scholarly journals Potential of Hyperspectral and Thermal Proximal Sensing for Estimating Growth Performance and Yield of Soybean Exposed to Different Drip Irrigation Regimes Under Arid Conditions

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6569 ◽  
Author(s):  
Adel H. Elmetwalli ◽  
Salah El-Hendawy ◽  
Nasser Al-Suhaibani ◽  
Majed Alotaibi ◽  
Muhammad Usman Tahir ◽  
...  
Keyword(s):  
Vegetative Growth ◽  
Growth Stage ◽  
Plant Traits ◽  
Growth Traits ◽  
Water Status ◽  
Accurate Estimation ◽  
Growth Stages ◽  
Irrigation Level ◽  
Arid Conditions ◽  
Irrigation Regimes

Proximal hyperspectral sensing tools could complement and perhaps replace destructive traditional methods for accurate estimation and monitoring of various morpho-physiological plant indicators. In this study, we assessed the potential of thermal imaging (TI) criteria and spectral reflectance indices (SRIs) to monitor different vegetative growth traits (biomass fresh weight, biomass dry weight, and canopy water mass) and seed yield (SY) of soybean exposed to 100%, 75%, and 50% of estimated crop evapotranspiration (ETc). These different plant traits were evaluated and related to TI criteria and SRIs at the beginning bloom (R1) and full seed (R6) growth stages. Results showed that all plant traits, TI criteria, and SRIs presented significant variations (p < 0.05) among irrigation regimes at both growth stages. The performance of TI criteria and SRIs for assessment of vegetative growth traits and SY fluctuated when relationships were analyzed for each irrigation regime or growth stage separately or when the data of both conditions were combined together. TI criteria and SRIs exhibited a moderate to strong relationship with vegetative growth traits when data from different irrigation regimes were pooled together at each growth stage or vice versa. The R6 and R1 growth stages are suitable for assessing SY under full (100% ETc) and severe (50% ETc) irrigation regimes, respectively, using SRIs. The overall results indicate that the usefulness of the TI and SRIs for assessment of growth, yield, and water status of soybean under arid conditions is limited to the growth stage, the irrigation level, and the combination between them.

Irrigation of field peas (Pisum sativum): response to timing at different crop growth stages

1999 ◽  
Vol 132 (4) ◽  
pp. 417-424 ◽  
Author(s):  
C. M. KNOTT
Keyword(s):  
Pisum Sativum ◽  
Vegetative Growth ◽  
Growth Stage ◽  
Crop Growth ◽  
Yield Response ◽  
Growth Stages ◽  
Field Peas ◽  
Crop Growth Stages ◽  
Different Growth Stages

The response of two cultivars of dry harvest field peas (Pisum sativum), Solara and Bohatyr, to irrigation at different growth stages was studied on light soils overlying sand in Nottinghamshire, England in 1990, when the spring was particularly dry, in 1991 which had a dry spring and summer and in contrast, 1992, when rainfall was greater compared with the long-term (40 year) mean.Solara, short haulmed and semi-leafless was more sensitive to drought than the tall conventional-leaved cultivar Bohatyr and gave a greater yield response to irrigation, particularly at the vegetative growth stage in the first two dry years 1990 and 1991, of 108% and 55% respectively, compared with unirrigated plots. Bohatyr was less sensitive to the timing of single applications.In all years, peas irrigated throughout on several occasions produced the highest yields, but this was the least efficient use of water.

scholarly journals Combining Thermal and RGB Imaging Indices with Multivariate and Data-Driven Modeling to Estimate the Growth, Water Status, and Yield of Potato under Different Drip Irrigation Regimes

2021 ◽  
Vol 13 (9) ◽  
pp. 1679
Author(s):  
Salah Elsayed ◽  
Salah El-Hendawy ◽  
Mosaad Khadr ◽  
Osama Elsherbiny ◽  
Nasser Al-Suhaibani ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Plant Traits ◽  
Water Status ◽  
Irrigation Management ◽  
Dry Weight ◽  
Data Driven ◽  
Inference System ◽  
Irrigation Regimes ◽  
Data Driven Modeling ◽  
Hyperspectral Sensing

Advances in proximal hyperspectral sensing tools, chemometric techniques, and data-driven modeling have enhanced precision irrigation management by facilitating the monitoring of several plant traits. This study investigated the performance of remote sensing indices derived from thermal and red-green-blue (RGB) images combined with stepwise multiple linear regression (SMLR) and an integrated adaptive neuro-fuzzy inference system with a genetic algorithm (ANFIS-GA) for monitoring the biomass fresh weight (BFW), biomass dry weight (BDW), biomass water content (BWC), and total tuber yield (TTY) of two potato varieties under 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Results showed that the plant traits and indices varied significantly between the three irrigation regimes. Furthermore, all of the indices exhibited strong relationships with BFW, CWC, and TTY (R2 = 0.80–0.92) and moderate to weak relationships with BDW (R2 = 0.25–0.65) when considered for each variety across the irrigation regimes, for each season across the varieties and irrigation regimes, and across all data combined, but none of the indices successfully assessed any of the plant traits when considered for each irrigation regime across the two varieties. The SMLR and ANFIS-GA models gave the best predictions for the four plant traits in the calibration and testing stages, with the exception of the SMLR testing model for BDW. Thus, the use of thermal and RGB imaging indices with ANFIS-GA models could be a practical tool for managing the growth and production of potato crops under deficit irrigation regimes.

scholarly journals Estimating the Leaf Water Status and Grain Yield of Wheat under Different Irrigation Regimes Using Optimized Two- and Three-Band Hyperspectral Indices and Multivariate Regression Models

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2666
Author(s):  
Salah Elsayed ◽  
Salah El-Hendawy ◽  
Yaser Hassan Dewir ◽  
Urs Schmidhalter ◽  
Hazem H. Ibrahim ◽  
...  
Keyword(s):  
Grain Yield ◽  
Water Content ◽  
Multivariate Regression ◽  
Regression Models ◽  
Plant Traits ◽  
Water Status ◽  
Growth Conditions ◽  
Leaf Water Status ◽  
Potential Yield ◽  
Irrigation Regimes

Spectral reflectance indices (SRIs) often show inconsistency in estimating plant traits across different growth conditions; thus, it is still necessary to develop further optimized SRIs to guarantee the performance of SRIs as a simple and rapid approach to accurately estimate plant traits. The primary goal of this study was to develop optimized two- and three-band vegetation- and water-SRIs and to apply different multivariate regression models based on these SRIs for accurately estimating the relative water content (RWC), gravimetric water content (GWCF), and grain yield (GY) of two wheat cultivars evaluated under three irrigation regimes (100%, 75%, and 50% of crop evapotranspiration (ETc)) for two seasons. Results showed that the three plant traits and all SRIs showed significant differences (p < 0.05) between the three irrigation treatments for each wheat cultivar. The three-band water-SRIs (NWIs-3b) showed the best performance in estimating the three plant traits for both cultivars (R2 > 0.80), and RWC and GWCF under 75% ETc (R2 ≥ 0.65). Four out of six three-band vegetation-SRIs (NDVIs-3b) performed better than any other SRIs for estimating GY under 100% ETc and 50% ETC, and RWC under 100% ETc (R2 ≥ 0.60). All types of SRIs demonstrated excellent performance in estimating the three plant traits (R2 ≥ 0.70) when the data of all growth conditions were combined and analyzed together. The NWIs-3b coupled with Random Forest models predicted the three plant traits with satisfactory accuracy for the calibration (R2 ≥ 0.96) and validation (R2 ≥ 0.93) datasets. The overall results of this study elucidate that extracting an optimized NWIs-3b from the full spectrum data and combined with an appropriate regression technique could be a practical approach for managing deficit irrigation regimes of crops through accurately, timely, and non-destructively monitoring the water status and final potential yield.

scholarly journals Growth Stages Classification of Potato Crop Based on Analysis of Spectral Response and Variables Optimization

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3995 ◽  
Author(s):  
Ning Liu ◽  
Ruomei Zhao ◽  
Lang Qiao ◽  
Yao Zhang ◽  
Minzan Li ◽  
...  
Keyword(s):  
Growth Stage ◽  
Spectral Response ◽  
Classification Model ◽  
Growth Stages ◽  
Potato Field ◽  
Spectral Bands ◽  
Svm Model ◽  
Selection Algorithms ◽  
Potato Crops

Potato is the world’s fourth-largest food crop, following rice, wheat, and maize. Unlike other crops, it is a typical root crop with a special growth cycle pattern and underground tubers, which makes it harder to track the progress of potatoes and to provide automated crop management. The classification of growth stages has great significance for right time management in the potato field. This paper aims to study how to classify the growth stage of potato crops accurately on the basis of spectroscopy technology. To develop a classification model that monitors the growth stage of potato crops, the field experiments were conducted at the tillering stage (S1), tuber formation stage (S2), tuber bulking stage (S3), and tuber maturation stage (S4), respectively. After spectral data pre-processing, the dynamic changes in chlorophyll content and spectral response during growth were analyzed. A classification model was then established using the support vector machine (SVM) algorithm based on spectral bands and the wavelet coefficients obtained from the continuous wavelet transform (CWT) of reflectance spectra. The spectral variables, which include sensitive spectral bands and feature wavelet coefficients, were optimized using three selection algorithms to improve the classification performance of the model. The selection algorithms include correlation analysis (CA), the successive projection algorithm (SPA), and the random frog (RF) algorithm. The model results were used to compare the performance of various methods. The CWT-SPA-SVM model exhibited excellent performance. The classification accuracies on the training set (Atrain) and the test set (Atest) were respectively 100% and 97.37%, demonstrating the good classification capability of the model. The difference between the Atrain and accuracy of cross-validation (Acv) was 1%, which showed that the model has good stability. Therefore, the CWT-SPA-SVM model can be used to classify the growth stages of potato crops accurately. This study provides an important support method for the classification of growth stages in the potato field.

scholarly journals Effect of Herbicides on the Management of the Invasive Weed Solanum rostratum Dunal (Solanaceae)

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 284
Author(s):  
Jackline Abu-Nassar ◽  
Maor Matzrafi
Keyword(s):  
Growth Stage ◽  
Late Summer ◽  
Early Spring ◽  
Growth Stages ◽  
Weed Species ◽  
Invasive Weed ◽  
Solanum Rostratum ◽  
Stage 4 ◽  
Chemical Solutions ◽  
Late Growth

Solanum rostratum Dunal is an invasive weed species that invaded Israel in the 1950s. The weed appears in several germination flashes, from early spring until late summer. Recently, an increase in its distribution range was observed, alongside the identification of new populations in the northern part of Israel. This study aimed to investigate the efficacy of herbicide application for the control of S. rostratum using two field populations originated from the Golan Heights and the Jezreel Valley. While minor differences in herbicide efficacy were recorded between populations, plant growth stage had a significant effect on herbicide response. Carfentrazone-ethyl was found to be highly effective in controlling plants at both early and late growth stages. Metribuzin, oxadiazon, oxyfluorfen and tembutrione showed reduced efficacy when applied at later growth stage (8–9 cm height), as compared to the application at an early growth stage (4–5 cm height). Tank mixes of oxadiazon and oxyfluorfen with different concentrations of surfactant improved later growth stage plant control. Taken together, our study highlights several herbicides that can improve weed control and may be used as chemical solutions alongside diversified crop rotation options. Thus, they may aid in preventing the spread and further buildup of S. rostratum field populations.

Carbon Storage and Carbon Dioxide Sequestration of Banana Plants at Different Growth Stages

2014 ◽  
Vol 1010-1012 ◽  
pp. 662-665
Author(s):  
Mu Qiu Zhao ◽  
Ming Li ◽  
Yun Feng Shi
Keyword(s):  
Carbon Dioxide ◽  
Vegetative Growth ◽  
Hainan Island ◽  
Growth Stages ◽  
Maturity Stage ◽  
C Storage ◽  
C Cycle ◽  
Fruit Growing ◽  
Storage Structures ◽  
Different Growth Stages

Large annual herbaceous plants such as banana (Musa spp.) have a very impressive carbon (C) storage and carbon dioxide (CO2) sequestration in agroecosystems, and play a certain role in global C cycle, climate regulation and reducing global warming. In this paper, we systematically studied C storage on the different growth stages, CO2sequestration and distribution, and mathematical models for predicting CO2sequestration by bananas which were planted in western Hainan island, China. The results showed that C content of dry matter in different structures of banana plants was 45-50% in line with the current results, and in fruit reached the highest, in stems and roots followed, while that in leaves were the lowest. C storage in different structures of banana plants increased exponentially during banana growing process (vegetative growth and bud stage), stems were the major storage structures of C, and roots and leaves also had considerable C storage. C fixed by banana plants was mainly distributed in fruit at fruit growing stage. CO2sequestration was 16.3, 41.1 and 80.0t/ha at vegetative growth, bud and fruit maturity stage separately, and power function model can be applied with stem diameter (D) or composite parameter (D2H) as independent variables to predict.

scholarly journals Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Shanjun Luo ◽  
Yingbin He ◽  
Qian Li ◽  
Weihua Jiao ◽  
Yaqiu Zhu ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Growth Stage ◽  
Estimation Error ◽  
Potato Yield ◽  
Hyperspectral Data ◽  
Single Stage ◽  
Accurate Estimation ◽  
Yield Estimation ◽  
Area Index ◽  
Red Edge

Abstract Background The accurate estimation of potato yield at regional scales is crucial for food security, precision agriculture, and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative leaf area index (rLAI) data to improve the accuracy of potato yield estimation based on the weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and the Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with a single and weighted stage was completed. Results The results showed that among the six test rVIs, the relative red edge chlorophyll index (rCIred edge) was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with an adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

scholarly journals Milk production and its relationship with milk composition, body and udder morphological traits in Bedouin goat reared under arid conditions

2018 ◽  
Vol 41 (1) ◽  
pp. 42552
Author(s):  
Fatima Kouri ◽  
Salima Charallah ◽  
Amina Kouri ◽  
Zaina Amirat ◽  
Farida Khammar
Keyword(s):  
Body Weight ◽  
Milk Production ◽  
Milk Yield ◽  
Milk Fat ◽  
Morphological Traits ◽  
Growth Traits ◽  
Average Daily Gain ◽  
Arid Conditions ◽  
Daily Milk Yield ◽  
Daily Milk

 This research aims to assess the performances of lactating Bedouin goat under arid conditions, focusing on the relationship of milk yield with body and udder morphology and growth traits. Fourteen goats and their kids were followed during early lactation (90 days post-partum). Mean daily milk yield was 0.56 kg. Milk fat, proteins, lactose, solids non-fat and minerals were respectively 34.9, 38.9, 48.8, 107 and 6.7 g L-1. Mean kids’ birth weight, adjusted body weight at 90 days of age and average daily gain were respectively 2 kg, 6.6 kg and 51.4 g day-1. Daily milk yield was correlated with goats’ body weight, withers height, rump height, udder width and udder circumference but not with linear udder score and growth performances. These results provide important data about the milk production of Bedouin goat emphasizing its correlation with body and udder morphological traits which can help to elaborate a specific breeding program for this breed. 

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