A Four Dimensional Spatio-Temporal Analysis of an Agricultural Dataset

Four-Dimensional Plant Phenotyping Model Integrating Low-Density LiDAR Data and Multispectral Images

High-throughput platforms for plant phenotyping usually demand expensive high-density LiDAR devices with computational intense methods for characterizing several morphological variables. In fact, most platforms require offline processing to achieve a comprehensive plant architecture model. In this paper, we propose a low-cost plant phenotyping system based on the sensory fusion of low-density LiDAR data with multispectral imagery. Our contribution is twofold: (i) an integrated phenotyping platform with embedded processing methods capable of providing real-time morphological data, and (ii) a multi-sensor fusion algorithm that precisely match the 3D LiDAR point-cloud data with the corresponding multispectral information, aiming for the consolidation of four-dimensional plant models. We conducted extensive experimental tests over two plants with different morphological structures, demonstrating the potential of the proposed solution for enabling real-time plant architecture modeling in the field, based on low-density LiDARs.

Spatial and Temporal Distribution of Leafhoppers (Hemiptera: Cicadellidae) in a Corn Field

Leafhoppers (Hemiptera: Cicadellidae) are phytophagous insects that transmit diseases and cause damage to this crop. Thus, the main goal of this study was to verify the spatiotemporal behavior of the leafhoppers Tapajosa ocellata (Osborn, 1926), Dechacona missionum (Berg, 1879), and Dalbulus maidis (DeLong, 1923) in a corn field in Igarapé-Açu, northeastern Pará, northern Brazil. An area of 1.0 ha was used for the development of the study. A mango agroecosystem, a pasture, and secondary forest fragment are adjacent to the experimental area. The sampling of leafhoppers occurred during the 2015 and 2016 harvests. For sampling, 10 random plants per plot were analyzed, totaling 1000 plants per sample. In order to verify the occurrence of leafhoppers, all aerial parts of the plants were analyzed visually. The spatiotemporal behavior of leafhoppers and influence of adjacent areas and phenological stages of corn were verified through geostatistics. From the composition of semivariograms, interpolation maps were constructed by kriging. The results showed that, in 2015, there was a higher incidence of D. maidis compared to the following year. In 2016, 352 individuals of T. ocellata and 66 of D. missionum were observed. Regarding the spatial distribution, it was found that the smallest area of influence of T. ocellata, D. missionum, and D. maidis was 0.09, 0.08, and 0.05 ha, respectively. The spatial distribution of leafhoppers showed an aggregate behavior concentrated close to adjacent areas with a predominance of grasses. Population fluctuation demonstrated that the highest incidence of leafhoppers occurs in the vegetative stages of corn.