Search results for: crop model

Authors: Masahiro Ohmura, Koh Kakusho, Takeshi Okadome

Abstract:

This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.

Keywords: stock market prediction, social moods, regression model, DJIA

Procedia PDF Downloads 549

Authors: Dessie Fisseha, Derbew Belew, Ambecha Olika

Abstract:

Potato is a high-potential food security crop in Ethiopia. However, the yield and productivity of the crop have been far below the world average. This is due to several factors, including appropriate agronomic practices, such as time of earthing-up and inflorescence management. A field experiment was conducted at Jimma, Southwest Ethiopia, during 2016/17 under irrigation to determine the effect of time of earthing-up and inflorescence removal on the growth, yield, and quality of potatoes. The treatments consisted of a time of earthing-up (no earthing-up, earthing-up at 15, 30, and 45 days after complete plant emergence) and inflorescence removal (inflorescence removed and not removed). Potato variety (Belete) was used for this experiment. A 2x4 factorial experiment was laid out with three replications. Data collected on the growth, yield, and quality components of potatoes were analyzed using SAS Version 9.3 statistical software. Inflorescence removal affected the majority of the growth and yield parameters, while the time of earthing-up affected all growth, yield, and quality (green tuber number) parameters. Earthing-up at 15 days in combination with inflorescence removal (at 60 days after complete plant emergence) gave better plant growth and maximum tuber yield of the Belete potato variety under irrigated conditions. Since the current research was conducted at one location, in one season, and with one potato cultivar (Belete), it would be advisable to repeat the experiment so as to arrive at a final conclusion and subsequent recommendation.

Keywords: Belete, earthing-up, inflorescence, yield

Procedia PDF Downloads 77

Authors: Adji Achmad Rinaldo Fernandes

Abstract:

SEM analysis is a complex multivariate analysis because it involves a number of exogenous and endogenous variables that are interconnected to form a model. The measurement model is divided into two, namely, the reflective model (reflecting) and the formative model (forming). Before carrying out further tests on SEM, there are assumptions that must be met, namely the linearity assumption, to determine the form of the relationship. There are three modeling approaches to path analysis, including parametric, nonparametric and semiparametric approaches. The aim of this research is to develop semiparametric SEM and obtain the best model. The data used in the research is secondary data as the basis for the process of obtaining simulation data. Simulation data was generated with various sample sizes of 100, 300, and 500. In the semiparametric SEM analysis, the form of the relationship studied was determined, namely linear and quadratic and determined one and two knot points with various levels of error variance (EV=0.5; 1; 5). There are three levels of closeness of relationship for the analysis process in the measurement model consisting of low (0.1-0.3), medium (0.4-0.6) and high (0.7-0.9) levels of closeness. The best model lies in the form of the relationship X1Y1 linear, and. In the measurement model, a characteristic of the reflective model is obtained, namely that the higher the closeness of the relationship, the better the model obtained. The originality of this research is the development of semiparametric SEM, which has not been widely studied by researchers.

Keywords: semiparametric SEM, measurement model, structural model, reflective model, formative model

Procedia PDF Downloads 43

Authors: Najafi S., Heidai R., Jamei R., Tofigh F.

Abstract:

In order to study the effects of magnetic field on the root system and growth of Phaseolus vulgaris, an experiment was conducted in 2012. The possible involvement of magnetic field (MF) pretreatment in physiological factors of Phaseolus vulgaris was investigated. Seeds were subjected to 10 days with 1.8 mT of magnetic field for 1h per day. MF pretreatment decreased the plant height, fresh and dry weight, length of root and length of shoot, Chlorophyll a, Chlorophyll b and carotenoid in 10 days old seedling. In addition, activity of enzymes such as Catalase and Guaiacol peroxidase was decreased due to MF exposure. Also, the total Protein and DPPH content of the treated by magnetic field was not significantly changed in compare to control groups, while the flavonoid, Phenol and prolin content of the treated of the treated by magnetic field was significantly changed in compare to control groups. Lateral branches of roots and secondary roots increased with MF. The results suggest that pretreatment of this MF plays important roles in changes in crop productivity. In all cases there was observed a slight stimulating effect of the factors examined. The growth dynamics were weakened. The plants were shorter. Moreover, the effect of a magnetic field on the crop of Phaseolus vulgaris and its structure was small.

Keywords: carotenoid, Chlorophyll a, Chlorophyll b, DPPH, enzymes, flavonoid, germination, growth, phenol, proline, protein, magnetic field, phaseolus vulgaris

Procedia PDF Downloads 578

Authors: Ibrahim O. Abdulmalik, Michael C. Amonye, Mahdi Makoyo

Abstract:

Manual knapsack sprayer is the most popular means of farm spraying in Nigeria. It has its limitations. Apart from the human fatigue, which leads to unsteady walking steps, their field capacities are small. They barely cover about 0.2hectare per hour. Their small swath implies that a sizeable farm would take several days to cover. Weather changes are erratic and often it is desired to spray a large farm within hours or few days for even effect, uniformity and to avoid adverse weather interference. It is also often required that a large farm be covered within a short period to avoid re-emergence of weeds before crop emergence. Deployment of many knapsack operators to large farms has not been successful. Human error in taking equally spaced swaths usually result in over dosage of overlaps and in unapplied areas due to error at edges overlaps. Large farm spraying require boom equipment with larger swath. Reduced error in swath overlaps and spraying within the shortest possible time are then assured. Tractor boom sprayers would readily overcome these problems and achieve greater coverage, but they are not available in the country. Tractor hire for cultivation is very costly with the attendant lack of spare parts and specialized technicians for maintenance wherefore farmers find it difficult to engage tractors for cultivation and would avoid considering the employment of a tractor boom sprayer. Animal traction in farming is predominant in Nigeria, especially in the Northern part of the country. Development of boom sprayers drawn by work animals surely implies the maximization of animal utilization in farming. The Hydraulic Equipment Development Institute, Kano, in keeping to its mandate of targeted R&D in hydraulic and pneumatic systems, has developed an Animal Drawn Knapsack Boom Sprayer with four nozzles using the axle mechanism of a two wheeled cart to actuate the piston pump of two knapsack sprayers in line with appropriate technology demand of the country. It is hoped that the introduction of this novel contrivance shall enhance crop protection practice and lead to greater crop and food production in Nigeria.

Keywords: boom, knapsack, farm, sprayer, wheel axle

Procedia PDF Downloads 283

Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

Abstract:

In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

Procedia PDF Downloads 258

Authors: Alan Wan

Abstract:

In recent years, the body of literature on frequentist model averaging in statistics has grown significantly. Most of this work focuses on models with different mean structures but leaves out the variance consideration. In this paper, we consider a regression model with multiplicative heteroscedasticity and develop a model averaging method that combines maximum likelihood estimators of unknown parameters in both the mean and variance functions of the model. Our weight choice criterion is based on a minimisation of a plug-in estimator of the model average estimator's squared prediction risk. We prove that the new estimator possesses an asymptotic optimality property. Our investigation of finite-sample performance by simulations demonstrates that the new estimator frequently exhibits very favourable properties compared to some existing heteroscedasticity-robust model average estimators. The model averaging method hedges against the selection of very bad models and serves as a remedy to variance function misspecification, which often discourages practitioners from modeling heteroscedasticity altogether. The proposed model average estimator is applied to the analysis of two real data sets.

Keywords: heteroscedasticity-robust, model averaging, multiplicative heteroscedasticity, plug-in, squared prediction risk

Procedia PDF Downloads 387

Authors: Myung Hwan Na, Ho- Chun Song, EunHee Hong

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We widely use normal linear mixed-effects model to analysis data in repeated measurement. In case of detecting heteroscedasticity and the non-normality of the population distribution at the same time, normal linear mixed-effects model can give improper result of analysis. To achieve more robust estimation, we use heavy tailed linear mixed-effects model which gives more exact and reliable analysis conclusion than standard normal linear mixed-effects model.

Keywords: reliability, tires, field data, linear mixed-effects model

Procedia PDF Downloads 564

Authors: Anusha Jain, Santosh Vishwanathan, Praveen K. Gupta, Shwetha S., Kavitha S. N.

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Agriculture has a major impact on the Indian economy, with the highest employment ratio than any sector of the country. Currently, most of the traditional agricultural practices and farming methods are manual, which results in farmers not realizing their maximum productivity often due to increasing in labour cost, inefficient use of water sources leading to wastage of water, inadequate soil moisture content, subsequently leading to food insecurity of the country. This research paper aims to solve this problem by developing a full-fledged web application-based platform that has the capacity to associate itself with a Microcontroller-based Automated Irrigation System which schedules the irrigation of crops based on real-time soil moisture content employing soil moisture sensors centric to the crop’s requirements using WSN (Wireless Sensor Networks) and M2M (Machine To Machine Communication) concepts, thus optimizing the use of the available limited water resource, thereby maximizing the crop yield. This robust automated irrigation system provides end-to-end automation of Irrigation of crops at any circumstances such as droughts, irregular rainfall patterns, extreme weather conditions, etc. This platform will also be capable of achieving a nationwide united farming community and ensuring the welfare of farmers. This platform is designed to equip farmers with prerequisite knowledge on tech and the latest farming practices in general. In order to achieve this, the MailChimp mailing service is used through which interested farmers/individuals' email id will be recorded and curated articles on innovations in the world of agriculture will be provided to the farmers via e-mail. In this proposed system, service is enabled on the platform where nearby crop vendors will be able to enter their pickup locations, accepted prices and other relevant information. This will enable farmers to choose their vendors wisely. Along with this, we have created a blogging service that will enable farmers and agricultural enthusiasts to share experiences, helpful knowledge, hardships, etc., with the entire farming community. These are some of the many features that the platform has to offer.

Keywords: WSN (wireless sensor networks), M2M (M/C to M/C communication), automation, irrigation system, sustainability, SAAS (software as a service), soil moisture sensor

Procedia PDF Downloads 131

Authors: Thavananthan Sivananthawerl

Abstract:

Shifting cultivation (Chena, Slash & Burn) is a cultivation method of raising, primarily, food crops (mainly annual) where an area of land is cleared off for its vegetation and cultivated for a period, and the abandoned (fallow) for its fertility to be naturally restored. Although this is the oldest (more than 5000 years) farming system, it is still practiced by indigenous communities of several countries such as Sri Lanka, India, Indonesia, Malaysia, Myanmar, West & Central Africa, and Amazon rainforest area. In Sri Lanka, shifting cultivation is mainly practiced during the North-East monsoon (called as Maha season, from Sept. to Dec.) with no irrigation. The traditional system allows farmers to cultivate for a short period of cultivation and a long period fallow period. This was facilitated mainly by the availability of land with less population. In addition, in the old system, cultivation practices were mostly related to religious and spiritual practices (Astrology, dynamic farming, etc.). At present, the majority of the shifting cultivators (SC’s) are cultivating in government lands, and most of them are adopting new technology (seeds, agrochemicals, machineries). Due to the local demand, almost 70% of the SC’s growing maize is mono-crop, and the rest with mixed-crop, such as groundnut, cowpea, millet, and vegetables. To ensure continuous cultivation and reduce moisture stress, they established ‘dug wells’ and used pumps to lift water from nearby sources. Due to this, the fallow period has been reduced drastically to 1- 2 years. To have the future prosperous of system, farmers should be educated so that they can understand the harmful effects of shifting cultivation and require new policies and a framework for converting the land use pattern towards high economic returns (new crop varieties, maintaining soil fertility, reducing soil erosion) while protecting the natural forests. The practice of agroforestry should be encouraged in which both the crops and the tall trees are cared for by farmers simultaneously. To facilitate the continuous cultivation, the system needs to develop water harvesting, water-conserving technologies, and scientific water management for the limited rainy season. Even though several options are available, all the solutions vary from region to region. Therefore, it is only the government and cultivators together who can find solutions to the problems of the specific areas.

Keywords: shifting cultivation, agroforestry, fallow, economic returns, government, Sri Lanka

Procedia PDF Downloads 96

Authors: Sabin Aslam, Sultan Habibullah Khan, James G. Thomson, Abhaya M. Dandekar

Abstract:

Losses due to viral diseases are posing a serious threat to crop production. A quick breakdown of resistance to viruses like Cotton Leaf Curl Virus (CLCuV) demands the application of a proficient technology to engineer durable resistance. Gene stacking has recently emerged as a potential approach for integrating multiple genes in crop plants. In the present study, recombinase technology has been used for site-specific gene stacking. A target vector (pG-Rec) was designed for engineering a predetermined specific site in the plant genome whereby genes can be stacked repeatedly. Using Agrobacterium-mediated transformation, the pG-Rec was transformed into Coker-312 along with Nicotiana tabacum L. cv. Xanthi and Nicotiana benthamiana. The transgene analysis of target lines was conducted through junction PCR. The transgene positive target lines were used for further transformations to site-specifically stack two genes of interest using Bxb1 and PhiC31 recombinases. In the first instance, Cas9 driven by multiplex gRNAs (for Rep gene of CLCuV) was site-specifically integrated into the target lines and determined by the junction PCR and real-time PCR. The resulting plants were subsequently used to stack the second gene of interest (AVP3 gene from Arabidopsis for enhancing cotton plant growth). The addition of the genes is simultaneously achieved with the removal of marker genes for recycling with the next round of gene stacking. Consequently, transgenic marker-free plants were produced with two genes stacked at the specific site. These transgenic plants can be potential germplasm to introduce resistance against various strains of cotton leaf curl virus (CLCuV) and abiotic stresses. The results of the research demonstrate gene stacking in crop plants, a technology that can be used to introduce multiple genes sequentially at predefined genomic sites. The current climate change scenario highlights the use of such technologies so that gigantic environmental issues can be tackled by several traits in a single step. After evaluating virus resistance in the resulting plants, the lines can be a primer to initiate stacking of further genes in Cotton for other traits as well as molecular breeding with elite cotton lines.

Keywords: cotton, CRISPR/Cas9, gene stacking, genome editing, recombinases

Procedia PDF Downloads 156

Authors: Abdoullah Fath-Allah, Laila Cheikhi, Rafa E. Al-Qutaish, Ali Idri

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The e-government emerging concept transforms the way in which the citizens are dealing with their governments. Thus, the citizens can execute the intended services online anytime and anywhere. This results in great benefits for both the governments (reduces the number of officers) and the citizens (more flexibility and time saving). Therefore, building a maturity model to assess the e-government portals becomes desired to help in the improvement process of such portals. This paper aims at proposing an e-government maturity model based on the measurement of the best practices’ presence. The main benefit of such maturity model is to provide a way to rank an e-government portal based on the used best practices, and also giving a set of recommendations to go to the higher stage in the maturity model.

Keywords: best practices, e-government portal, maturity model, quality model

Procedia PDF Downloads 338

Authors: Maletsema Alina Mofokeng, Nemera Shargie

Abstract:

Sorghum is one of the most important cereal crops grown as a source of calories for many people in tropics and sub-tropics of the world. Proper characterisation and evaluation of crop germplasm is an important component for effective management of genetic resources and their utilisation in the improvement of the crop through plant breeding. The objective of the study was to estimate the genetic diversity present in sorghum accessions grown in South Africa using agro-morphological traits and some nutritional contents. The experiment was carried out in Potchefstroom. Data were subjected to correlations, principal components analysis, and hierarchical clustering using GenStat statistical software. There were highly significance differences among the accessions based on agro-morphological and nutritional quality traits. Grain yield was highly positively correlated with panicle weight. Plant height was highly significantly correlated with internode length, leaf length, leaf number, stem diameter, the number of nodes and starch content. The Principal component analysis revealed three most important PCs with a total variation of 78.6%. The protein content ranged from 7.7 to 14.7%, and starch ranged from 58.52 to 80.44%. The accessions that had high protein and starch content were AS16cyc and MP4277. There was vast genetic diversity observed among the accessions assessed that can be used by plant breeders to improve yield and nutritional traits.

Keywords: accessions, genetic diversity, nutritional quality, sorghum

Procedia PDF Downloads 263

Authors: Ahmad Aridah, Maria Fay Rola-Rubzen, Zora Singh

Abstract:

This study investigated the effectiveness of various types of irrigation systems in regards to the impact that these have on the productivity of date palms in the semi-arid and arid region of Sebha, Southwest Libya. The date palm is an economically important crop in Libya and contributes to the agriculture industry, foreign exchange earnings, farmers’ income, and employment in the country. The date palm industry relies on large amounts of water for growing the crop. Farmers in Southwest Libya use a variety of irrigation systems, but the quality and quantity of water varies between systems and this affects the productivity and income of farmers. Using survey data from 210 farmers, this study estimated and assessed the pros and cons of different types of irrigation systems for date palm production under various irrigation systems currently used in Sebha, Libya. The number of years farmers have used irrigation, the area, irrigation water consumption, time of irrigation, number of farm workers (including family labour) and inputs used were measured for surface, sprinkler and drip irrigation methods. Findings from this research provide new insights into the advantages and disadvantages of the various irrigation systems, problems encountered by farmers and the factors that affect the quality and quantity of the irrigation system. The paper discussed proposed solutions to deal with the problems including timing of irrigation, canal maintenance, repair of wells and water control.

Keywords: Libya, factors, irrigation method, date palm

Procedia PDF Downloads 352

Authors: I. C. Tolias, A. G. Venetsanos, N. Markatos

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In the present work, CFD simulations of a large scale open deflagration experiment are performed. Stoichiometric hydrogen-air mixture occupies a 20 m hemisphere. Two combustion models are compared and are evaluated against the experiment. The Eddy Dissipation Model and a Multi-physics combustion model which is based on Yakhot’s equation for the turbulent flame speed. The values of models’ critical parameters are investigated. The effect of the turbulence model is also examined. k-ε model and LES approach were tested.

Keywords: CFD, deflagration, hydrogen, combustion model

Procedia PDF Downloads 504

Authors: J. Rhodes, V. Cheng, P. Lok

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The aim of this study is to develop a parsimonious model that explains the effect of different stimulus on a tourist’s intention to visit a new destination. The model consists of destination trust and interest as the mediating variables. The model was tested using two different types of stimulus; both studies empirically supported the proposed model. Furthermore, the first study revealed that advertising has a stronger effect than positive online reviews. The second study found that the peripheral route of the elaboration likelihood model has a stronger influence power than the central route in this context.

Keywords: advertising, electronic word-of-mouth, elaboration likelihood model, intention to visit, trust

Procedia PDF Downloads 459

Authors: Ahmad Sarfaraz, Raiyad Herwies

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In this paper, a multi-criteria decision making analysis is used to help any organization selects the best KM tool that fits and serves its needs. The AHP model is used based on a previous study to highlight and identify the main criteria and sub-criteria that are incorporated in the selection process. Different KM tools alternatives with different criteria are compared and weighted accurately to be incorporated in the GP model. The main goal is to combine the GP model with the AHP model to ensure that selecting the KM tool considers the resource constraints. Two important issues are discussed in this paper: how different factors could be taken into consideration in forming the AHP model, and how to incorporate the AHP results into the GP model for better results.

Keywords: knowledge management, analytical hierarchy process, goal programming, multi-criteria decision making

Procedia PDF Downloads 386

Authors: Farzad Tofigh, Saeideh Najafi, Reza Heidari, Rashid Jamei

Abstract:

In order to study the effects of magnetic field on the root system and growth of Phaseolus vulgaris, an experiment was conducted in 2012. The possible involvement of magnetic field (MF) pretreatment in physiological factors of Phaseolus vulgaris was investigated. Seeds were subjected to 10 days with 1.8 mT of magnetic field for 1h per day. MF pretreatment decreased the plant height, fresh and dry weight, length of root and length of shoot, Chlorophyll a, Chlorophyll b and carotenoid in 10 days old seedling. In addition, activity of enzymes such as Catalase and Guaiacol peroxidase was decreased due to MF exposure. Also, the total Protein and DPPH content of the treated by magnetic field was not significantly changed in compare to control groups, while the flavonoid, Phenol and prolin content of the treated of the treated by magnetic field was significantly changed in compare to control groups. Lateral branches of roots and secondary roots increased with MF. The results suggest that pretreatment of this MF plays important roles in changes in crop productivity. In all cases there was observed a slight stimulating effect of the factors examined. The growth dynamics were weakened. The plants were shorter. Moreover, the effect of a magnetic field on the crop of Phaseolus vulgaris and its structure was small.

Keywords: carotenoid, chlorophyll a, chlorophyll b, DPPH, enzymes, flavonoid, germination, growth, phenol, proline, protein, magnetic field

Procedia PDF Downloads 504

Authors: Zunnu Raen Akhtar, Haseeb Jan, Muhammad Latif, Ali Aziz, Ali Akash, Waleed Afzal Naveed, Muhammad Naveed Akhtar

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Wheat (Triticum aestivium) is main staple food crop of Pakistan. This crop is highly infested with aphids which cause the loss of yield. A study was carried out at Entomological Research Institute of Ayub Agriculture Research Institute Faisalabad during 2015-16. Eleven wheat genotypes (FSD- 08, v-11098, NIBGE gandum-3, shafaq 2006, v-13372, Punjab-2011, v-12304, 11C023, v-13005, v-13016, v-12120) were sown using the Randomized Complete Block Design in the research area of Entomological Research Institute Faisalabad during the year 2015-16. The aphid infestation per tiller on each genotype was observed from the first week of January till the third week of March maximum. The results reveal that shafaq 2006 and V-12120 were found more susceptible with 10.22 and 9.90 aphids per tiller and minimum infestation was observed on the Punjab-2011 and 11C023 i.e., 5.72 and 5.99 aphid per tiller respectively. When the peak season observations were analyzed, slight changes occur in the peak population of aphid among all wheat genotypes. The most susceptible genotypes were Shafaq 2006 and V-12304 with 18.63 and 18.23 aphids per tiller while the wheat genotypes 11C023 and Punjab 2011 received minimum aphid population which was 9.99 and 10.47 aphids per tiller and they considered more tolerant.

Keywords: Triticum aestivium, Schizaphis graminum, population, resistance

Procedia PDF Downloads 334

Authors: Maharjan Shree Kumar

Abstract:

Climate change impacts are observed in all livelihood sectors primarily in agriculture and forestry. Multiple factors have influenced the climate vulnerabilities and adaptations in agricultural at the household level. This study focused on the factors affecting adaptation in agriculture in Madi and Deukhuri valleys of Central and Western Nepal. The systematic random sampling technique was applied to select 154 households in Madi and 150 households in Deukhuri. The main purpose of the study was to analyze the socio-economic factors that either influence or restrain the farmers’ adaptation to climate change at the household level by applying the linear probability model. Based on the analysis, it is revealed that crop diversity, education, training and total land holding (acre) were positively significant for adaptation choices the study sites. Rest of the variables were not significant though indicated positive as expected except age, occupation, ethnicity, family size, and access to credit.

Keywords: adaptation, agriculture, climate, factors, Nepal

Procedia PDF Downloads 153

Authors: Lu Zhao, Nadir Farhi, Yeltsin Valero, Zoi Christoforou, Nadia Haddadou

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In this paper, a long short-term memory (LSTM) neural network model is proposed to replicate simultaneously car-following and lane-changing behaviors in road networks. By combining two kinds of LSTM layers and three input designs of the neural network, six variants of the LSTM model have been created. These models were trained and tested on the NGSIM 101 dataset, and the results were evaluated in terms of longitudinal speed and lateral position, respectively. Then, we compared the LSTM model with a classical car-following model (the intelligent driving model (IDM)) in the part of speed decision. In addition, the LSTM model is compared with a model using classical neural networks. After the comparison, the LSTM model demonstrates higher accuracy than the physical model IDM in terms of car-following behavior and displays better performance with regard to both car-following and lane-changing behavior compared to the classical neural network model.

Keywords: traffic modeling, neural networks, LSTM, car-following, lane-change

Procedia PDF Downloads 263

Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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The assessment of disease levels in crop fields is an important and time-consuming task that generally relies on expert knowledge of trained individuals. Image classification in agriculture problems historically has been based on classical machine learning strategies that make use of hand-engineered features in the top of a classification algorithm. This approach tends to not produce results with high accuracy and generalization to the classes classified by the system when the nature of the elements has a significant variability. The advent of deep convolutional neural networks has revolutionized the field of machine learning, especially in computer vision tasks. These networks have great resourcefulness of learning and have been applied successfully to image classification and object detection tasks in the last years. The objective of this work was to propose a new method based on deep learning convolutional neural networks towards the task of disease level monitoring. Common RGB images of winter wheat were obtained during a growing season. Five categories of disease levels presence were produced, in collaboration with agronomists, for the algorithm classification. Disease level tasks performed by experts provided ground truth data for the disease score of the same winter wheat plots were RGB images were acquired. The system had an overall accuracy of 84% on the discrimination of the disease level classes.

Keywords: crop disease assessment, deep learning, precision agriculture, residual neural networks

Procedia PDF Downloads 334

Authors: Nubwimana Rachel, Mugabowindekwe Maurice

Abstract:

Trees play a crucial role in maintaining biodiversity and providing various ecological services. Traditional methods of counting trees are time-consuming, and there is a need for more efficient techniques. However, deep learning makes it feasible to identify the multi-scale elements hidden in aerial imagery. This research focuses on the application of deep learning techniques for tree detection and counting in both forest and non-forest areas through the exploration of the deep learning application for automated tree detection and counting using satellite imagery. The objective is to identify the most effective model for automated tree counting. We used different deep learning models such as YOLOV7, SSD, and UNET, along with Generative Adversarial Networks to generate synthetic samples for training and other augmentation techniques, including Random Resized Crop, AutoAugment, and Linear Contrast Enhancement. These models were trained and fine-tuned using satellite imagery to identify and count trees. The performance of the models was assessed through multiple trials; after training and fine-tuning the models, UNET demonstrated the best performance with a validation loss of 0.1211, validation accuracy of 0.9509, and validation precision of 0.9799. This research showcases the success of deep learning in accurate tree counting through remote sensing, particularly with the UNET model. It represents a significant contribution to the field by offering an efficient and precise alternative to conventional tree-counting methods.

Keywords: remote sensing, deep learning, tree counting, image segmentation, object detection, visualization

Procedia PDF Downloads 77

Authors: Gholba Niranjan Dilip, Anil Kumar

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Conduct of accurate reliable mapping of oil palm plantations and census of individual palm trees is a huge challenge. This study addresses this challenge and developed an optimized solution implemented deep learning techniques on remote sensing data. The oil palm is a very important tropical crop. To improve its productivity and land management, it is imperative to have accurate census over large areas. Since, manual census is costly and prone to approximations, a methodology for automated census using panchromatic images from Cartosat-2, SkySat and World View-3 satellites is demonstrated. It is selected two different study sites in Indonesia. The customized set of training data and ground-truth data are created for this study from Cartosat-2 images. The pre-trained model of Single Shot MultiBox Detector (SSD) Lite MobileNet V2 Convolutional Neural Network (CNN) from the TensorFlow Object Detection API is subjected to transfer learning on this customized dataset. The SSD model is able to generate the bounding boxes for each oil palm and also do the counting of palms with good accuracy on the panchromatic images. The detection yielded an F-Score of 83.16 % on seven different images. The detections are buffered and dissolved to generate polygons demarcating the boundaries of the oil palm plantations. This provided the area under the plantations and also gave maps of their location, thereby completing the automated census, with a fairly high accuracy (≈100%). The trained CNN was found competent enough to detect oil palm crowns from images obtained from multiple satellite sensors and of varying temporal vintage. It helped to estimate the increase in oil palm plantations from 2014 to 2021 in the study area. The study proved that high-resolution panchromatic satellite image can successfully be used to undertake census of oil palm plantations using CNNs.

Keywords: object detection, oil palm tree census, panchromatic images, single shot multibox detector

Procedia PDF Downloads 161

Authors: D. S. Gurjar, R. Kaur, K. P. Singh, R. Singh

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A greater volume of wastewater generate from urban areas in India. Due to the adequate availability, less energy requirement and nutrient richness, farmers of urban and peri-urban areas are deliberately using wastewater to grow high value vegetable crops. Wastewater contains pathogens and toxic pollutants, which can enter in the food chain system while using wastewater for irrigating vegetable crops. Hence, wastewater can use for growing commercial flower crops that may avoid food chain contamination. Tuberose (Polianthes tuberosa L.) is one of the most important commercially grown, cultivated over 30, 000 ha area, flower crop in India. Its popularity is mainly due to the sweet fragrance as well as the long keeping quality of the flower spikes. The flower spikes of tuberose has high market price and usually blooms during summer and rainy seasons when there is meager supply of other flowers in the market. It has high irrigation water requirement and fresh water supply is inadequate in tuberose growing areas of India. Therefore, wastewater may fulfill the water and nutrients requirements and may enhance the productivity of tuberose. Keeping in view, the present study was carried out at WTC farm of ICAR-Indian Agricultural Research Institute, New Delhi in 2014-15. Prajwal was the variety of test crop. The seven treatments were taken as T-1. Wastewater irrigation at 0.6 ID/CPE, T-2: Wastewater irrigation at 0.8 ID/CPE, T-3: Wastewater irrigation at 1.0 ID/CPE, T-4: Wastewater irrigation at 1.2 ID/CPE, T-5: Wastewater irrigation at 1.4 ID/CPE, T-6: Conjunctive use of Groundwater and Wastewater irrigation at 1.0 ID/CPE in cyclic mode, T-7: Control (Groundwater irrigation at 1.0 ID/CPE) in randomized block design with three replication. Wastewater and groundwater samples were collected on monthly basis (April 2014 to March 2015) and analyzed for different parameters of irrigation quality (pH, EC, SAR, RSC), pollution hazard (BOD, toxic heavy metals and Faecal coliforms) and nutrients potential (N, P, K, Cu, Fe, Mn, Zn) as per standard methods. After harvest of tuberose crop, soil samples were also collected and analyzed for different parameters of soil quality as per standard methods. The vegetative growth and flower parameters were recorded at flowering stage of tuberose plants. Results indicated that wastewater samples had higher nutrient potential, pollution hazard as compared to groundwater used in experimental crop. Soil quality parameters such as pH EC, available phosphorous & potassium and heavy metals (Cu, Fe, Mn, Zn, Cd. Pb, Ni, Cr, Co, As) were not significantly changed whereas organic carbon and available nitrogen were significant higher in the treatments where wastewater irrigations were given at 1.2 and 1.4 ID/CPE as compared to groundwater irrigations. Significantly higher plant height (68.47 cm), leaves per plant (78.35), spike length (99.93 cm), rachis length (37.40 cm), numbers of florets per spike (56.53), cut spike yield (0.93 lakh/ha) and loose flower yield (8.5 t/ha) were observed in the treatment of Wastewater irrigation at 1.2 ID/CPE. Study concluded that given quality of wastewater improves the productivity of tuberose without an adverse impact on soil quality/health. However, its long term impacts need to be further evaluated.

Keywords: conjunctive use, irrigation, tuberose, wastewater

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Authors: Coskun Serefoglu

Abstract:

The study aims to analyse how a traditional sector such as agri-food could be mobilized through regional innovation strategies. A principal component analysis as well as qualitative information, such as in-depth interviews, focus group and surveys, were employed to find the priority sectors. An agri-food model was developed which includes both a linear model and interactive model. The model consists of two main components, one of which is technological integration and the other one is agricultural extension which is based on Land-grant university approach of U.S. which is not a common practice in Turkey.

Keywords: regional innovation strategy, interactive model, agri-food sector, local development, planning, regional development

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Authors: Sasiporn Rattanasupha, Settapat Chinviriyasit

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The treatment function adopts a continuous and differentiable function which can describe the effect of delayed treatment when the number of infected individuals increases and the medical condition is limited. In this paper, the SEIR epidemic model with treatment function is studied to investigate the dynamics of the model due to the effect of treatment. It is assumed that the treatment rate is proportional to the number of infective patients. The stability of the model is analyzed. The model is simulated to illustrate the analytical results and to investigate the effects of treatment on the spread of infection.

Keywords: basic reproduction number, local stability, SEIR epidemic model, treatment function

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Authors: Tefera Kebede Leyu

Abstract:

The purpose of this study was to estimate the impact of LIVES project participation on the level of technical efficiency of farm households in three regions of Ethiopia. We used household-level data gathered by IRLI between February and April 2014 for the year 2013(retroactive). Data on 1,905 (754 intervention and 1, 151 control groups) sample households were analyzed using STATA software package version 14. Efforts were made to combine stochastic frontier modeling with impact evaluation methodology using the Heckman (1979) two-stage model to deal with possible selectivity bias arising from unobservable characteristics in the stochastic frontier model. Results indicate that farmers in the two groups are not efficient and operate below their potential frontiers i.e., there is a potential to increase crop productivity through efficiency improvements in both groups. In addition, the empirical results revealed selection bias in both groups of farmers confirming the justification for the use of selection bias corrected stochastic frontier model. It was also found that intervention farmers achieved higher technical efficiency scores than the control group of farmers. Furthermore, the selectivity bias-corrected model showed a different technical efficiency score for the intervention farmers while it more or less remained the same for that of control group farmers. However, the control group of farmers shows a higher dispersion as measured by the coefficient of variation compared to the intervention counterparts. Among the explanatory variables, the study found that farmer’s age (proxy to farm experience), land certification, frequency of visit to improved seed center, farmer’s education and row planting are important contributing factors for participation decisions and hence technical efficiency of farmers in the study areas. We recommend that policies targeting the design of development intervention programs in the agricultural sector focus more on providing farmers with on-farm visits by extension workers, provision of credit services, establishment of farmers’ training centers and adoption of modern farm technologies. Finally, we recommend further research to deal with this kind of methodological framework using a panel data set to test whether technical efficiency starts to increase or decrease with the length of time that farmers participate in development programs.

Keywords: impact evaluation, efficiency analysis and selection bias, stochastic frontier model, Heckman-two step

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Authors: Ahmed Abdulfatah Yusuf, Collins Oduor Ondiek

Abstract:

The automation of farming activities can have a transformational impact on the agricultural sector, especially from the emerging new technologies such as the Internet of Things (IoT). The system uses water level sensors and soil moisture sensors that measure the content of water in the soil as the values generated from the sensors enable the system to use an appropriate quantity of water, which avoids over or under irrigation. Due to the increase in the world’s population, there is a need to increase food production. With this demand in place, it is difficult to increase crop yield using the traditional manual approaches that lead to the wastage of water, thus affecting crop production. Food insecurity has become a scourge greatly affecting the developing countries and agriculture is an essential part of human life and tends to be the mainstay of the economy in most developing nations. Thus, without the provision of adequate food supplies, the population of those living in poverty is likely to multiply. The project’s main objective is to design and develop an IoT (Internet of Things) microcontroller-based Smart Irrigation System. In addition, the specific research objectives are to find out the challenges with traditional irrigation approaches and to determine the benefits of IoT-based smart irrigation systems. Furthermore, the system includes Arduino, a website and a database that works simultaneously in collecting and storing the data. The system is designed to pave the way in attaining the Sustainable Development Goal (SDG 1), which aims to end extreme poverty in all forms by 2030. The research design aimed at this project is a descriptive research design. Data was gathered through online questionnaires that used both quantitative and qualitative in order to triangulate the data. Out of the 32 questionnaires sent, there were 32 responses leading to a 100% response rate. In terms of sampling, the target group of this project is urban farmers, which account for about 25% of the population of Nairobi. From the findings of the research carried out, it is evident that there is a need to move away from manual irrigation approaches due to the high wastage of water to the use of smart irrigation systems that propose a better way of conserving water while maintaining the quality and moisture of the soil. The research also found out that urban farmers are willing to adopt this system to better their farming practices. However, this system can be improved in the future by incorporating it with other features and deploying it to a larger geographical area.

Keywords: crop production, food security, smart irrigation system, sustainable development goal

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Authors: Rakesh Kumar Mishra, Tarun Kumar Dan

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Lead-Lag based Internal Model Control method is proposed based on Internal Model Control (IMC) strategy. In this paper, we have designed the Lead-Lag based Internal Model Control for binary distillation column for SISO process (considering only bottom product). The transfer function has been taken from Wood and Berry model. We have find the composition control and disturbance rejection using Lead-Lag based IMC and comparing with the response of simple Internal Model Controller.

Keywords: SISO, lead-lag, internal model control, wood and berry, distillation column

Procedia PDF Downloads 647