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نتیجه جستجو - ذرت

تعداد مقالات یافته شده: 17
ردیف عنوان نوع
1 Monitoring crop phenology with street-level imagery using computer vision
پایش فنولوژی محصول با تصاویر سطح خیابان با استفاده از بینایی ماشین-2022
Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining the use of cheap high-quality cameras with recent advances in deep learning compute solutions to derive relevant thematic information. We present a framework to collect and extract crop type and phenological information from street level imagery using computer vision. Monitoring crop phenology is critical to assess gross primary productivity and crop yield. During the 2018 growing season, high-definition pictures were captured with side- looking action cameras in the Flevoland province of the Netherlands. Each month from March to October, a fixed 200-km route was surveyed collecting one picture per second resulting in a total of 400,000 geo-tagged pictures. At 220 specific parcel locations, detailed on the spot crop phenology observations were recorded for 17 crop types (including bare soil, green manure, and tulips): bare soil, carrots, green manure, grassland, grass seeds, maize, onion, potato, summer barley, sugar beet, spring cereals, spring wheat, tulips, vegetables, winter barley, winter cereals and winter wheat. Furthermore, the time span included specific pre-emergence parcel stages, such as differently cultivated bare soil for spring and summer crops as well as post-harvest cultivation practices, e.g. green manuring and catch crops. Classification was done using TensorFlow with a well-known image recognition model, based on transfer learning with convolutional neural network (MobileNet). A hypertuning methodology was developed to obtain the best performing model among 160 models. This best model was applied on an independent inference set discriminating crop type with a Macro F1 score of 88.1% and main phenological stage at 86.9% at the parcel level. Potential and caveats of the approach along with practical considerations for implementation and improvement are discussed. The proposed framework speeds up high quality in-situ data collection and suggests avenues for massive data collection via automated classification using computer vision.
keywords: Phenology | Plant recognition | Agriculture | Computer vision | Deep learning | Remote sensing | CNN | BBCH | Crop type | Street view imagery | Survey | In-situ | Earth observation | Parcel | In situ
مقاله انگلیسی
2 Computer-vision classification of corn seed varieties using deep convolutional neural network
طبقه بندی بینایی ماشین انواع بذر ذرت با استفاده از شبکه عصبی پیچیده عمیق-2021
Automated classification of seed varieties is of paramount importance for seed producers to maintain the purity of a variety and crop yield. Traditional approaches based on computer vision and simple feature extraction could not guarantee high accuracy classification. This paper presents a new approach using a deep convolutional neural network (CNN) as a generic feature extractor. The extracted features were classified with artificial neural network (ANN), cubic support vector machine (SVM), quadratic SVM, weighted k-nearest-neighbor (kNN), boosted tree, bagged tree, and linear discriminant analysis (LDA). Models trained with CNN-extracted features demonstrated better classification accuracy of corn seed varieties than models based on only simple features. The CNN-ANN classifier showed the best performance, classifying 2250 test instances in 26.8 s with classification accuracy 98.1%, precision 98.2%, recall 98.1%, and F1-score 98.1%. This study demonstrates that the CNN-ANN classifier is an efficient tool for the intelligent classification of different corn seed varieties.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Machine vision | Deep learning | Feature extraction | Non-handcrafted features | Texture descriptors
مقاله انگلیسی
3 Introducing an aflatoxin-safe labeling program in complex food supply chains: Evidence from a choice experiment in Nigeria
معرفی یک برنامه برچسب زدن بدون آفلاتوکسین در زنجیره های تأمین مواد غذایی پیچیده: شواهدی از یک آزمایش انتخاب در نیجریه-2021
Food contaminated with aflatoxins is one of the more prominent food safety issues facing developing countries. These toxins impose an immense burden on countries that have to deal with the repercussions of the contamination. Repercussions include increased public health concerns, increased health care expenditures, and other economic tolls. To alleviate these food safety concerns, the implementation of aflatoxin-safe certification can potentially incentivize and elevate food safety standards. This study uses a discrete choice experiment approach to assess if traders are willing to pay a price premium for aflatoxin-safe maize and whether such a premium varies across their market channels. Results indicate that maize traders who sell to other traders, large feed mills, food companies, and retailers exhibit a higher willingness to pay (WTP) for aflatoxin-safe certification compared to those who sell to small feed mills and consumers. Relevant policy implications are discussed.
Keywords: Aflatoxin contamination | Aflatoxin safe certification | Traders preferences | Traders willingness to pay | Maize, Nigeria
مقاله انگلیسی
4 In-field automatic detection of maize tassels using computer vision
تشخیص خودکار کاکل ذرت با استفاده از بینایی ماشین-2021
The heading stage of maize is an important period during its growth and development and indicates the beginning of its pollination. In this regard, an automated method for maize tassel detection is highly important to monitor maize growth. However, the recognition of maize heading stage mainly relies on visual evaluation. This method presents some limitations, such as expensive and subjective. This work proposed a novel method for automatic tassel detection. In the proposed algorithm, a color attenuation prior model was used to model the scene depth of saturation graph to remove image saturation. An Itti visual attention detection algorithm was used to detect the area of interest. Texture features and vegetation indices were used to develop a classification model to eliminate false positives. Pictures were captured using a commercial camera for two years to verify the stability of the proposed algorithm. Three indices were calculated to quantitatively assess and rate the algorithms. Experimental results show that the proposed method outperforms other existing methods, and its recall, precision, and F1 measure values are 86.30%, 91.44%, and 88.36%, respectively. Results indicate that the proposed method can effectively detect maize tassels in field images and remain stable with time.© 2020 China Agricultural University. Production and hosting by Elsevier B.V. on behalf of KeAi. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Maize tassel detection | Texture feature | Vegetation index | Saliency based
مقاله انگلیسی
5 استفاده از روش GIS-AHP برای ارزیابی مستعد بودن زمین در زراعت ذرت در منطقه نیمه خشک ، ایران
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 17
هدف از این مطالعه تهیه نقشه های در زمینه استعداد زمین در زراعت ذرت در خاک های آهکی و شور در دشت مرودشت ، ایران است. برای تخمین وزنی ویژگی های خاک ، اقلیم و توپوگرافی از روش چندمعیاره ای از فرآیند سلسله مراتبی تحلیلی (AHP) استفاده شده است. طبق نتایج، بافت خاک بیشترین ضریب وزنی ویژه (0.20) را در زراعت ذرت نشان داد و پس از آن، هدایت الکتریکی (121/0) ، شیب (1 2 0) و pH (1111/0) بیشترین ضریب وزنی را نشان داد. نقشه مستعد بودن اراضی نشان داد که 38.72٪ (76.646.7 هکتار) از اراضی کشاورزی مورد مطالعه، خاک مستعدی در تولید ذرت داشتند یعنی در طبقه مناسب ، 26.89٪ (53216.0 هکتار) در طبقه متوسط و 9/23٪ (47473 هکتار) در طبقه کمی مناسب قرار گرفتند. حدود ٪ 41/10 (4/2086٪) منطقه مورد مطالعه مناسب زراعت ذرت نبود. می توان دریافت که داده های مربوط به ویژگی های خاک ، آب و هوا و توپوگرافی به نظر متخصصان محلی، اولین قدم در کشت محصولات زراعی است.
کلمات کلیدی: داده های خاک | مدل سازی | توپوگرافی | روش AHP | GIS | مرودشت
مقاله ترجمه شده
6 Maize production and environmental costs: Resource evaluation and strategic land use planning for food security in northern Ghana by means of coupled emergy and data envelopment analysis
تولید ذرت و هزینه های زیست محیطی: ارزیابی منابع و برنامه ریزی استراتژیک کاربری اراضی برای امنیت غذایی در شمال غنا با استفاده از تجزیه و تحلیل آمیخته و پوشش داده ها-2020
This paper applies an integrated methodology which is constituted of the following: (i) the Emergy-Data Envelopment Analysis (EM-DEA), (ii) environmental Cost-Benefit Analysis (CBA), (iii) Value Chain Analysis (VCA), and (iv) Sustainability Balanced Scorecard (SBSC) approaches, -to support multicriteria decision analysis (MCDA) for strategic agricultural land use planning, which could contribute to improve food security in northern Ghana. Five scenarios of land use and resource management practices for maize production were modelled. The business-as-usual scenario was based on primary data, which were collected using semi-structured questionnaires administered to 56 small-scale maize farmers through personal interviews. The dominant land use was characterised by an external input ≤12 kg/ha/yr inorganic fertilizer with/without the addition of manure in rainfed maize systems. The project scenarios were based on APSIM simulations of maize yield response to 0, 20, 50 and 100 kg/ha/yr urea dosages, with/without supplemental irrigation. The scenarios were dubbed as follows: (1) no/low input systems were denoted by Extensive0, Extensive12, and Intercrop20, and (2) moderate/high input systems were denoted by Intensive50, and Intensive100. The EM-DEA approach was used to assess the resource use efficiency (RUE) and sustainability in maize production systems, Ghana. The measured RUE and sustainability were used as a proxy for further analyses by applying the environmental CBA and VCA approaches to calculate: (a) the environmental costs of producing maize, i.e. resource use measured as total emergy (U), and (b) benefits from the yielded maize, i.e. (b i) food provision from grain measured in kcal/yr, and (b ii) potential electricity (bioenergy) which could be generated from residue measured in MWh/yr. The information which was derived from the applications of the EM-DEA, CBA and VCA approaches was aggregated by applying the SBSC approach to do a sustainability appraisal of the scenarios. The results show that, when labour and services are included in the assessment of RUE and sustainability, Intercrop20 and Intensive50 achieved greater marginal yield, better RUE, sustainability and appraisal score. The same scenarios caused lesser impacts in terms of expansion of area cultivated compared to Extensive0 and Extensive12. Meanwhile the impacts of Intercrop20 and Intensive50 in terms of ecotoxicity, emissions, and demand for resources (energy, materials, labour and services) were lesser compared to Intensive100. The implications of the various scenarios are discussed. The environmental performance of the scenarios are compared to maize production systems in other developing regions in order to put this study within a broader context. We conclude that, the EM-DEA approach is useful for assessing RUE and sustainability of agricultural production systems at farm and regional scales, as well as in connecting the management planning level and regional development considerations.
Keywords: Food security | Sustainable agriculture | Strategic land use planning | Emergy-Data envelopment analysis | Environment-biomass-food-energy nexus | Sub-Saharan Africa
مقاله انگلیسی
7 Using machine learning to quantify the impacts of genetically modified crops on US midwest corn yields
استفاده از یادگیری ماشینی برای تعیین کمیت تأثیر محصولات اصلاح شده ژنتیکی بر عملکرد ذرت میان غربی ایالات متحده-2019
Global food security is becoming increasingly stressed by growing populations and climate change. To compensate for these stresses, crop yields must increase throughout the upcoming century. One of the more prominently featured solutions entails genetically modified crops, but their impacts on yields are contested. Here, we leverage machine learning techniques to examine the effects genetically modified crops have had on US corn yields. In particular, a principal components analysis conducted on US Midwest county yields reveals that the commercialization of genetically modified corn accentuated preexisting spatial disparities in production and explains approximately 6–12% of the regions inter-county variation in yields from 1980 to 2015. Additionally, counterfactual yield trajectories predicted by Bayesian structural time series models using non-genetically modified crops as synthetic controls suggest that the adoption of this biotechnology amounted to an approximate 13% increase in overall US corn yields from 1996 to 2015.
Keywords: GM crops | Principal components analysis | Corn yields | Machine learning | Bayesian structural time series
مقاله انگلیسی
8 Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations
روش های یادگیری آماری و ماشین برای ترکیب خاک و آب و هوا به ارزیابی توصیه های نیتروژن ذرت -2019
Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest.
Keywords: Corn | Machine learning | Nitrogen fertilizer recommendations | Soil | Weather
مقاله انگلیسی
9 تأثیر ماده همبند سمی بر عملکرد تولید گاوهای شیرده فریزیانی
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 25
از 20 گاو شیرده فریزیانی با متوسط وزن 540±17.59 کیلوگرم و در دوره شیردهی دوم تا پنجم، 60 روز قبل از زایمان استفاده شد و تا 120 روز آزمایش تغذیه شیردهی ادامه یافت تا تأثیر ماده سمی (بنتونیت و زئولیت) روی عملکرد تولید گاوهای شیرده بررسی شود. گاوها به طور تصادفی به چهار گروه مشابه تقسیم شدند (3 نفر). همه گاوها جیره اساسی (BR) دریافت کردند که متشکل از مخلوط علوفه کنسانتره (CFM) ، سیلاژ ذرت (CS) و کاه برنج (RS) بود. گروه 1 (BR) جیره اساسی بدون مکمل دریافت کرده و به عنوان جیره شاهد در نظر گرفته شد، در حالی که گروه‌های 2، 3 و 4 به ترتیب رژیم شاهد را به علاوه 2% بنتونیت ، 1% بنتونیت به علاوه 1٪ زئولیت یا 2٪ زئولیت مصرف DM به عنوان جیره‌های آزمایشی دریافت کردند. نتایج نشان داد که گروه‌های‌ دریافت کننده مکمل (P<0.05) نسبت به گروه‌هایی که مکمل دریافت نکردند، مصرف کل DM ، TDN و DCP بیشتری داشتند. همچنین با همین روند، غلظت pH و TVFA به طور قابل توجهی افزایش یافت (P<0.05)، در حالی که آمونیاک - N در گروه‌های‌ دریافت کننده مکمل نسبت به گروه 1 به طور چشمگیری (P<0.05) کاهش یافت. گروه 2 بیشترین غلظت پروتئین کل، گلوبولین ، گلوکز و T3 را (P<0.05) ثبت کرد و پس از آن گروه 3 و گروه 4 قرار گرفتتند، در حالی که گروه 1 کمترین مقادیر را داشت. در حالی که غلظت آلبومین با افزودن بنتونیت و زئولیت به طور چشمگیری کاهش یافت (P<0.05). غلظت کراتینین، اوره ، چربی کل و کلسیم و همچنین فعالیت AST و ALT برای گروه های مختلف تقریباً مشابه بود. تولید شیر واقعی و 4٪ FCM در گروه 2 (P<0.05) نسبت به گروه 4 و گروه 1 به طور چشمگیری بیشتر بود و از جیره گروه 3 بیشتر نبود: گروه 2 (P<0.05) بیشترین مقدار چربی ، پروتئین، لاکتوز، SNF و TS را نشان داد، در حالی که گروه 1 کمترین مقادیر را داشت. مقدار خاکستر شیر برای گروه های مختلف تقریباً مشابه بود. مکمل بنتونیت و زئولیت ضریب تبدیل غذایی را بهبود بخشید و گروه 2 بهترین مورد را ثبت کرد. این اختلاف تنها بین جیره بنتونیت (گروه 2) و شاهد (گروه 1) قابل توجه بود. گروه 2 بیشترین هزینه علوفه روزانه ، تولید محصول 4٪ FCM، درآمد خالص و بازده اقتصادی را ثبت کرد و پس از آن گروه 3 و گروه 4 بیشترین مقدار را داشتند، در حالی که گروه 1 کمترین هزینه علوفه، درآمد خالص و بازده اقتصادی را داشت، هزینه علوفه هر کیلوگرم 4٪ FCM گروه 2 و گروه 3 و گروه 4 به طور قابل توجهی کمتر بود (P<0.05)، در حالی که گروه 1 بیشترین مقدار را داشت. در نتیجه، درمقایسه با سایر جیره های تکمیلی و شاهد (بدون مکمل)، مکمل بنتونیت برای گاوهای شیرده فریزیانی در سطح 2٪ مصرف DM، به عنوان ماده همبند سمی بر هضم‌پذیری، لیکور شکمبه، برخی از پارامترهای خونی، مصرف علوفه ، میزان تولید و ترکیب شیر، ضریب تبدیل غذا یی و بازده اقتصادی بهترین اثر مثبت را داشت.
کلمات کلیدی: ماده همبند سمی گاوها | هضم‌پذیری | پارامترهای شکمبه و خون | عملکرد تولیدی و بازده اقتصادی.
مقاله ترجمه شده
10 Potential benefits of drought and heat tolerance for adapting maize to climate change in tropical environments
منافع بالقوه خشکسالی و حد گرما برای سازگاری ذرت با تغییرات آب و هوایی در محیط های گرمسیری-2018
Climate change and population growth pose great challenges to the food security of the millions of people who grow maize in the already fragile agricultural systems in tropical environments. There is an urgent need for maize varieties that are both drought and heat tolerant given the already prevailing drought and heat stress levels in many tropical environments, which are set to exacerbate with climate change. In this study, the crop growth simulation model for maize (CERES-Maize) was used to quantify the impact of climate change on maize and the potential benefits of incorporating drought and heat tolerance into the commonly grown (benchmark) maize varieties at six sites in Eastern and Southern Africa and one site in South Asia. Simulation results indicate that climate change will have a negative impact on maize yield at all the sites studied but the degree of the impact varies with location, level of warming and rainfall changes. Combined hotter and drier climate change scenarios (involving increases in warming with a reduction in rainfall) resulted in greater average simulated maize yield reduction (21, 33 and 50% under 1, 2 and 4 °C warming, respectively) than hotter only climate change scenarios (11, 21 and 41%, respectively). Incorporating drought, heat and combined drought & heat tolerance into benchmark varieties increased simulated maize yield under both the baseline and future climates. The average simulated benefit from combined drought & heat tolerance was at least twice that of heat or drought tolerance and it increased with the increase in warming levels. The magnitude of the simulated benefits from drought tolerance, heat tolerance and combined drought & heat tolerance and potential acceptability of the varieties by farmers varied across sites and climate scenarios indicating the need for proper targeting of varieties where they fit best and benefit most. It is concluded that incorporating drought and heat tolerance into maize germplasm has the potential to offset predicted yield losses and sustain maize productivity under climate change in vulnerable sites.
keywords: Climate change |Maize |Drought tolerance |Heat tolerance |Tropical environments
مقاله انگلیسی
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