scholarly journals Applying Dickson Quality Index, Chlorophyll Fluorescence, and Leaf Area Index for Assessing Plant Quality of Pentas lanceolata

2018 ◽  
Vol 47 (1) ◽  
pp. 169-176 ◽  
Author(s):  
Kuan-Hung LIN ◽  
Chun-Wei WU ◽  
Yu-Sen CHANG
Keyword(s):  
Chlorophyll Fluorescence ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Quality Index ◽  
Star Cluster ◽  
Plant Quality ◽  
Area Index ◽  
Multiple Variables ◽  
Pentas Lanceolata ◽  
Dickson Quality Index

Plant quality greatly relates to the seedling vigor (SV), survival and growth of plants after transplantation. The objective of this study was to use the nondestructive measurements of chlorophyll fluorescence (ChlF) and leaf area index (LAI) as SV indices for star cluster (Pentas lanceolata). Plants were grown in potting soil under nature sunlight for 90 d. A total of 13 morphological and physiological parameters were selected for measurements. Among them, root growth potential (RGP) was the best predictor for SV in all tested plants. Plants were separated into 5 RGP groups based on the number of new roots, and remaining parameters were also separated into those same levels. The trends and rates of increase from levels 1 to 5 in Dickson quality index (DQI), LAI, total dry mass, and ChlF were all similar to the RGP index. Although RGP and DQI are frequently used as indices for SV, these measurements are time-consuming and require sample destruction. Consistent and strongly high correlations were observed among DQI, LAI, and ChlF, demonstrating the applicability of these indices for measuring SV in star cluster. The measurements of LAI and ChlF were predicted using multiple variables from validation datasets, and showed novel and useful parameters for examining the SV of star cluster.

scholarly journals GROWTH AND PHYSIOLOGY OF Balfourodendron riedelianum SEEDLINGS IN THE NURSERY AND IN THE FIELD

FLORESTA ◽  
2019 ◽  
Vol 49 (4) ◽  
pp. 763
Author(s):  
Felipe Turchetto ◽  
Jessé Caletti Mezzomo ◽  
Maristela Machado Araujo ◽  
Adriana Maria Griebeler ◽  
Álvaro Luís Pasquetti Berghetti ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Controlled Release ◽  
Leaf Area ◽  
Quality Index ◽  
Dry Mass ◽  
Positive Influence ◽  
Total Root Length ◽  
Plastic Bags ◽  
Chlorophyll Index ◽  
Dickson Quality Index

The present study aimed to determine the effect of different container volumes and doses of controlled release fertilizer (CRF) on the morphophysiological aspects of Balfourodendron riedelianum seedlings in the nursery and verify if these responses were replicated in the field. For the production of seedlings in nursery, three container volumes (180 and 280 cm³ polypropylene tubes and 500 cm³ plastic bags) and four doses of CRF (0, 4, 8, and 12 g L-1 of substrate) were tested, and the seedlings were grown for 240 days. At the end of the nursery period, the following parameters were measured: height (H); stem diameter (SD); dry mass of shoot, root, and total; root length; leaf area; and chlorophyll fluorescence. The H/SD ratio and the Dickson Quality Index were calculated. The same treatments were evaluated in the field at 540 days after planting. Survival, height, and diameter increase, aerial dry mass, leaf area, chlorophyll a fluorescence and chlorophyll index (a, b and total) were measured. Basic fertilization using CRF had a positive influence on the production of B. riedelianum seedlings. It is recommended to use a 180 cm³ tube and a dose of 12 g L-1 CRF for the production of seedlings. The results obtained in the nursery for the production of seedlings were confirmed to occur in the field.

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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