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.

Identification of a novel hydrolase encoded by hy-1 from Bacillus amyloliquefaciens for bioremediation of carbendazim contaminated soil and food

Carbendazim(MBC)is an effective antifungal and antibacterial pesticide in agricultural applications.However,the MBC-contaminated soil and food are difficult to be restored.In this work,a novel MBC-hydrolase HY-1 encoded by gene hy-1 from an isolated MBC-degrading bacteria Bacillus amyloliquefaciens has been screened and identified.The 858 bp hydrolase gene was expressed in E.coli BL21 and the 32 kDa hydrolase HY-1 was purified.The purified HY-1 was able to catalyze MBC into 2-aminobenzimidazole(2-AB)without the need for any cofactors.Then bioremediation experiment was conducted and both the strain Car4 and cell crude extract of E.coli(pET-hy1)accelerated MBC degradation in soil.Moreover,purified HY-1 was available in removing MBC residue on the surface of cucumber.This work explored the possibility of microbial and enzymatic bioremediation on MBC-contaminated soil and food,provide a new way for bioremediation of pesticide contaminations.