Modeling of transpiration of paprika (Capsicum annuum L.) plants based on radiation and leaf area index in soilless culture

2011 ◽  
Vol 52 (3) ◽  
pp. 265-269 ◽  
Author(s):  
The Hung Ta ◽  
Jong Hwa Shin ◽  
Tae In Ahn ◽  
Jung Eek Son
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Capsicum Annuum ◽  
Soilless Culture ◽  
Area Index ◽  
Capsicum Annuum L

Response of red chilli (Capsicum annuum L.) to fertigation and mulching

2017 ◽  
Vol 2 (02) ◽  
pp. 169-173
Author(s):  
G. Chandramohan Reddy ◽  
S. S. Hebbar
Keyword(s):  
Leaf Area ◽  
Capsicum Annuum ◽  
Growth Parameters ◽  
Fruit Yield ◽  
Water Soluble ◽  
Growth And Yield ◽  
Index Number ◽  
Area Index ◽  
Capsicum Annuum L ◽  
Dry Fruit

Experiments were conducted to evaluate the performance of red chilli (Capsicum annuum L.) during 2015-16 at the Division of vegetable crops, Indian Institute of Horticulture Research, Hessaraghatta, Bangalore to determine the effect of different fertigation sources and mulching on growth parameters, yield and fertilizer use efficiency (FUE). Fertigation was done both water soluble fertilizers and normal fertilizers with different doses. The results revealed that significantly higher growth and yield parameters viz., plant height (cm), number of branches per plant, leaf area and leaf area index, number of fruits per plant, length of the fruit (cm), girth of the fruit (cm), fruit weight (g) dry fruit yield per plant (g), dry fruit yield per hectare (t) were observed by the treatments viz., application of water soluble fertilizers 100 per cent (Recommended dose of fertilizers) RDF using urea, 19:19:19 and KNO3 through fertigation with mulching, followed by Normal fertilizers 100 per cent RDF using Urea, DAP, MOP through fertigation with mulching. From this investigation it is concluded that water soluble fertilizers as well as normal fertilizers fertigation with mulching ideal for maximum growth and yield of the chilli crop.

scholarly journals Growth and Photosynthetic Response of Capsicum annuum L. in Biochar Amended Soil

2020 ◽  
Vol 10 (12) ◽  
pp. 4111 ◽  
Author(s):  
Klára Pokovai ◽  
Eszter Tóth ◽  
Ágota Horel
Keyword(s):  
Leaf Area ◽  
Plant Height ◽  
Capsicum Annuum ◽  
Environmental Changes ◽  
Fruit Yield ◽  
Higher Plant ◽  
Area Index ◽  
Capsicum Annuum L ◽  
Loam Soil ◽  
Different Doses

The present study investigated the growth of Capsicum annuum L. (pepper) in an outdoor pot experiment. Changes in the plants’ aboveground and root biomass, leaf area, plant height, stem thickness, and yield, as a response to different doses of biochar amendments were observed. During the 12.5-week-long study, four treatments with biochar amounts of 0, 0.5%, 2.5%, and 5.0% (by weight) were added to silt loam soil. Photochemical responses of plants, the plants photochemical reflectance index (PRI) modified by the different doses of biochar were continuously monitored. Plant height and fruit yield were initially the highest for BC5.0; however, by the end of the experiment, both parameters showed higher values for BC2.5, e.g., 15.9 and 9.1% higher plant height and 32.5 and 22.6% higher fruit yield for BC2.5 and BC5.0 compared to control, respectively. By the end of the experiment the BC2.5 treatments had significantly higher stem thickness (p < 0.001) compared to all other amendments. Root dry matter in biochar treatments increased relative to controls with the highest values (54.9% increase) observed in the BC2.5 treatment. Biochar treatment increased leaf area index (LAI) values for the higher doses (1.58, 1.59, 2.03, and 1.89 for C, BC0.5, BC2.5, and BC5.0, respectively). Significant differences between control and biochar amended soils’ PRI measurements were observed (p < 0.001), showing less plant sensitivity to environmental changes when biochar was applied to the soil. While biochar amendment could greatly enhance plant growth and development, there is an optimal amount of biochar after which additional amount might not result in substantial differences, or even can result in lower fruit yield as found in the present study.

Estimation of Forest Leaf Area Index from Remote Sensing Data Using the Algorithm Based on Geometric-optical Model

2012 ◽  
Vol 14 (3) ◽  
pp. 358-365
Author(s):  
Hanyue CHEN ◽  
Wenjiang HUANG ◽  
Zheng NIU ◽  
Shuai GAO
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Optical Model ◽  
Remote Sensing Data ◽  
Area Index ◽  
Sensing Data

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

Hyperspectral characteristics and leaf area index monitoring of rice (Oryza sativa L.) under carbon dioxide concentration enrichment

2021 ◽  
Vol 54 (3) ◽  
pp. 231-243
Author(s):  
Chao Liu ◽  
Zhenghua Hu ◽  
Rui Kong ◽  
Lingfei Yu ◽  
Yuanyuan Wang ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Oryza Sativa ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Oryza Sativa L ◽  
Carbon Dioxide Concentration ◽  
Area Index

scholarly journals Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards

Agriculture ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 208
Author(s):  
Daniel Queirós da Silva ◽  
André Silva Aguiar ◽  
Filipe Neves dos Santos ◽  
Armando Jorge Sousa ◽  
Danilo Rabino ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Optical Sensors ◽  
Precision Agriculture ◽  
Real Data ◽  
Relevant Information ◽  
Normal Operation ◽  
Area Index ◽  
Sensory Data ◽  
The Right

Smart and precision agriculture concepts require that the farmer measures all relevant variables in a continuous way and processes this information in order to build better prescription maps and to predict crop yield. These maps feed machinery with variable rate technology to apply the correct amount of products in the right time and place, to improve farm profitability. One of the most relevant information to estimate the farm yield is the Leaf Area Index. Traditionally, this index can be obtained from manual measurements or from aerial imagery: the former is time consuming and the latter requires the use of drones or aerial services. This work presents an optical sensing-based hardware module that can be attached to existing autonomous or guided terrestrial vehicles. During the normal operation, the module collects periodic geo-referenced monocular images and laser data. With that data a suggested processing pipeline, based on open-source software and composed by Structure from Motion, Multi-View Stereo and point cloud registration stages, can extract Leaf Area Index and other crop-related features. Additionally, in this work, a benchmark of software tools is made. The hardware module and pipeline were validated considering real data acquired in two vineyards—Portugal and Italy. A dataset with sensory data collected by the module was made publicly available. Results demonstrated that: the system provides reliable and precise data on the surrounding environment and the pipeline is capable of computing volume and occupancy area from the acquired data.

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