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To control heavy metal pollution effectively, prevention of heavy metal accumulations in agricultural soils should be the priority rather than remediation of heavy metal contamination. In this research, papers which contained input and output fluxes of Cd, As, Cr, Hg, and Pb in topsoil (the plough layer) of agricultural lands in Hunan, Zhejiang, the Yangtze River Delta, Hainan, and China as a whole were explored. Fluxes of heavy metal species were recalculated, and future trends of pollution were predicted. Also, cases regarding the application of technologies to control the input and output of heavy metals were analyzed. Results indicated that atmospheric decomposition was the dominant input source of heavy metals in all study sites except Hainan. The relative contributions of fertilizers, irrigation water, and straw returning fluctuated greatly among different sites. Cd pollution in all sites was the most serious, followed by Cr and Pb. In Hunan, Cd has already exceeded the maximum limit value and needs to be controlled urgently. The input of heavy metals from irrigation water, fertilizers, and straw returning could be controlled by proposing more policies to manage their quality and application amounts. The amount of heavy metals absorbed by plants could be increased by cultivating crops with hyperaccumulators and high-biomass plants. [ABSTRACT FROM AUTHOR]

Areca yellow leaf disease is a major attacker of the planting and production of arecanut. The continuous expansion of arecanut (Areca catechu L.) planting areas in Hainan has placed a great need to strengthen the monitoring of this disease. At present, there is little research on the monitoring of areca yellow leaf disease. PlanetScope imagery can achieve daily global coverage at a high spatial resolution (3 m) and is thus suitable for the high-precision monitoring of plant pest and disease. In this paper, PlanetScope images were employed to extract spectral features commonly used in disease, pest and vegetation growth monitoring for primary models. In this paper, 13 spectral features commonly used in vegetation growth and pest monitoring were selected to form the initial feature space, followed by the implementation of the Correlation Analysis (CA) and independent t-testing to optimize the feature space. Then, the Random Forest (RF), Backward Propagation Neural Network (BPNN) and AdaBoost algorithms based on feature space optimization to construct double-classification (healthy, diseased) monitoring models for the areca yellow leaf disease. The results indicated that the green, blue and red bands, and plant senescence reflectance index (PSRI) and enhanced vegetation index (EVI) exhibited highly significant differences and strong correlations with healthy and diseased samples. The RF model exhibits the highest overall recognition accuracy for areca yellow leaf disease (88.24%), 2.95% and 20.59% higher than the BPNN and AdaBoost models, respectively. The commission and omission errors were lowest with the RF model for both healthy and diseased samples. This model also exhibited the highest Kappa coefficient at 0.765. Our results exhibit the feasible application of PlanetScope imagery for the regional large-scale monitoring of areca yellow leaf disease, with the RF method identified as the most suitable for this task. Our study provides a reference for the monitoring, a rapid assessment of the area affected and the management planning of the disease in the agricultural and forestry industries. [ABSTRACT FROM AUTHOR]