wheat crop
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Brahim Jabir ◽  
Noureddine Falih

<span>In precision farming, identifying weeds is an essential first step in planning an integrated pest management program in cereals. By knowing the species present, we can learn about the types of herbicides to use to control them, especially in non-weeding crops where mechanical methods that are not effective (tillage, hand weeding, and hoeing and mowing). Therefore, using the deep learning based on convolutional neural network (CNN) will help to automatically identify weeds and then an intelligent system comes to achieve a localized spraying of the herbicides avoiding their large-scale use, preserving the environment. In this article we propose a smart system based on object detection models, implemented on a Raspberry, seek to identify the presence of relevant objects (weeds) in an area (wheat crop) in real time and classify those objects for decision support including spot spray with a chosen herbicide in accordance to the weed detected.</span>

Nahil Abebe ◽  
Mulugeta Negeri ◽  
Emana Getu ◽  
Thangavel Selvara

Background: Wheat (Triticum aestivum L.) is an important cereal crop as being consumed as staple food in the world as well as in Ethiopia. The production of wheat in Ethiopia decreased due to the incidence of insect pests. Out of insects’ pests the Russian wheat aphid (Diuraphis noxia) is the recent one that causes yield loss either directly or indirectly. Methods: The experiment was carried out at selected districts of West Showa zone, Ethiopia during off cropping season 2019 to evaluate the yield reduction in wheat crop due to the invasion of Russian wheat aphids. Malamar, Dimethoate, neem seeds, leaves, Beaveria bassiana and Metarhizium anisopliae were used in form of spray. Result: However, Malamar and Dimethoate highly significantly lowered the population of Diuraphis noxia. The combination of Beaveria bassiana and Metarhizium anisopleae significantly lowered the population of Russian wheat aphid. The combination of Neem leaf and Neem seeds, as well as Beaveria bassiana, proved to be effective against Russian wheat aphid yet they were protected and sound against the environments. Malamar showed the maximum decrease in Diuraphis noxia populations followed by Dimethoate, the combination of Beaveria bassiana and Metarhizium anisopleae.

2022 ◽  
Vol 73 (1) ◽  
pp. 189-192
A.S. NAIN ◽  

The ideal sowing period is critical for maximizing the crop's yield potential under specific agroclimatic conditions (Nain, 2016; Patra et al., 2017). It influences the phenological stages of the crop's development and, as a result, the efficient conversion of biomass into economic yield. During rabi 2013-14, a field research was done at GBPUA&T's Borlaug Crop Research Centre to determine the best sowing dates for wheat crops employing Aquacrop model. Aquacrop model has been calibrated against vegetative and economic yield forthree sowing dates, viz., 3rd December, 18th December and 3rd January (Pareek et al., 2017). After calibrating the Aquacrop model, a set of conservative variables was obtained (Pareek et al., 2017). Afterward, the calibrated Aquacrop model was used to validate wheat yield and biomass for three years in a row, namely 2010-11, 2011-12 and 2012-13. The model subsequently used to simulate yield under different sowing dates. For all of the tested years, the simulation findings of the Aquacrop model reflected the observed crop yields and biomass of wheat. The model was used to simulate the optimum sowing week based on varying sowing dates and produced grain yield for a period of 10 years (Malik et al., 2013). The average and assured yield of wheat was worked out based on probability analysis (60, 75 and 90%). The optimum sowing time for Tarai region of Uttarakhand was suggested as first week of November followed by second week of November (Nain, 2016). In no case wheat should be sown during third week of November and beyond due to poor assured yield and average yield (Nain, 2016). The finding of the studies will help to increase productivity and production of wheat crop in Tarai region of Uttarakhand.  

Plants ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 217
Anamaria Mălinaş ◽  
Roxana Vidican ◽  
Ioan Rotar ◽  
Cristian Mălinaş ◽  
Cristina Maria Moldovan ◽  

Although essential for achieving high crop yields required for the growing population worldwide, nitrogen, (N) in large amounts, along with its inefficient use, results in environmental pollution and increased greenhouse gas (GHG) emissions. Therefore, improved nitrogen use efficiency (NUE) has a significant role to play in the development of more sustainable crop production systems. Considering that wheat is one of the major crops cultivated in the world and contributes in high amounts to the large N footprint, designing sustainable wheat crop patterns, briefly defined by us in this review as the 3 Qs (high quantity, good quality and the quintessence of natural environment health) is urgently required. There are numerous indices used to benchmark N management for a specific crop, including wheat, but the misunderstanding of their specific functions could result in an under/overestimation of crop NUE. Thus, a better understanding of N dynamics in relation to wheat N cycling can enhance a higher efficiency of N use. In this sense, the aim of our review is to provide a critical analysis on the current knowledge with respect to wheat NUE. Further, considering the key traits involved in N uptake, assimilation, distribution and utilization efficiency, as well as genetics (G), environment (E) and management (M) interactions, we suggest a series of future perspectives that can enhance a better efficiency of N in wheat.

2022 ◽  
Vol 53 (1) ◽  
pp. 45-52
R. K. MALL ◽  

This study reports the role of field experimentation and system simulation in better quantifying the productivity of wheat crop, and examine how knowledge on potential productivity can improve the efficiency of the production system. When knowledge from field experimentation is utilised into crop weather simulation models, gap between actual, attainable and potential yield for a given environment can be determined and opportunities for yield improvement can be assessed. Results show that while actual district average yields show increasing trend, decreasing trend is noticed in potential and attainable yield. While the total and management yield gap is decreasing over time, research yield gap does not show any trend, it is nearly stagnant from early eighties to late nineties. The study reported here presents the advantage of simulation models to determine the yield gap against a variable annual yield potential for a agro-climatic region.

2022 ◽  
Vol 53 (2) ◽  
pp. 119-126
R. K. MALL ◽  

Actual evapotranspiration of wheat crop during different year from 1978-79 to 1992-93 was measured daily in Varanasi, Uttar Pradesh using lysimeter. In this study three evapotranspiration computing models namely Doorenbos and Pruitt, Thornthwaite and Soil Plant Atmosphere Water (SPAW) have been used. Comparisons of these three methods show that the SPAW model is better than the other two methods for evapotraspiration estimation. In the present study the MBE (Mean-Bias-Error), RMSE (Root Mean Square Error) and t-statistic have also been obtained for better evaluations of a model performance.

2022 ◽  
Vol 52 (3) ◽  
pp. 567-574
R. K. MALL ◽  
B. R. D. GUPTA ◽  

The Soil-Plant-Atmosphere- Water (SPA W) model has been calibrated and validated using field experiment data from 1991-92 to 1993-94 for wheat crop at Varanasi district. Long-term (1973-74 to 1995-96) daily weather data were combined with general observation of wheat growth and soils to provide daily water budgets for 23 years. The model was calibrated with one year detailed crop growth characteristics and soil water observations and validated with another year soil water observations. The daily-integrated water stress index (WSI) values at the end of crop season correlated quite well with observed grain yield in this region.   The water budget analysis shows a distinct optimum sowing period from 5th to 25th November and  an optimum sowing date on 15th November with minimal water stress index. These results demonstrate the potential of SPA W model for planning irrigation scheduling and water management for wheat crop in this region.

Vinod Kumar Yadav ◽  
Neeta Bhagat ◽  
Sushil K. Sharma

Drought is one of the most detrimental environmental stressors to plants with the potential to decrease crop yields and affect agricultural sustainability. Native bacteria with beneficial traits enhance plant growth and help avoid and reverse the effects of drought in plants to a greater extent. In the present study, we aimed to ( i ) isolate drought-tolerant Bacillus isolates from the rhizosphere soil of wheat crop grown at different locations in Jaisalmer district, Rajasthan state and (ii) further evaluate their ability to enhance plant growth and induce drought tolerance in wheat ( Var. HD-2967) grown under drought stress conditions. Of more than 100 isolates, two putative Bacillus isolates capable of tolerating 30 % polyethylene glycol-6000 (PEG-6000) [equivalent to -9.80 MPa (Megapascal)] were identified as Bacillus altitudinis DT-89 and Bacillus paramycoides DT-113. These isolates exhibited different plant growth promoting (PGP) attributes such as phosphate solubilization, and production of siderophore, exopolysaccharide, ammonia, indole acetic acid and cytokinin at low osmotic stress of 10% PEG-6000 but shown variable response at higher osmotic stress particularly at 30% PEG-6000. However, they did not show any antifungal activity and one isolate was negative for phosphate solubilization. Of two strains, B. altitudinis DT-89 function more prominently with respect to plant growth promotion and drought tolerance to plant in the early stage but protective traits of B. paramycoides DT-113 was more prominent after 75 days as evident by increased EPS (164%), root dry weight (144.44%), chlorophyll content (90.26%), SOD (389%) and proline (99.3%). The results support both the strains as a potential candidate to alleviate drought stress and enhance plant growth in the drought regions.

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