scholarly journals Aerial high-throughput phenotyping of peanut leaf area index and lateral growth

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sayantan Sarkar ◽  
Alexandre-Brice Cazenave ◽  
Joseph Oakes ◽  
David McCall ◽  
Wade Thomason ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
High Throughput ◽  
Lateral Growth ◽  
Area Index ◽  
Pigment Ratio ◽  
Leaf Reflectance ◽  
Pod Yield ◽  
Plant Pigment ◽  
High Throughput Phenotyping

AbstractLeaf area index (LAI) is the ratio of the total one-sided leaf area to the ground area, whereas lateral growth (LG) is the measure of canopy expansion. They are indicators for light capture, plant growth, and yield. Although LAI and LG can be directly measured, this is time consuming. Healthy leaves absorb in the blue and red, and reflect in the green regions of the electromagnetic spectrum. Aerial high-throughput phenotyping (HTP) may enable rapid acquisition of LAI and LG from leaf reflectance in these regions. In this paper, we report novel models to estimate peanut (Arachis hypogaea L.) LAI and LG from vegetation indices (VIs) derived relatively fast and inexpensively from the red, green, and blue (RGB) leaf reflectance collected with an unmanned aerial vehicle (UAV). In addition, we evaluate the models’ suitability to identify phenotypic variation for LAI and LG and predict pod yield from early season estimated LAI and LG. The study included 18 peanut genotypes for model training in 2017, and 8 genotypes for model validation in 2019. The VIs included the blue green index (BGI), red-green ratio (RGR), normalized plant pigment ratio (NPPR), normalized green red difference index (NGRDI), normalized chlorophyll pigment index (NCPI), and plant pigment ratio (PPR). The models used multiple linear and artificial neural network (ANN) regression, and their predictive accuracy ranged from 84 to 97%, depending on the VIs combinations used in the models. The results concluded that the new models were time- and cost-effective for estimation of LAI and LG, and accessible for use in phenotypic selection of peanuts with desirable LAI, LG and pod yield.

scholarly journals A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

Agronomy ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 175 ◽  
Author(s):  
Orly Enrique Apolo-Apolo ◽  
Manuel Pérez-Ruiz ◽  
Jorge Martínez-Guanter ◽  
Gregorio Egea
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
High Throughput ◽  
Model Performance ◽  
Wheat Breeding ◽  
Plant Phenotyping ◽  
Indirect Methods ◽  
Area Index ◽  
Model Based ◽  
Recent Emergence

Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model.

Effect of Sowing Spacing on Vegetative Growth, Dry Matter Production, and Peanut Pod Yield

1998 ◽  
Vol 25 (2) ◽  
pp. 86-87 ◽  
Author(s):  
O. Giayetto ◽  
G. A. Cerioni ◽  
W. E. Asnal
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetative Growth ◽  
Dry Matter ◽  
Block Design ◽  
Individual Plant ◽  
Dry Matter Distribution ◽  
Area Index ◽  
Pod Yield ◽  
Number Of Branches

Abstract Peanut growth and pod yield are influenced by sowing spacing and plant density. Production and distribution of dry matter on peanut cultivars sown in different spacings and densities and their relationships with pod and kernel yields were assessed. The factors evaluated were two cultivars (Florman INTA, virginia-type “runner” and Colorado Irradiado, valencia-type erect), three interrow spacings (IRS) (0.70, 0.50, and 0.30 m) and two interplant spacings (IS) (0.06 and 0.12 m). The 12 treatments were disposed in a factorial arrangement of 2×3×2 and a randomized block design with three replications. Weeds were controlled with Imazetapir (100 g ai/ha) and also hand weeded while leaf spot control was done with Fluzilasole (60 g ai/ha). Sowing spacings did not affect phenologic stage duration, but the differences observed were due to the cultivar. Vegetative growth was sensitive to spacings effect. At an individual plant level, dry matter and leaf area decreased significantly because of the greater intraspecific competition produced by the shortening of distances between rows (from 0.70 to 0.30 m) and between plants (from 0.12 to 0.06 m) and the corresponding density increase from 12 to 56 plants/m2. However, at a population level, most compact spacings produced more dry matter per surface and leaf area index. This also is related to the lesser time required for plants at these spacings to achieve a radiation interception higher than 90%. Dry matter distribution did not vary with sowing spacings. The number of branches per plant was reduced with the increase of density. The effect was greater in the late maturing cultivar. The most compact sowing spacings (0.30×0.06, 0.50×0.06 and 0.30×0.12 m) produced higher pod and kernel yield/ha than those less dense. This response is based upon the significant correlations between the dry matter and number of branches per surface area, and leaf area index and pod yield.

scholarly journals Integrated High-Throughput Phenotyping with High Resolution Multispectral, Hyperspectral and 3D Point Cloud Techniques for Screening Wheat Genotypes on Sodic Soils

Proceedings ◽  
2020 ◽  
Vol 36 (1) ◽  
pp. 206
Author(s):  
Malini Roy Choudhury ◽  
Jack Christopher ◽  
Armando Apan ◽  
Scott Chapman ◽  
Neal Menzies ◽  
...  
Keyword(s):  
Leaf Area ◽  
High Throughput ◽  
Point Cloud ◽  
Vegetation Index ◽  
Partial Least Square ◽  
3D Point Cloud ◽  
Sodic Soils ◽  
Area Index ◽  
Wheat Genotypes ◽  
High Throughput Phenotyping

Wheat production in southern Queensland, Australia is adversely affected by soil sodicity. Crop phenotyping could be useful to improve productivity in such soils. This research focused on adapting high-throughput phenotyping of crop biophysical attributes to monitor crop health, nutrient deficiencies and plant moisture availability. We conducted an aerial and ground-based campaign during several wheat growing stages to capture crop information for 18 wheat genotypes at a moderately sodic site near Goondiwindi in southern Queensland. Three techniques were employed (multispectral, hyperspectral, and 3D point cloud) to monitor crop characteristics and predict biomass and yield. Spectral information and vegetation indices (VI) such as, normalized different vegetation index (NDVI), modified soil adjusted vegetation index (MSAVI), and leaf area index (LAI) were derived from multispectral imagery and compared with ground-based agronomic data for biomass, leaf area, and yield. Significant correlations were observed between NDVI and yield (R2 = 0.81), LAI (R2 = 0.74), and biomass (R2 = 0.65). Partial least square regression (PLS-R) modelling using hyperspectral spectroscopy data provided crop yield predictions that correlated significantly with observed yield (R2 = 0.65). The 3D point cloud technique was effective with comparison to in field manual measurements of crop architectural traits height and foliage cover (e.g., for height R2 = 0.73). For, this study multispectral techniques showed a greater potential to predict biomass and yield of wheat genotypes under moderately sodic soils than hyperspectral and 3D point cloud techniques. In future, the genotypes will be tested under more severely sodic soils to monitor crop performance and predicting yield.

scholarly journals Use of PNUTGRO model for optimization of sowing date and plant spacing to maximize yield of groundnut (Arachis hypogaea L.)

MAUSAM ◽  
2021 ◽  
Vol 57 (2) ◽  
pp. 307-314
Author(s):  
R. P. SAMUI ◽  
R. BALASUBRAMANIAN ◽  
P. S. KULKARNI ◽  
A. M. SHEKH ◽  
PIARA SINGH
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Field Experiments ◽  
Sowing Date ◽  
Area Index ◽  
Pod Yield ◽  
Arachis Hypogaea L ◽  
Growth Development ◽  
Normal Spacing ◽  
Satisfactory Prediction

Lkkj & ihuVxzks ¼ih- ,u- ;w- Vh- th- vkj- vks-½ ekWMy dh izkekf.kdrk fl) djus ds fy, 1987 - 90 ds nkSjku vkuan] xqtjkr esa {ks=h; iz;ksx fd, x, gSaA bl ekWMy dk mi;ksx ew¡xQyh dh QhuksykWth] c<+ksrjh] fodkl vkSj iSnkokj dk iwokZuqeku yxkus ds fy, fd;k x;k gSSA ew¡xQyh ds izfr:fir iq"iu] isfxax] Qyh cuus vkSj Qyh idus dh frfFk;ksa] i.khZ {ks=Qy lwpdkad  ¼,y- ,- vkbZ-½ tSoHkkj] 'kSfyax dk izfr’kr rFkk iSnkokj dh rqyuk rhu i)fr;ksa uker% th- ,- ;w- th- 10] th- ,- ;w- th- 2 vkSj vkj- vk-sa - 33 - 1 ls izkIr gq, iszf{kr ekuksa ds lkFk dh xbZ gSA izfr:fir ?kVukØe ls iq"iu  ds fy, ,d fnu deh rFkk ik¡p fnu dh c<+r dk] isfxxa ds fy, 2 ls 6 fnuksa dh c<+r] Qyh cuus ds fy, 3 fnu dh deh rFkk 6   fnuksa dh c<+r dk vkSj Qyh idus ds fy, 6 fnu dh deh rFkk 5 fnu rd dh c<+r dk varj ik;k x;k gSA okLrfod ekuksa dh rqyuk esa bl ekWMy ls i.khZ {ks=Qy lwpdkad 91-8 ls 105-8 izfr’kr vkSj 'kSfyax dk izfr’kr 81-5 ls 109-8 ik;k x;k gSA bl ekWMy ls ew¡xQyh dh iSnkokj izsf{kr ekuksa dh rqyuk esa 88-5 ls 112-7 izfr’kr rd ikbZ xbZ gSA bl ekWMy ls izkIr ifj.kkeksa ds vk/kkj ij ij yxkrkj  pkj Qlyksa vkSj _rqvksa ds laca/k esa ew¡xQyh  dh QhuksYkWkth] c<+ksrjh] fodkl vkSj iSnkokj ds ckjs  esa iwokZuqeku larks"ktud ik;k x;k gSA ew¡xQyh dh izsf{kr vkSj izfr:fir iSnkokj ds chp 11 izfr’kr dh ?kVc<+ ikbZ xbZ gS ftlls irk pyrk gS fd ekWMy ds vk/kkj ij fd;k x;k iwokZuqeku larks"ktud gSA ,y- ,- vkbZ- dks NksMdj okLrfod ekuksa vkSj izsf{kr ekuksa esa varj ¼Mh-½ 0-03 vkSj 1-77 ds chp jgk gS ftlls ekWMy ds larks"ktud dk;Z djus dk irk pyrk gSA izfr:i.k v/;;uksa ds ifj.kkeksa ls irk pyrk gS fd tc vf/kd o"kkZ gksus dhs laHkkouk gks rks ew¡xQyh ds chtksa dh lkekU; nwjh rFkk cqokbZ ds lkekU; le; dh vis{kk chtksa dks vf/kd ikl&ikl cksdj rFkk cqokbZ yxHkx ,d lIrkg igys djds ew¡xQyh dh vf/kd iSnkokj  izkIr dh tk ldrh gSA  Field experiments were conducted at Anand, Gujarat during 1987-90 to validate the PNUTGRO model. The model was used to predict phenology, growth, development and yield of groundnut. The simulated flowering, pegging, pod formation and pod maturity dates, leaf area index (LAI), biomass, shelling % and pod yield of groundnut were compared with the observed values for three cultivars viz., GAUG 10, GAUG 2 and Ro-33-1. The simulated phenological events showed a deviation of –1 to +5 days for flowering, +2 to +6 days for peg formation, -3 to +6 days for pod formation and –6 to +5 days for pod maturity of the crop. The model estimated leaf area index within 91.8 to 105.8% and shelling percentage within 81.5 to 109.8% of the actual values. The model simulated the pod yields within 88.5 to 112.7% of the observed values. The results obtained with the model for the four consecutive crops and seasons revealed satisfactory prediction of phenology, growth, development and yield of groundnut. The percent error between observed and simulated pod yield was 11% which indicated satisfactory prediction by the model. The degree of agreement (d) ranged between 0.03 and 1.77 except for LAI indicating satisfactory performance of the model. Results of simulation studies indicated that when there is a possibility of high rainfall higher pod yield can be achieved by adopting closer spacing and early sowing (one week earlier than normal date of sowing) compared to normal spacing and date of sowing.

Effect of Sowing Date on Groundnuts in Sudan Gezira

1978 ◽  
Vol 14 (1) ◽  
pp. 13-16 ◽  
Author(s):  
Idris M. Nur ◽  
Ali A. E. Gasim
Keyword(s):  
Leaf Area Index ◽  
Iodine Number ◽  
Leaf Area ◽  
Oil Content ◽  
Sowing Date ◽  
Area Index ◽  
Pod Yield ◽  
Sudan Gezira

SUMMARYA 4-year investigation of the effects of sowing date on three types of groundnut showed that the earlier the sowing date, the later was kernel initiation and kernel maturity. Early sowing resulted in a high pod yield, oil content and iodine number, but reduced the shelling percentage. The leaf area index ranged from 3·0 to 5·0, the highest value being obtained with early sowing and with non-upright bunch varieties.

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