Timely crop disease detection, pathogen identification, and infestation severity assessments can aid disease prevention and control efforts to mitigate crop-yield decline. However, improved disease monitoring methods are needed that can extract high-resolution, accurate, and rich color and spatial features from leaf disease spots in the field to achieve precise fine-grained disease-severity classification and sensitive disease-recognition accuracy. Here, we propose a neural-network-based method incorporating an improved Rouse spatial pyramid pooling strategy to achieve crop disease detection against a complex background. For neural network construction, first, a dual-attention module was introduced into the cross-stage partial network backbone to enable extraction of multi-dimensional disease information from the channel and space perspectives. Next, a dilated convolution-based spatial pyramid pooling module was integrated within the network to broaden the scope of the collection of crop-disease-related information from images of crops in the field. The neural network was tested using a set of sample data constructed from images collected at a rate of 40 frames per second that occupied only 17.12 MB of storage space. Field data analysis conducted using the miniaturized model revealed an average precision rate approaching 90.15% that exceeded the corresponding rates obtained using comparable conventional methods. Collectively, these results indicate that the proposed neural network model simplified disease-recognition tasks and suppressed noise transmission to achieve a greater accuracy rate than is obtainable using similar conventional methods, thus demonstrating that the proposed method should be suitable for use in practical applications related to crop disease recognition.

Efficient microalgae harvesting is a great challenge hindering diverse industrial applications of microalgae. Flocculation is regarded as an effective and promising technology for microalgae harvesting. In this study, sulfate (Al₂(SO₄)₃ and Fe₂(SO₄)₃) and chloride flocculants (AlCl₃ and FeCl₃) were used to harvest Chlorella vulgaris. Flocculation conditions, including flocculant dose, flocculation time, stirring speed, stirring time, and flocculation pH, were optimized, and flocculant effects on microalgal cell status, floc characteristics, biomass composition, algal cell re-culture, and media recycling were investigated. All flocculants exhibited efficient flocculation efficiency (93.5–98.8%) with lower doses of sulfate salts (60 mg/L algal culture) and higher doses of chloride salts (100 mg/L algal culture). The tested flocculants had no obvious influence on biomass composition (including lipids, carbohydrates, proteins, and carotenoids), and microalgal cells in flocs could efficiently regrow. The spent medium of all treatments was successfully recycled for subsequent cell growth, thus reducing dependency on fresh medium.

Population genomics is a rapidly developing discipline at the crossroads of population genetics and genomics