Micro- and nano-plastics pollution and its potential remediation pathway by phytoremediation.

MAIN CONCLUSION: This review proposed that phytoremediation could be applied for the decontamination of MPs/NPs. Micro- and nano-plastics (MPs < 5 mm; NPs < 100 nm) are emerging contaminants. Much of the recent concerns have focused on the investigation of their pollution and their potential eco-toxicity. Yet little review was available on the decontamination of MPs/NPs. Recently, the uptake of MPs/NPs by plants has been confirmed. Here, in view of the current knowledge, this review introduces MPs/NPs pollution and highlights the updated information about the interaction between MPs/NPs and plants. This review proposed that phytoremediation could be a potential possible way for the in situ remediation of MPs/NPs-contaminated environment. The possible mechanisms, influencing factors, and existing problems are summarized, and further research needs are proposed. This review herein provides new insights into the development of plant-based process for emerging pollutants decontamination, as well as the alleviation of MPs/NPs-induced toxicity to the ecosystem.

Analysis of Corn Yield Prediction Potential at Various Growth Phases Using a Process-Based Model and Deep Learning.

Early and accurate prediction of grain yield is of great significance for ensuring food security and formulating food policy. The exploration of key growth phases and features is beneficial to improving the efficiency and accuracy of yield prediction. In this study, a hybrid approach using the WOFOST model and deep learning was developed to forecast corn yield, which analysed yield prediction potential at different growth phases and features. The World Food Studies (WOFOST) model was used to build a comprehensive simulated dataset by inputting meteorological, soil, crop and management data. Different feature combinations at various growth phases were designed to forecast yield using machine learning and deep learning methods. The results show that the key features of corn's vegetative growth stage and reproductive growth stage were growth state features and water-related features, respectively. With the continuous advancement of the crop growth stage, the ability to predict yield continued to improve. Especially after entering the reproductive growth stage, corn kernels begin to form, and the yield prediction performance is significantly improved. The performance of the optimal yield prediction model in flowering (R2 = 0.53, RMSE = 554.84 kg/ha, MRE = 8.27%), in milk maturity (R2 = 0.89, RMSE = 268.76 kg/ha, MRE = 4.01%), and in maturity (R2 = 0.98, RMSE = 102.65 kg/ha, MRE = 1.53%) were given. Thus, our method improves the accuracy of yield prediction, and provides reliable analysis results for predicting yield at various growth phases, which is helpful for farmers and governments in agricultural decision making. This can also be applied to yield prediction for other crops, which is of great value to guide agricultural production.