Tauqeer Ahmad Yasir^1,2, Muhammad Ateeq^1,3, Allah Wasaya^1,2,*, Mubshar Hussain², Naeem Sarwar², Khuram Mubeen⁴, Mudassir Aziz⁴, Muhammad Aamir Iqbal⁵, Chukwuma C. Ogbaga⁶, Ibrahim Al-Ashkar⁷, Md Atikur Rahman⁸, Ayman El Sabagh^9,10,*

Phyton-International Journal of Experimental Botany, Vol.92, No.11, pp. 3113-3131, 2023, DOI:10.32604/phyton.2023.029502

Abstract Drought is one of the critical limitations to agricultural soils and crop plants. Scarcity of water is increasing due to climate change that lead to increasing threats to global food security. Therefore, ecofriendly and cost effective strategies are highly desirable for mitigating drought stress along with sustainable and smart agricultural production. The aim of the study was to mitigate DS using seed priming and exogenous supplementation of β-aminobutyric acid (BABA) in linseed (Linum usitatissimum L.). Different doses (0, 50, 100 and 150 µM) of BABA were used for seed priming agent and foliar spraying under three soil moisture levels viz.,… More >

Sana Parez¹, Naqqash Dilshad², Turki M. Alanazi³, Jong Weon Lee^1,*

Computer Systems Science and Engineering, Vol.47, No.1, pp. 515-536, 2023, DOI:10.32604/csse.2023.037992

Abstract A country’s economy heavily depends on agricultural development. However, due to several plant diseases, crop growth rate and quality are highly suffered. Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information. Therefore, the agricultural management system is searching for an automatic early disease detection technique. To this end, an efficient and lightweight Deep Learning (DL)-based framework (E-GreenNet) is proposed to overcome these problems and precisely classify the various diseases. In the end-to-end architecture, a MobileNetV3Small model is utilized as a backbone that generates refined, discriminative,… More >