Salinity and water stress are serious environmental issues that reduced crop production worldwide. The current research was initiated (2012) in the wirehouse of the Institute of Soil and Environmental Sciences, University of Agriculture, Faisalabad, Pakistan to investigate the growth, stress tolerance, and physiological responses of guava to salinity and water shortage. Guava was grown for one year in pots containing soil with Eight treatments (control, 10 dS m−1, 20 dS m−1, 40 dS m−1, control + water stress (WS), 10 dS m−1 + WS, 20 dS m−1 + WS, 40 dS m−1 + WS) in a completely randomized design. The results indicated that plant growth, stress tolerance, and physiological parameters declined at higher salinity and water stress and could not survive at 40 dS m−1. The 20 dS m−1 + WS caused a > 70% decline in dry weights of shoot and root regarding control. Similarly, the highest decrease in stress tolerance was noticed in 20 dS m−1 + WS followed by the 20 dS m−1 treatment than control. Our findings validated that guava can be cultivated on soils having salinity ≤ 10 dS m−1 but it could not be cultivated on soils having salinity ≥ 20 dS m−1 with limited water supply.
Agriculture is essential to the growth of every country. Cotton and other major crops fall into the cash crops. Cotton is affected by most of the diseases that cause significant crop damage. Many diseases affect yield through the leaf. Detecting disease early saves crop from further damage. Cotton is susceptible to several diseases, including leaf spot, target spot, bacterial blight, nutrient deficiency, powdery mildew, leaf curl, etc. Accurate disease identification is important for taking effective measures. Deep learning in the identification of plant disease plays an important role. The proposed model based on meta Deep Learning is used to identify several cotton leaf diseases accurately. We gathered cotton leaf images from the field for this study. The dataset contains 2385 images of healthy and diseased leaves. The size of the dataset was increased with the help of the data augmentation approach. The dataset was trained on Custom CNN, VGG16 Transfer Learning, ResNet50, and our proposed model: the meta deep learn leaf disease identification model. A meta learning technique has been proposed and implemented to provide a good accuracy and generalization. The proposed model has outperformed the Cotton Dataset with an accuracy of 98.53%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.