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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%.

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.

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Modern industrial systems are growing day by day and unlikely their complexity is also increasing. On the other hand, the design and operations have become a key focus of the researchers in order to improve the production system. To cope up with these chellenges, the data-driven technique like principal component analysis (PCA) is famous to assist the working systems. A data in bulk quanitity from the sensor measurements are often available in such industrial systems. Considering the modern industrial systems and their economic benifits, the fault diagnostic techniqes have been deeply studied. For example, the techniques that consider the process data as the key element. In this paper, the faults have been detected with the data-driven approach using PCA. In particular, the faults have been detected by using and statistics. In this process, PCA projects large data into smaller dimensions. Additionally it also preserves all the important information of process. In order to understand the impact of the technique, Tennessee Eastman chemical plant is considerd for the performance evaluation.

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