The growth of the fish is influenced by a variety of scientific factors. So, profit can be easily achieved by using some clever techniques, for example, maintaining the correct pH level along with the dissolved oxygen (DO) level and temperature, as well as turbidity for good growth of fish. Fully grown fish are generally sold at a good price because price of fish in the market is governed by weight as well as size of nurtured fish. Artificial intelligence (AI)-based systems may be created to regulate key water quality factors including salinity, dissolved oxygen, pH, and temperature. The software programme operates on an application server and is connected to multiparameter water quality meters in this system. This study examines smart fish farming methods that show how complicated science and technology may be simplified for use in seafood production. This research focuses on the use of artificial intelligence in fish culture in this setting. The technical specifics of DL approaches used in smart fish farming which includes data and algorithms as well as performance was also examined. In a nutshell, our goal is to provide academics and practitioners with a better understanding of the current state of the art in DL implementation in aquaculture, which will help them deploy smart fish farming applications as well their benefits.

The residual population growth imposes an increase in food demand, driving humans to practice agricultural intensification on a large scale. Paradoxically, food and feed production may end up causing various environmental problems. At the same time, about 2.37 billion people in the World currently lack basic food security insurance. As a consequence, alternative sources that can substantially address the demand for food and feed sustainably are needed. Insect farming may offer an environmentally friendly solution for mitigating global food and feed challenges. The article aims to explore the potential of insects as sustainable food and feed sources while assessing their environmental impact, offering innovative solutions for global food security challenges. By highlighting the benefits of edible insects, the article supports informed decision-making and promotes sustainable practices. Mass production of edible insects has seen record growth over the decade, and their demand as future proteins is projected to reach up to 3 million tons in 2030. Additionally, insect farming is evidenced to be economically viable. To meet the demand for edible insects, a breakthrough such as the internet of things can be used to scale up production and processing. However, detailed environmental impact assessments are needed to predict scenarios of large-scale insect farming. Life cycle assessments of some edible insect production systems have validated that insect farming has various beneficial environmental impacts. The utilization of edible insects as food and feed is promising for significantly improving food security and the environmental sustainability of food.

Agricultural crop productivity relies on the application of chemical pesticides to reduce pest and pathogen damage. However, chemical pesticides also pose a range of ecological, environmental and economic penalties. This includes the development of pesticide resistance by insect pests and pathogens, rendering pesticides less effective. Alternative sustainable crop protection tools should therefore be considered. Semiochemicals are signalling molecules produced by organisms, including plants, microbes, and animals, which cause behavioural or developmental changes in receiving organisms. Manipulating semiochemicals could provide a more sustainable approach to the management of insect pests and pathogens across crops. Here, we review the role of semiochemicals in the interaction between plants, insects and microbes, including examples of how they have been applied to agricultural systems. We highlight future research priorities to be considered for semiochemicals to be credible alternatives to the application of chemical pesticides.

To prevent the occurrence of elevator safety accidents, in this study, an Internet of things-based elevator failure monitoring system is investigated. First, it introduces the Internet of things technology, preprocesses the relevant data, and extracts the features of the elevator operation data continuously collected by the elevator sensors. The objective of this paper is to study the application of IoT in elevators. Method. The Relief-F algorithm is used to evaluate the potential influencing factors. The results show that, as the batch size increases, the accuracy rate will gradually increase, but after more than 20, the accuracy rate will decrease. When the batch size is 20, the training result is the best. It can be seen that, as the time step increases, the accuracy of the prediction will be significantly improved. When the time step is 24, the prediction accuracy is the highest; after 24, the prediction rate will decline. In the diagram of the influence of learning rate on the model, the blue line indicates that the learning rate is 1.0, the red line is 0.1, and the black line is 0.01. With the increase of the iteration times, the effect is the best when the learning rate is 0.1. This system has high research value and broad application prospects and can make up for the current lack of monitoring in the elevator industry.

Water contamination, temperature imbalance, feed, space, and cost are key issues that traditional fish farming encounters. The aquaculture business still confronts obstacles such as the development of improved monitoring systems, the early detection of outbreaks, enormous mortality, and promoting sustainability, all of which are open problems that need to be solved. The goal of this study is to provide a machine learning (ML)-based aquaculture solution that boosts prawn growth and production in ponds. The study described a proposed framework that collects data using sensors, analyses it using a machine learning framework, and provides results like a preferred list of water quality (QOW) variables that affect prawn development and yield, as well as pond categorization into low, medium, and high prawn-producing ponds. In this study, we use eight distinct machine-learning classifiers to discover the driving elements that influence the development and yield of aquatic food products in ponds in terms of QOW variables, as well as three feature selection approaches to identify the aspects that have the largest impact on the pond's total harvest performance. To validate and obtain satisfying results, the suggested system was installed and tested. The average F score and accuracy when yield is employed as a harvest parameter are determined to be 0.85 and 0.78, respectively. The average merit ratings of temperature, dissolved oxygen, and salinity are significantly higher than those of the other QOW components. The temperature variations are greatest during the second, fourth, and seventh weeks. Temperature, salinity, and dissolved oxygen are the three QOW variables that have the largest influence on overall pond harvest performance, according to the data. Additionally, it has been discovered that a key QOW factor in separating high-yielding ponds from low-yielding ponds is the temperature change following stocking.

Buruli ulcer (BU), a neglected tropical disease (NTD), is an infection of the skin and subcutaneous tissue caused by Mycobacterium ulcerans. The disease has been documented in many South American, Asian, and Western Pacific countries and is widespread throughout much of Africa, especially in West and Central Africa. In rural areas with scarce medical care, BU is a devastating disease that can leave patients permanently disabled and socially stigmatized. Mycobacterium ulcerans is thought to produce a mycolactone toxin, which results in necrosis of the afflicted tissue and may be involved in the etiology of BU. Initially, patients may notice a painless nodule or plaque on their skin; as the disease progresses, however, it may spread to other parts of the body, including the muscles and bones. Clinical signs, microbial culture, and histological analysis of afflicted tissue all contribute to a diagnosis of BU. Though antibiotic treatment and surgical removal of infected tissue are necessary for BU management, plant-derived medicine could be an alternative in areas with limited access to conventional medicine. Herein we reviewed the geographical distribution, socioeconomic, risk factors, diagnosis, biology and ecology of the pathogen. Complex environmental, socioeconomic, and genetic factors that influence BU are discussed. Further, our review highlights future research areas needed to develop strategies to manage the disease through the use of indigenous African plants.