Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique’s effectiveness is confirmed by a fair comparison to existing procedures.

COVID-19 declared as a pandemic that has a faster rate of infection and has impacted the lives and the country’s economy due to forced lockdowns. Its detection using RT-PCR is required long time and due to which its infection has grown exponentially. This creates havoc for the shortage of testing kits in many countries. This work has proposed a new image processing-based technique for the health care systems named “C19D-Net”, to detect “COVID-19” infection from “Chest X-Ray” (XR) images, which can help radiologists to improve their accuracy of detection COVID-19. The proposed system extracts deep learning (DL) features by applying the InceptionV4 architecture and Multiclass SVM classifier to classify and detect COVID-19 infection into four different classes. The dataset of 1900 Chest XR images has been collected from two publicly accessible databases. Images are pre-processed with proper scaling and regular feeding to the proposed model for accuracy attainments. Extensive tests are conducted with the proposed model (“C19D-Net”) and it has succeeded to achieve the highest COVID-19 detection accuracy as 96.24% for 4-classes, 95.51% for three-classes, and 98.1% for two-classes. The proposed method has outperformed well in expressions of “precision”, “accuracy”, “F1-score” and “recall” in comparison with most of the recent previously published methods. As a result, for the present situation of COVID-19, the proposed “C19D-Net” can be employed in places where test kits are in short supply, to help the radiologists to improve their accuracy of detection of COVID-19 patients through XR-Images.

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Automation of agricultural food production is growing in popularity in scientific communities and industry. The main goal of automation is to identify and detect weeds in the crop. Weed intervention for the duration of crop establishment is a serious difficulty for wheat in North India. The soil nutrient is important for crop production. Weeds usually compete for light, water and air of nutrients and space from the target crop. This research paper assesses the growth rate of weeds due to macronutrients (nitrogen, phosphorus and potassium) absorbed from various soils (fertile, clay and loamy) in the rabi crop field. The weed image data have been collected from three different places in Madhya Pradesh, India with 10 different rabi crops (Maize, Lucerne, Cumin, Coriander, Wheat, Fenugreek, Gram, Onion, Mustard and Tomato) and 10 different weeds (Corchorus Capsularis, Cynodondactylon, Chloris barbata, Amaranthaceae, Argemone mexicana, Carthamus oxyacantha, Capsella bursa Pastoris, Chenopodium Album, Dactyloctenium aegyptium and Convolvulus Ravens). Intel Real Sense LiDAR digital camera L515 and Canon digital SLR DIGICAM EOS 850 D 18-55IS STM cameras were mounted over the wheat crop in 10 × 10 square feet area of land and 3670 different weed images have been collected. The 2936 weed images were used for training and 734 images for testing and validation. The Efficient Net-B7 and Inception V4 architectures have been used to train the model that has provided accuracy of 97% and 94% respectively. The Image classification using Inspection V4 was unsuccessful with less accurate results as compared to Effi-cientNet-B7.

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Summary In cloud, the most prominent area is workflow scheduling due to its widespread application in different domains. It comes under the NP‐complete problem, henceforth researchers have suggested the nature‐inspired heuristics and metaheuristic algorithms but still, the results of these heuristics are not optimal. These algorithms are not competitive but complementary to each other, so in that case, hybridization may yield better results. Since then, researchers have started to combine different heuristics for better results. But in this process of hybridization, the two most important factors have not been considered yet, namely global and local optimization. The proposed work has considered the two most popular nature‐inspired workflow scheduling Cuckoo Search (CS) algorithm and Flower Pollination Algorithm (FPA) for hybridization. CS works best for global optimization, and FPA provides good results in the case of local optimization. The proposed work leveraged the benefits of both optimizations and proposed a novel hybrid algorithm entitled Cuckoo Search Flower Pollination Algorithm (CSFPA). The proposed hybrid algorithm uses multi‐objective functions to minimize cost and time and thus enhance the utilization of resources. The CSFPA algorithm has been compared with both single nature aspired algorithms and hybrid algorithm for performance evaluation.