Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization.

Tomato is one of the most popular and most important food crops consumed globally. The quality and quantity of yield by tomato plants are affected by the impact made by various kinds of diseases. Therefore, it is essential to identify these diseases early so that it is possible to reduce the occurrences and effect of the diseases on tomato plants to improve the overall crop yield and to support the farmers. In the past, many research works have been carried out by applying the machine learning techniques to segment and classify the tomato leaf images. However, the existing machine learning-based classifiers are not able to detect the new types of diseases more accurately. On the other hand, deep learning-based classifiers with the support of swarm intelligence-based optimization techniques are able to enhance the classification accuracy, leading to the more effective and accurate detection of leaf diseases. This research paper proposes a new method for the accurate classification of tomato leaf diseases by harnessing the power of an ensemble model in a sample dataset of tomato plants, containing images pertaining to nine different types of leaf diseases. This research introduces an ensemble model with an exponential moving average function with temporal constraints and an enhanced weighted gradient optimizer that is integrated into fine-tuned Visual Geometry Group-16 (VGG-16) and Neural Architecture Search Network (NASNet) mobile training methods for providing improved learning and classification accuracy. The dataset used for the research consists of 10,000 tomato leaf images categorized into nine classes for training and validating the model and an additional 1,000 images reserved for testing the model. The results have been analyzed thoroughly and benchmarked with existing performance metrics, thus proving that the proposed approach gives better performance in terms of accuracy, loss, precision, recall, receiver operating characteristic curve, and F1-score with values of 98.7%, 4%, 97.9%, 98.6%, 99.97%, and 98.7%, respectively.

Microbial production of N-acetyl-D-glucosamine (GlcNAc) for versatile applications: Biotechnological strategies for green process development.

N-acetyl-d-glucosamine (GlcNAc) is a commercially important amino sugar for its wide range of applications in pharmaceutical, food, cosmetics and biofuel industries. In nature, GlcNAc is polymerised into chitin biopolymer, which is one of the major constituents of fungal cell wall and outer shells of crustaceans. Sea food processing industries generate a large volume of chitin as biopolymeric waste. Because of its high abundance, chitinaceous shellfish wastes have been exploited as one of the major precursor substrates of GlcNAc production, both in chemical and enzymatic means. Nevertheless, the current process of GlcNAc extraction from shellfish wastes generates poor turnover and attracts environmental hazards. Moreover, GlcNAc isolated from shellfish could not be prescribed to certain groups of people because of the allergic nature of shell components. Therefore, an alternative route of GlcNAc production is advocated. With the advancement of metabolic construction and synthetic biology, microbial synthesis of GlcNAc is gaining much attention nowadays. Several new and cutting-edge technologies like substrate co-utilization strategy, promoter engineering, and CRISPR interference system were proposed in this fascinating area. The study would put forward the potential application of microbial engineering in the production of important pharmaceuticals. Very recently, autotrophic fermentation of GlcNAc synthesis has been proposed. The metabolic engineering approaches would offer great promise to mitigate the issues of low yield and high production cost, which are major challenges in microbial bio-processes industries. Further process optimization, optimising metabolic flux, and efficient recovery of GlcNAc from culture broth, should be investigated in order to achieve a high product titer. The current study presents a comprehensive review on microbe-based eco-friendly green methods that would pave the way towards the development of future research directions in this field for the designing of a cost-effective fermentation process on an industrial setup.