scholarly journals A Survey on Rice Crop Yield Prediction in India Using Improved Classification Technique

2019 ◽  
pp. 501-507
Author(s):  
Kolin Sukhadia ◽  
M. B. Chaudhari
Keyword(s):  
Crop Yield ◽  
Crop Production ◽  
Potential Evapotranspiration ◽  
Environmental Parameters ◽  
Rice Crop ◽  
Maximum Temperature ◽  
Support Vector ◽  
Pressure Potential ◽  
Average Temperature ◽  
Reference Crop

<p>India is an agricultural country. Agriculture is the important contributor to the Indian economy. There are many classification techniques like Support Vector Machine(SVM), LADTree, Natve Bayes, Bayesnet, K Nearest Neighbour(KNN), Locally Weighted Learning(LWL) on rice crop production datasets. They have some drawbacks like low accuracy and more errors. To achieve more significant result, To increase classification accuracy and reducing classification errors, our research uses classification method Bayesnet based adaboost will be proposed in work. Rice crop yield depend on environment's parameters like Rainfall, minimum temperature, average temperature, Maximum temperature, Vapour Pressure, potential evapotranspiration, reference crop evapotranspiration, cloud cover, wet day frequency for the kharif season. our dataset containing these environmental parameters for accurate prediction of Rice crop yield.</p>

scholarly journals Horse Transport to Three South American Horse Slaughterhouses: A Descriptive Study

Animals ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 602 ◽  
Author(s):  
Béke Nivelle ◽  
Liesbeth Vermeulen ◽  
Sanne Van Beirendonck ◽  
Jos Van Thielen ◽  
Bert Driessen
Keyword(s):  
Relative Humidity ◽  
Ambient Temperature ◽  
Average Distance ◽  
Stocking Density ◽  
Environmental Parameters ◽  
Descriptive Study ◽  
Maximum Temperature ◽  
South American ◽  
Average Temperature ◽  
Humidity Index

Between November 2016 and October 2017, 23 horse transports from 18 collection points to two slaughterhouses in Argentina and one in Uruguay were monitored. The goal of this study was to characterize the current practices in commercial horse transports and to detect potential threats to horse welfare. A total of 596 horses were transported over an average distance of 295 ± 250 km. Average transport duration was 294 ± 153 min. The infrastructure did not always promote smooth loading, but the amount of horses that refused to enter the trailers was limited. In each loading space, a camera was mounted to observe horse behaviour during the journey. Ambient temperature and relative humidity (RH) were recorded every five minutes in each loading space. In 14 of the 23 transports, the maximum temperature rose above 25 °C and the average temperature was over 25 °C during six transports. The average temperature humidity index (THI) exceeded 72 during six transports. The average stocking density was 1.40 ± 0.33 m2 per horse, or 308 ± 53 kg/m2. The degree of aggression differed between the front and rear loading space. Stocking density, environmental parameters, trailer characteristics, and transport duration and distance did not influence aggressiveness.

scholarly journals Crop yield prediction integrating genotype and weather variables using deep learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252402
Author(s):  
Johnathon Shook ◽  
Tryambak Gangopadhyay ◽  
Linjiang Wu ◽  
Baskar Ganapathysubramanian ◽  
Soumik Sarkar ◽  
...  
Keyword(s):  
Crop Yield ◽  
Crop Production ◽  
Short Term Memory ◽  
Time Windows ◽  
Climatic Conditions ◽  
Support Vector ◽  
Yield Prediction ◽  
Lasso Regression ◽  
Important Time ◽  
Interpretable Models

Accurate prediction of crop yield supported by scientific and domain-relevant insights, is useful to improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop production. We used performance records from Uniform Soybean Tests (UST) in North America to build a Long Short Term Memory (LSTM)—Recurrent Neural Network based model that leveraged pedigree relatedness measures along with weekly weather parameters to dissect and predict genotype response in multiple-environments. Our proposed models outperformed other competing machine learning models such as Support Vector Regression with Radial Basis Function kernel (SVR-RBF), least absolute shrinkage and selection operator (LASSO) regression and the data-driven USDA model for yield prediction. Additionally, for providing interpretability of the important time-windows in the growing season, we developed a temporal attention mechanism for LSTM models. The outputs of such interpretable models could provide valuable insights to plant breeders.

scholarly journals Evaluation of Machine Learning Algorithms for Crop Yield Prediction

2019 ◽  
Vol 8 (6) ◽  
pp. 4082-4086 ◽  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Crop Yield ◽  
Crop Production ◽  
Crop Yields ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Training Dataset ◽  
Support Vector ◽  
K Nearest Neighbor

Agriculture plays a significant role in the growth of the national economy. It relay on weather and other environmental aspects. Some of the factors on which agriculture is dependent are Soil, climate, flooding, fertilizers, temperature, precipitation, crops, insecticides and herb. The crop yield is dependent on these factors and hence difficult to predict. To know the status of crop production, in this work we perform descriptive study on agricultural data using various machine learning techniques. Crop yield estimates include estimating crop yields from available historical data such as precipitation data, soil data, and historic crop yields. This prediction will help farmers to predict crop yield before farming. Here we are utilizing three datasets like as clay dataset, precipitation dataset, and production dataset of Karnataka state, then we structure an assembled data sets and on this dataset we employ three different algorithms to get the genuine assessed yield and the precision of three different methods. K-Nearest Neighbor(KNN), Support Vector Machine(SVM), and Decision tree algorithms are applied on the training dataset and are tested with the test dataset, and the implementation of these algorithms is done using python programming and spyder tool. The performance comparison of algorithms is shown using mean absolute error, cross validation and accuracy and it is found that Decision tree is giving accuracy of 99% with very less mean square error(MSE). The proposed model can exhibit the precise expense of assessed crop yield and it is mark like as LOW, MID, and HIGH.

scholarly journals Crop Yield Prediction

2021 ◽  
pp. 561-569
Author(s):  
Pallavi Shankarrao Mahore ◽  
Dr. Aashish A. Bardekar
Keyword(s):  
Crop Yield ◽  
Soil Type ◽  
Rural Economy ◽  
Nearest Neighbor ◽  
Maximum Yield ◽  
Weather Condition ◽  
Maximum Temperature ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Weather Parameters

Cotton, popularly known as White Gold has been an important commercial crop of National significance due to the immense influence of its rural economy. Transfer of technology to identify the quality of fibre is gaining importance for crop yield is compared with Random forest, Support Vector Machine, Weather, K Nearest neighbor. , which shows better performance results for each selected weather parameters. Crop yield rate depends upon various parameters such as the geography of area, soil type, soil nutrients, soil alkaline, weather condition, etc. The combination of these parameters can be used for selection of suitable crops for a farm or land to gain maximum yield. In this manuscript, soil and weather parameters such as soil type, soil fertility, maximum temperature, minimum temperature, rainfall are used to identify suitable crops for specified farm or land.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

scholarly journals Experimental Study on Infrared Temperature Characteristics and Failure Modes of Marble with Prefabricated Holes under Uniaxial Compression

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 713
Author(s):  
Yanyan Peng ◽  
Qunchao Lin ◽  
Manchao He ◽  
Chun Zhu ◽  
Haijiang Zhang ◽  
...  
Keyword(s):  
Uniaxial Compression ◽  
Failure Modes ◽  
Rock Sample ◽  
Vertical Axis ◽  
Maximum Temperature ◽  
Thermal Imager ◽  
Infrared Thermal Imaging ◽  
Average Temperature ◽  
Observation Area ◽  
Evolution Behavior

In rock engineering, it is of great significance to study the failure mechanical behavior of rocks with holes. Using a combination of experiment and infrared detection, the strength, deformation, and infrared temperature evolution behavior of marble with elliptical holes under uniaxial compression were studied. The test results showed that as the vertical axis b of the ellipse increased, the peak intensity first decreased and then increased, and the minimum value appeared when the horizontal axis was equal to the vertical axis. The detection results of the infrared thermal imager showed that the maximum temperature, minimum temperature, and average temperature of the observation area in the loading stage showed a downward trend, and the range of change was between 0.02 °C and 1 °C. It was mainly due to the accumulation of energy in the loading process of the rock sample that caused the surface temperature of the specimen to decrease. In the brittle failure stage, macroscopic cracks appeared on the surface of the rock sample, which caused the energy accumulated inside to dissipate, thereby increasing the maximum temperature and average temperature of the rock sample. The average temperature increase was about 0.05 °C to about 0.19 °C. The evolution of infrared temperature was consistent with the mechanical characteristics of rock sample failure, indicating that infrared thermal imaging technology can provide effective monitoring for the study of rock mechanics. The research in this paper provides new ideas for further research on the basic characteristics of rock failure under uniaxial compression.

scholarly journals Agro-Nanotechnology as an Emerging Field: A Novel Sustainable Approach for Improving Plant Growth by Reducing Biotic Stress

2021 ◽  
Vol 11 (5) ◽  
pp. 2282
Author(s):  
Masudulla Khan ◽  
Azhar U. Khan ◽  
Mohd Abul Hasan ◽  
Krishna Kumar Yadav ◽  
Marina M. C. Pinto ◽  
...  
Keyword(s):  
Plant Growth ◽  
Crop Yield ◽  
Biotic Stress ◽  
Crop Production ◽  
Plant Disease ◽  
Yield Loss ◽  
Growth Parameters ◽  
Plant Diseases ◽  
High Yield ◽  
Plant Disease Management

In the present era, the global need for food is increasing rapidly; nanomaterials are a useful tool for improving crop production and yield. The application of nanomaterials can improve plant growth parameters. Biotic stress is induced by many microbes in crops and causes disease and high yield loss. Every year, approximately 20–40% of crop yield is lost due to plant diseases caused by various pests and pathogens. Current plant disease or biotic stress management mainly relies on toxic fungicides and pesticides that are potentially harmful to the environment. Nanotechnology emerged as an alternative for the sustainable and eco-friendly management of biotic stress induced by pests and pathogens on crops. In this review article, we assess the role and impact of different nanoparticles in plant disease management, and this review explores the direction in which nanoparticles can be utilized for improving plant growth and crop yield.

scholarly journals How Can We Realize Sustainable Development Goals in Rocky Desertified Regions by Enhancing Crop Yield with Reduction of Environmental Risks?

2021 ◽  
Vol 13 (9) ◽  
pp. 1614
Author(s):  
Boyi Liang ◽  
Timothy A. Quine ◽  
Hongyan Liu ◽  
Elizabeth L. Cressey ◽  
Ian Bateman
Keyword(s):  
Sustainable Development ◽  
Crop Yield ◽  
Crop Production ◽  
Sustainable Development Goals ◽  
Positive Impact ◽  
Total Yield ◽  
Maximum Yield ◽  
Guizhou Province ◽  
Crop Species ◽  
Development Goals

To meet the sustainable development goals in rocky desertified regions like Guizhou Province in China, we should maximize the crop yield with minimal environmental costs. In this study, we first calculated the yield gap for 6 main crop species in Guizhou Province and evaluated the quantitative relationships between crop yield and influencing variables utilizing ensembled artificial neural networks. We also tested the influence of adjusting the quantity of local fertilization and irrigation on crop production in Guizhou Province. Results showed that the total yield of the selected crops had, on average, reached over 72.5% of the theoretical maximum yield. Increasing irrigation tended to be more consistently effective at increasing crop yield than additional fertilization. Conversely, appropriate reduction of fertilization may even benefit crop yield in some regions, simultaneously resulting in significantly higher fertilization efficiency with lower residuals in the environment. The total positive impact of continuous intensification of irrigation and fertilization on most crop species was limited. Therefore, local stakeholders are advised to consider other agricultural management measures to improve crop yield in this region.

scholarly journals Seed Set Patterns in East African Highland Cooking Bananas Are Dependent on Weather before, during and after Pollination

Horticulturae ◽  
2021 ◽  
Vol 7 (7) ◽  
pp. 165
Author(s):  
Allan Waniale ◽  
Rony Swennen ◽  
Settumba B. Mukasa ◽  
Arthur K. Tugume ◽  
Jerome Kubiriba ◽  
...  
Keyword(s):  
Critical Period ◽  
Floral Development ◽  
Seed Set ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Maximum Temperature ◽  
Fruit Pulp ◽  
East African ◽  
Average Temperature ◽  
Wild Banana

Seed set in banana is influenced by weather, yet the key weather attributes and the critical period of influence are unknown. We therefore investigated the influence of weather during floral development for a better perspective of seed set increase. Three East African highland cooking bananas (EAHBs) were pollinated with pollen fertile wild banana ‘Calcutta 4′. At full maturity, bunches were harvested, ripened, and seeds extracted from fruit pulp. Pearson’s correlation analysis was then conducted between seed set per 100 fruits per bunch and weather attributes at 15-day intervals from 105 days before pollination (DBP) to 120 days after pollination (DAP). Seed set was positively correlated with average temperature (P < 0.05–P < 0.001, r = 0.196–0.487) and negatively correlated with relative humidity (RH) (P < 0.05–P < 0.001, r = −0.158–−0.438) between 75 DBP and the time of pollination. After pollination, average temperature was negatively correlated with seed set in ‘Mshale’ and ‘Nshonowa’ from 45 to 120 DAP (P < 0.05–P < 0.001, r = −0.213–−0.340). Correlation coefficients were highest at 15 DBP for ‘Mshale’ and ‘Nshonowa’, whereas for ‘Enzirabahima’, the highest were at the time of pollination. Maximum temperature as revealed by principal component analysis at the time of pollination should be the main focus for seed set increase.

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