Gas exchange rates, plant height, yield components, and productivity of upland rice as affected by plant regulators

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Popping expansion and yield responses of popcorn cultivars under different row spacings and plant populations

The objective of this work was to evaluate the agronomic traits and the popping expansion index of three Brazilian popcorn cultivars under different row spacings and plant populations. The trials were performed during two crop seasons, under field conditions. The experimental design used was a randomized complete block, in a split-split plot, with 27 treatments and four replicates. Treatments were represented in a triple factorial arrangement: three row spacings (0.40, 0.60, and 0.80 m), three plant populations (40,000, 60,000, and 80,000 plants per hectare), and three popcorn cultivars (IAC-TC 01, IAC 12, and Zelia). The increase in plant population causes a reduction in the number of grains per ear, lower prolificacy, and grain weight loss. Cultivar grain yield is affected by row spacing and popcorn plant population. Cultivar IAC 12 shows highest grain yield under row spacings of 0.40 and 0.60 m and plant population between 60,000 and 80,000 plants per hectare. The popping expansion index is not affected by row spacing or plant population.

Fractal descriptors for discrimination of microscopy images of plant leaves

This study proposes the application of fractal descriptors method to the discrimination of microscopy images of plant leaves. Fractal descriptors have demonstrated to be a powerful discriminative method in image analysis, mainly for the discrimination of natural objects. In fact, these descriptors express the spatial arrangement of pixels inside the texture under different scales and such arrangements are directly related to physical properties inherent to the material depicted in the image. Here, we employ the Bouligand-Minkowski descriptors. These are obtained by the dilation of a surface mapping the gray-level texture. The classification of the microscopy images is performed by the well-known Support Vector Machine (SVM) method and we compare the success rate with other literature texture analysis methods. The proposed method achieved a correctness rate of 89%, while the second best solution, the Co-occurrence descriptors, yielded only 78%. This clear advantage of fractal descriptors demonstrates the potential of such approach in the analysis of the plant microscopy images.

Effect of nitrogen and application ways of a plant biostimulant on different wheat genotypes contrasting in stature

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)