Review: Presence, distribution and current pesticides used in Spanish agricultural practices

To guarantee an adequate food supply for the world's growing population, intensive agriculture is necessary to ensure efficient food production. The use of pesticides helps maintain maximum productivity in intensive agriculture by minimizing crop losses due to pests. However, pesticide contamination of surface waters constitutes a major problem as they are resistant to degradation and soluble enough to be transported in water. In recent years, all groups of pesticides defined by the World Health Organization have increased their use and, therefore, their prevalence in the different environmental compartments that can have harmful effects. Despite this effort, there is no rigorous monitoring program that quantifies and controls the toxic effects of each pesticide. However, multiple scientific studies have been published by specialized research groups in which this information is disseminated. Therefore, any attempt to systematize this information is relevant. This review offers a current overview of the presence and distribution of the most widelyused pesticides (insecticides, herbicides, and fungicides) by crop type and an evaluation of the relationships between their uses and environmental implications in Spain. The data demonstrated that there are correlations between the presence of specific pesticides used in the main crops and their presence in the environmental compartments. We have found preliminary data pointing to existing associations between specific pesticides used in the main crops and their presence in environmental compartments within different geographical areas of Spain; this should be the subject of further investigation

Applicability domains of neural networks for toxicity prediction

In this paper, the term "applicability domain" refers to the range of chemical compounds for which the statistical quantitative structure-activity relationship (QSAR) model can accurately predict their toxicity. This is a crucial concept in the development and practical use of these models. First, a multidisciplinary review is provided regarding the theory and practice of applicability domains in the context of toxicity problems using the classical QSAR model. Then, the advantages and improved performance of neural networks (NNs), which are the most promising machine learning algorithms, are reviewed. Within the domain of medicinal chemistry, nine different methods using NNs for toxicity prediction were compared utilizing 29 alternative artificial intelligence (AI) techniques. Similarly, seven NN-based toxicity prediction methodologies were compared to six other AI techniques within the realm of food safety, 11 NN-based methodologies were compared to 16 different AI approaches in the environmental sciences category and four specific NN-based toxicity prediction methodologies were compared to nine alternative AI techniques in the field of industrial hygiene. Within the reviewed approaches, given known toxic compound descriptors and behaviors, we observed a difficulty in being able to extrapolate and predict the effects with untested chemical compounds. Different methods can be used for unsupervised clustering, such as distance-based approaches and consensus-based decision methods. Additionally, the importance of model validation has been highlighted within a regulatory context according to the Organization for Economic Co-operation and Development (OECD) principles, to predict the toxicity of potential new drugs in medicinal chemistry, to determine the limits of detection for harmful substances in food to predict the toxicity limits of chemicals in the environment, and to predict the exposure limits to harmful substances in the workplace. Despite its importance, a thorough application of toxicity models is still restricted in the field of medicinal chemistry and is virtually overlooked in other scientific domains. Consequently, only a small proportion of the toxicity studies conducted in medicinal chemistry consider the applicability domain in their mathematical models, thereby limiting their predictive power to untested drugs. Conversely, the applicability of these models is crucial; however, this has not been sufficiently assessed in toxicity prediction or in other related areas such as food science, environmental science, and industrial hygiene. Thus, this review sheds light on the prevalent use of Neural Networks in toxicity prediction, thereby serving as a valuable resource for researchers and practitioners across these multifaceted domains that could be extended to other fields in future research

Predicting the suitable fertilizer for crop based on soil and environmental factors using various feature selection techniques with classifiers

Agriculture is an essential part of human life. The crop productivity is based on the soil and environmental factors. Different crops are cultivated in different areas. Nowadays, the crop productivity level is affected by the climate change and diseases in the crops. Due to this pest infestation, the crop growth is heavily affected. To overcome this problem, the right fertilizer for a particular crop has to be chosen and fertilizer helps farmers to improve the crop productivity rate. This process can be done by using various machine learning techniques. In this work, various features selection techniques with classifiers used to predict the suitable fertilizer for a crop. The experimental results show that recursive feature elimination along the proposed Heterogeneous Stacked Ensemble classifier gives better prediction rate than other methods