific burn incidents, as they occur. Such informed responses should lead to short-term and long-term multi-faceted benefits by helping to prevent certain types of burn incidents being repeated or spread.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiia.2021.01.004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"108779988","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}

{"title":"An investigation into the potential of Gabor wavelet features for scene classification in wild blueberry fields","authors":"Gashaw Ayalew ,&nbsp;Qamar Uz Zaman ,&nbsp;Arnold W. Schumann ,&nbsp;David C. Percival ,&nbsp;Young Ki Chang","doi":"10.1016/j.aiia.2021.03.001","DOIUrl":"10.1016/j.aiia.2021.03.001","url":null,"abstract":"<div><p>A Gabor wavelets based technique was investigated as a potential tool for scene classification (into one of bare patch, plant, or weed) for its ultimate utility in site-specific application of agrochemicals in wild blueberry fields.</p><p>Images were gathered from five sites located in central Nova Scotia, Canada. Gabor wavelet features extracted from these images were used to classify scenes according to visually determined classes using step-wise linear discriminant analysis.</p><p>For individual fields, classification accuracy attained ranged between 87.9% and 98.3%; selected Gabor features ranged between 27 and 72; contextual accuracy for herbicide ranged between 67.5% and 96.7%, and contextual accuracy for fertilizer ranged between 63.6% and 97.1%. The pooled scenes yielded a classification accuracy of 81.4%, and contextual accuracy figures of 61.1% and 73.1% for herbicide and fertilizer, respectively, with selected Gabor features of 36.</p><p>Calibrations based on LDA coefficients from the pooled scenes could help avoid the need to re-calibrate for each field, whereas those based on individual field LDA coefficients could improve accuracy, hence enable saving on expensive agrochemicals.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiia.2021.03.001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"94014456","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}

{"title":"Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network","authors":"Punam Bedi,&nbsp;Pushkar Gole","doi":"10.1016/j.aiia.2021.05.002","DOIUrl":"10.1016/j.aiia.2021.05.002","url":null,"abstract":"<div><p>Plants are susceptive to various diseases in their growing phases. Early detection of diseases in plants is one of the most challenging problems in agriculture. If the diseases are not identified in the early stages, then they may adversely affect the total yield, resulting in a decrease in the farmers' profits. To overcome this problem, many researchers have presented different state-of-the-art systems based on Deep Learning and Machine Learning approaches. However, most of these systems either use millions of training parameters or have low classification accuracies. This paper proposes a novel hybrid model based on Convolutional Autoencoder (CAE) network and Convolutional Neural Network (CNN) for automatic plant disease detection. To the best of our knowledge, a hybrid system based on CAE and CNN to detect plant diseases automatically has not been proposed in any state-of-the-art systems present in the literature. In this work, the proposed hybrid model is applied to detect Bacterial Spot disease present in peach plants using their leaf images, however, it can be used for any plant disease detection. The experiments performed in this paper use a publicly available dataset named PlantVillage to get the leaf images of peach plants. The proposed system achieves 99.35% training accuracy and 98.38% testing accuracy using only 9,914 training parameters. The proposed hybrid model requires lesser number of training parameters as compared to other approaches existing in the literature. This, in turn, significantly decreases the time required to train the model for automatic plant disease detection and the time required to identify the disease in plants using the trained model.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.aiia.2021.05.002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113579666","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}

{"title":"Freeform path fitting for the minimisation of the number of transitions between headland path and interior lanes within agricultural fields","authors":"Mogens Graf Plessen","doi":"10.1016/j.aiia.2021.10.004","DOIUrl":"10.1016/j.aiia.2021.10.004","url":null,"abstract":"<div><p>Within the context of in-field path planning this paper discusses freeform path fitting for the minimisation of the number of transitions between headland path and interior lanes within agricultural fields. This topic is motivated by two observations. Due to crossings of tyre traces such transitions in practice often cause an increase of compacted area. Furthermore, for very tight angles between headland path and interior lanes undesired hairpin turns may become necessary due to the limited agility of in-field operating tractors. By minimising the number of interior lanes both detrimental effects can be mitigated. The potential of minimising the number of interior lanes by freeform path fitting is evaluated on 10 non-convex real-world fields including obstacle areas, and compared to the more common technique of fitting straight interior lanes.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721721000313/pdfft?md5=2fbd15ec28eef8536bb1de61ed438b49&pid=1-s2.0-S2589721721000313-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43219557","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}

{"title":"Will digital solution transform Sub-Sahara African agriculture?","authors":"Gezahagn Kudama ,&nbsp;Mabiratu Dangia ,&nbsp;Hika Wana ,&nbsp;Bona Tadese","doi":"10.1016/j.aiia.2021.12.001","DOIUrl":"10.1016/j.aiia.2021.12.001","url":null,"abstract":"<div><p>Given its superior importance of digital agricultural solutions to overcome challenges in agricultural activities, many of the solutions are in face of challenges to scale in Sub-Saharan Africa (SSA). On the other hand, the impact of digitalization on economic development in developing countries is documented in several literatures but digital technologies have lately touched the agricultural sector in SSA. The objective of this study was to briefly review the impact of digital solution on smallholder farmers agriculture transformation, and the key and challenges influencing of agricultural digitalization in SSA. We used all-inclusive approach comprising original research articles, peer-reviewed articles, working papers, conference papers, book chapters, database, guide book, and indexes from 60 recent empirical academic studies conducted on impacts on digital solution in the region to produce a broad review. Results show that digital solution, when effectively used in SSA, has enabled smallholder farmers to gain a wide range of benefits involving access to real timely price, market, and farming information and safe financial transactions, alternative value chain linkages, multifaceted knowledge, better earning and yield, reduce costs, social well-being and risk minimization, women empowerment benefits. In contrast, fail to use adaptable tools, unaffordability, digital illiterateness, low participation of women and old smallholder farmers due to their low income and education status, are main barriers to digitalization in agriculture. Accordingly, it essential to the SSA countries to invest on technologies that is adaptable their target population, ensure balancing regulatory and delivery approaches that permit equal involvement of women, old age category, and remote areas, realize affordable access to digital services through reducing data costs and tax cuts on digital agricultural tools, and offering digital skill training for farmers by segmenting them into their gender, age, and education to fully harness the opportunities of digitalization in agriculture.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721721000362/pdfft?md5=493eb0ef69fba904f43a7a3bcf89be53&pid=1-s2.0-S2589721721000362-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46408074","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}