Effect of Capsicum carotenoids on growth and aflatoxins production by Aspergillus flavus isolated from paprika and chilli

A Crop Growth Prediction Model Using Energy Data Based on Machine Learning in Smart Farms

In the recent past, the agricultural industry has rapidly digitalized in the form of smart farms through the broad usage of data analysis and artificial intelligence. Commonly, high operating costs in a smart farm are primarily due to inefficient energy usage. Therefore, accurate estimation of agricultural energy usage and environmental factors is considered as one of the significant tasks for crop growth control. The growth sequences of crops in agricultural environments like smart farms are related to agricultural energy usage and consumption. This study aims to develop and validate an algorithm that can interpret the crop growth rate response to environmental and solar energy factors based on machine learning, and to evaluate the algorithm's accuracy compared to the base model. The proposed model was determined through a comparative experiment of three representative machine learning techniques, which are random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM), considering the energy usage for environmental control is highly associated with the paprika crop growth. Through the experiment performance with real data gathered from a paprika smart farm in South Korea, the multi-level RF can effectively predict paprika growth with an accuracy of 0.88, considering data analysis of factors that use solar energy. As a result of the experiment with the suggested model, the growth factors such as leaf length, leaf width, and environmental factors were found. Furthermore, the proposed algorithm can contribute to the development of applications through analysis of the crop growth big data for various plants in agricultural environments such as a smart farm.

The effects of different ecophysiological factors on ochratoxin A production

Mycotoxin biosynthesis is affected by peculiar agro-ecosystem dependent conditions. Accordingly, the correlation between mycotoxin contamination and environmental condition change scenarios should be carefully considered. This is because fungal colonization and mycotoxin contamination could critically impair the availability of food products, especially in poor countries. At any time, the agricultural products might become susceptible to fungal contamination and mycotoxin production in uncontrolled storage. From 2000 to 2010, many studies were carried out in the case of the effects of different parameters on ochratoxin A (OTA) production by fungi in order to avoid providing the appropriate conditions for producing OTA. Water activity (a(w)), temperature, type of species and the substrates were introduced as the most important in these articles. This paper reviews the major published articles regarding the different ecophysiological factors influencing on OTA production.

In vitro effect of some fungicides on growth and aflatoxins production by Aspergillus flavus isolated from Capsicum powder

The aim of this study was to determine the effect of some pre-harvest fungicides on growth and aflatoxin (AF) production of three Aspergillus flavus strains found in Capsicum powder. Each isolate, previously isolated from paprika, chilli and smoked paprika, was inoculated on yeast extract sucrose agar and on a 3% paprika extract agar medium supplemented with different fungicides and incubated at 20 and 30°C during 7 days. Growth measurements were obtained on days 3, 5 and 7, and the AF production was determined on day 7. The significance of the effects of the factors (strain, medium, temperature, time and fungicides) and their interaction over colony diameter and AF production was determined. Temperature constrained the effectiveness of fungicides in reducing growth, the fungicides being most effective at 20°C. The efficacy of the fungicides over AF production depended on the medium used and temperature. The most effective fungicides in inhibiting growth and AF production, regardless of the strain tested or applied conditions, were tebuconazole 25% and mancozeb 80% applied at a concentration of 0.75 and 3.5 g l(-1), respectively. Care should thus be taken in the choice of a suitable fungicide because their effectiveness may depend on intra-specific variation and temperature. Moreover, it is necessary to take into account that the most efficient fungicide in reducing growth is not always the best choice for pre-harvest treatments because it may promote AF production. Thus, the best fungicide is the one that can simultaneous prevent growth and AF production.