Urbanization has provided greater demand for food, and the search for strategies capable of reducing waste is essential to ensure food security. Lettuce (Lactuca sativa L.) culture has a short life cycle and its harvest point is determined visually, causing waste and important losses. Using vegetation indices could be an important alternative to reduce errors during harvest definition. The objective of this study was to evaluate different vegetation indices to predict the growth rate and harvest point of lettuce. Twenty-five genotypes of biofortified green lettuce were evaluated. The Green Leaf Index (GLI), Normalized Green Red Difference Index (NGRDI), Spectral Slope Saturation Index (SI), and Overall Hue Index (HUE) were calculated from images captured at 1, 8, 18, 24, and 36 days after transplanting (vegetative state). The diameter and average leaf area of plants were measured using QGIS software. Green mass, number of leaves, and plant and stem diameter were measured in the field. The means were compared using the Scott–Knott test (p ≤ 0.05) and simple linear regression models were generated to monitor the growth rate, obtaining R² values ranging from 62% to 99%. Genetic dissimilarity was confirmed by the multivariate analysis presenting a cophenetic correlation coefficient of 88.49%. Furthermore, validation between data collected in the field versus data obtained by imaging was performed using Pearson’s correlations and showed moderate to high values. Overall, the vegetation indices SI, GLI, and NGRDI were efficient for monitoring the growth rate and determining the harvest point of different green lettuce genotypes, in attempts to reduce waste and losses. It is suggested that the definition of the harvest point based on vegetation indices are specific for each genotype.

The coffee plant is one of the main crops grown in Brazil. However, strategies to estimate its yield are questionable given the characteristics of this crop; in this context, robust techniques, such as those based on machine learning, may be an alternative. Thus, the aim of the present study was to estimate the yield of a coffee crop using multispectral images and machine learning algorithms. Yield data from a same study area in 2017, 2018 and 2019, Sentinel 2 images, Random Forest (RF) algorithms, Support Vector Machine (SVM), Neural Network (NN) and Linear Regression (LR) were used. Statistical analysis was performed to assess the absolute Pearson correlation and coefficient of determination values. The Sentinel 2 satellite images proved to be favorable in estimating coffee yield. Despite the low spatial resolution in estimating agricultural variables below the canopy, the presence of specific bands such as the red edge, mid infrared and the derived vegetation indices, act as a countermeasure. The results show that the blue band and green normalized difference vegetation index (GNDVI) exhibit greater correlation with yield. The NN algorithm performed best and was capable of estimating yield with 23% RMSE, 20% MAPE and R² 0.82 using 85% of the training and 15% of the validation data of the algorithm. The NN algorithm was also more accurate (27% RMSE) in predicting yield.

When obtaining new cultivars or monitoring the nutritional composition of lettuce, new techniques are necessary given the high cost and time required to conduct laboratory analyses of plant composition by conventional methods. The objective of this study was to evaluate different vegetation indices for the estimation of anthocyanin, chlorophyll, and carotenoids in mini-lettuce genotypes with different leaf colors and different typologies from red, green, and blue (RGB) images. The contents of pigments were evaluated in 15 lettuce genotypes, in addition to the soil plant analysis development (SPAD) index and vegetation indices in the visible range. The variability among genotypes was confirmed by the Scott-Knott test (p < 0.05) and multivariate analysis. Linear regressions were obtained between the green leaf index (GLI) and leaf pigments. GLI was a good predictor for estimating the contents of anthocyanin (r = −0.83; r² = 0.75), carotenoid (r = −0.59; r² = 0.43), chlorophyll a (r = −0.69; r² = 0.48), chlorophyll b (r = −0.62; r² = 0.39), and total chlorophyll (r = −0.77; r² = 0.65) in red and green mini-lettuce. The high-performance phenotyping technique can be used to evaluate leaf pigments in breeding programs, as well as in crops for monitoring biofortification levels in lettuce.

Developing biofortified foods such as lettuce is a frequent goal of breeding programs. One obstacle to the success of these efforts is the high temporal and financial cost of determining leaf constituents. Image phenotyping has been increasingly used in crop breeding, but not in lettuce breeding. Until now, the use of image phenotyping to indirectly select carotenoid-rich lettuce inbred lines has not been reported. Therefore, the goal of this study was to use image phenotyping to select lettuce inbred lines with different carotenoid levels. Twenty-two inbred lettuce lines, resulting from the hybridization of the cultivars Pira 72 and Uberlândia 10000 and six successive selfings were evaluated. ‘Grand Rapids’, ‘UFU-Biofort’ and ‘Uberlândia 10000’ were used as controls. The following variables were evaluated: agronomic potential, apparent genetic diversity and image phenotyping. The data were submitted to the ANOVA F test (p ≤ 0.05) and means compared by the Scott–Knott test (p ≤ 0.05). Genetic divergence was represented by dendrograms constructed by UPGMA and the Tocher optimization method. The relative contribution of characters was assessed to identify the most relevant response variable. The genetic diversity of the evaluated germplasm bank was the greatest regarding soil plant analysis development (SPAD)/carotenoid values. Image phenotyping was successfully used to detect different levels of SPAD/carotenoid levels and could be a useful tool for plant breeding. The results of this study can be used to predict the nutritional values of the carotenoid content in lettuce leaves before commercialization.

Water magnetization and geoprocessing are increasingly utilized tools in weed management. Our objective was to study the influence of water magnetization on herbicide efficiency and to verify whether there is a relationship between control scores and the normalized difference vegetation index (NDVI). In the laboratory experiment, water was subjected to magnetization and evaluated with respect to four characteristics. In the field experiment, plots of Brachiaria grass were subjected to treatments in a factorial scheme (6 × 2 + 1). Six herbicidal factors (doses of glyphosate and glyphosate + 2,4-D) and the magnetization or absence of magnetization of the spray solution were evaluated and compared against the control treatment (without spraying). Weed control assessments were carried out six times. Images were obtained using an embedded multispectral camera to determine the NDVI values. Data related to water characteristics were analyzed using the t test. Weed control and NDVI data were subjected to analysis of variance and are presented in regression graphs. Dispersion analysis of NDVI data was performed according to the control scores. The magnetization process decreased the pH of the water and increased the surface tension, but it did not influence the control scores or the NDVI. As the glyphosate dose was increased, the control scores were higher and the NDVI values were lower. Magnetized water did not affect the biological efficiency of the herbicides, and there was a strong correlation between the control scores and the NDVI values.

Sweet corn (Zea mays subsp. saccharata) is mainly intended for industrial processing. Optimizing time and costs during plant breeding is fundamental. An alternative is the use of high-throughput phenotyping (HTP) indirect associated with agronomic traits and chlorophyll contents. This study aimed to (i) verify whether HTP by digital images is useful for screening sweet corn genotypes and (ii) investigate the correlations between the traits evaluated by conventional methods and those obtained from images. Ten traits were evaluated in seven S3 populations of sweet corn and in two commercial hybrids, three traits by classical phenotyping and the others by HTP based on RGB (red, green, blue) and multispectral imaging analysis. The data were submitted to the analyses of variance and Scott-Knott test. In addition, a phenotypic correlation graph was plotted. The hybrids were more productive than the S3 populations, showing an efficient evaluation. The traits extracted using HTP and classical phenotyping showed a high degree of association. HTP was efficient in identifying sweet corn genotypes with higher and lower yield. The vegetative canopy area (VCA), normalized difference vegetation index (NDVI), and visible atmospherically resistant index (VARI) indices were strongly associated with grain yield.