ABSTRACT Leaf chemical analysis is one of the ways to assess plant development. However, this type of assessment is expensive and time-consuming. The variation of nutrient content in the leaves modifies the proportion of light reflected and absorbed by plants at different wavelengths. Being able to relate the color reflected by the leaves with their phosphorus (P) content and using this data as input into an artificial neural network (ANN) can be an alternative for its determination. For this, it is necessary to establish which colors are most correlated with the different nutrients. Therefore, the phosphorus content in tomato leaves was evaluated in this study, according to four treatments (0.25, 50, 75, and 100% of the P doses). Different vegetation indices were also evaluated using images of mini-tomato leaves through a principal component analysis to determine which ones would be suitable to serve as an input to an ANN (multilayer perceptron). DGCI (Dark Green Color Index) and Bn (Normalized Blue) were the indices most related to P content. The neural network obtained 90% accuracy in the classification after training using both sides of the leaves.

ABSTRACT Lowland rice is traditionally irrigated by flood systems, demanding high water consumption. Localized irrigation by subsurface dripping is proposed as an alternative, in addition to the replacement of the source of water intended for human consumption with another of lower quality, such as saline water. However, plants can be affected by saline and/or water stress in both conditions, and the use of thermal imaging emerges as a tool to assess the plant status. In this context, this study aimed to identify the stress of subsurface drip-irrigated arborio rice under different salt concentrations and soil moisture by thermographic images. The design consisted of randomized blocks in a (2×4)+2 factorial with three replications, totaling 30 experimental plots. Soil solution salinity was assessed by electrical conductivity. The thermal images were processed by an algorithm to determine the normalized relative canopy temperature (NRCT) index at different crop development stages. Saline stress was identified by the NRCT index, with higher sensitivity of plants at the flowering stage with a rebalance over time, confirmed at grain filling and harvest stages.