Q-Learning-Based Pesticide Contamination Prediction in Vegetables and Fruits

Pesticides have become more necessary in modern agricultural production.However,these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the ecosystem.Due to a shortage of basic pesticide exposure awareness,farmers typically utilize pesticides extremely close to harvesting.Pesticide residues within foods,particularly fruits as well as veggies,are a significant issue among farmers,merchants,and particularly consumers.The residual concentrations were far lower than these maximal allowable limits,with only a few surpassing the restrictions for such pesticides in food.There is an obligation to provide a warning about this amount of pesticide use in farming.Previous technologies failed to forecast the large number of pesticides that were dangerous to people,necessitating the development of improved detection and early warning systems.A novel methodology for verifying the status and evaluating the level of pesticides in regularly consumed veggies as well as fruits has been identified,named as the Hybrid Chronic Multi-Residual Framework(HCMF),in which the harmful level of used pesticide residues has been predicted for contamination in agro products using Q-Learning based Recurrent Neural Network and the predicted contamination levels have been analyzed using Complex Event Processing(CEP)by processing given spatial and sequential data.The analysis results are used to minimize and effectively use pesticides in the agricultural field and also ensure the safety of farmers and consumers.Overall,the technique is carried out in a Python environment,with the results showing that the proposed model has a 98.57%accuracy and a training loss of 0.30.

Adsorption and desorption characteristics of diphenylarsenicals in two contrasting soils

Diphenylarsinic acid （DPAA） is formed during the leakage of aromatic arsenic chemical weapons in soils, is persistent in nature, and results in arsenic contamination in the field. The adsorption and desorption characteristics of DPAA were investigated in two typical Chinese soils, an Acrisol （a variable-charge soil） and a Phaeozem （a constant-charge soil）. Their thermodynamics and some of the factors influencing them （i.e., initial pH value, ionic strength and phosphate） were also evaluated using the batch method in order to understand the environmental fate of DPAA in soils. The results indicate that Acrisol had a stronger adsorption capacity for DPAA than Phaeozem. Soil DPAA adsorption was a spontaneous and endothermic process and the amount of DPAA adsorbed was affected significantly by variation in soil pH and phosphate. In contrast, soil organic matter and ionic strength had no significant effect on adsorption. This suggests that DPAA adsorption may be due to specific adsorption on soil mineral surfaces. Therefore, monitoring the fate of DPAA in soils is recommended in areas contaminated by leakage from chemical weapons.