Automatic Diagnosis of Rice Diseases Using Deep Learning.

Rice disease has serious negative effects on crop yield, and the correct diagnosis of rice diseases is the key to avoid these effects. However, the existing disease diagnosis methods for rice are neither accurate nor efficient, and special equipment is often required. In this study, an automatic diagnosis method was developed and implemented in a smartphone app. The method was developed using deep learning based on a large dataset that contained 33,026 images of six types of rice diseases: leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot. The core of the method was the Ensemble Model in which submodels were integrated. Finally, the Ensemble Model was validated using a separate set of images. Results showed that the three best submodels were DenseNet-121, SE-ResNet-50, and ResNeSt-50, in terms of several attributes, such as, learning rate, precision, recall, and disease recognition accuracy. Therefore, these three submodels were selected and integrated in the Ensemble Model. The Ensemble Model minimized confusion among the different types of disease, reducing misdiagnosis of the disease. Using the Ensemble Model to diagnose six types of rice diseases, an overall accuracy of 91% was achieved, which is considered to be reasonably good, considering the appearance similarities in some types of rice disease. The smartphone app allowed the client to use the Ensemble Model on the web server through a network, which was convenient and efficient for the field diagnosis of rice leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot.

Simulation analysis of fertilizer discharge process using the Discrete Element Method (DEM).

Fertilizer discharge process is a critical part of fertilizer application, as it affects the fertilizer discharge rate and uniformity of fertilizer application. In this study, a spiral grooved-wheel fertilizer discharge device was designed to replace the conventional straight grooved-wheel. Comparisons of the fertilizer discharge performance of the two grooved-wheel types were performed through tests and simulations using the discrete element method (DEM). The discharge performance of the two discharge devices was assessed by measuring the discharge mass rate, discharge uniformity, and the falling velocity of the fertilizer particles. Results showed that under similar conditions, the fertilizer discharge mass rate of the spiral grooved-wheel was higher than that of the straight grooved-wheel. The fertilizer discharge uniformity of the spiral grooved-wheel was much better than that of the straight grooved-wheel. The average falling velocity of fertilizer particles through the discharge spout was higher under the spiral grooved-wheel. The relative errors between the test and simulation results for the discharge mass rates, discharge uniformity, and particle falling velocities of the spiral grooved-wheel were all less than 10%. The developed spiral grooved-wheel exhibited a better performance than the conventional straight grooved-wheel, in all the aspects examined. The results serve as a theoretical basis for guiding the design of high-performance fertilizer applicators.

A stress-responsive NAC transcription factor SNAC3 confers heat and drought tolerance through modulation of reactive oxygen species in rice.

Adverse environmental conditions such as high temperature and drought stress greatly limit the growth and production of crops worldwide. Several NAC (NAM, ATAF1/2, and CUC2) proteins have been documented as important regulators in stress responses, but the molecular mechanisms are largely unknown. Here, a stress-responsive NAC gene, SNAC3 (ONAC003, LOC_Os01g09550), conferring drought and heat tolerance in rice is reported. SNAC3 was ubiquitously expressed and its transcript level was induced by drought, high temperature, salinity stress, and abscisic acid (ABA) treatment. Overexpression (OE) of SNAC3 in rice resulted in enhanced tolerance to high temperature, drought, and oxidative stress caused by methyl viologen (MV), whereas suppression of SNAC3 by RNAi resulted in increased sensitivity to these stresses. The SNAC3-OE transgenic plants exhibited significantly lower levels of H2O2, malondiadehyde (MDA), and relative electrolyte leakage than the wild-type control under heat stress conditions, implying that SNAC3 may confer stress tolerance by modulating reactive oxygen species (ROS) homeostasis. Quantitative PCR experiments showed that the expression of a large number of ROS-scavenging genes was dramatically increased in the SNAC3-OE plants, but significantly decreased in the SNAC3-RNAi transgenic plants. Five ROS-associated genes which were up-regulated in SNAC3-OE plants showed co-expression patterns with SNAC3, and three of the co-expressed ROS-associated enzyme genes were verified to be direct target genes of SNAC3. These results suggest that SNAC3 plays important roles in stress responses, and it is likely to be useful for engineering crops with improved tolerance to heat and drought stress.