Plant height of maize is related to lodging resistance and yield and is highly heritable but also polygenic, and thus is an important trait in maize breeding. Various manual methods exist to determine the plant height of maize, yet they are labor-intensive and time consuming. Therefore, we established digital surface models (DSM) based on RGB-images captured by an unmanned aerial vehicle (UAV) at five different dates throughout the growth period to rapidly estimate plant height of 400 maize genotypes. The UAV-based estimation of plant height (PH_UAV) was compared to the manual measurement from the ground to the highest leaf (PH_L), to the tip of the manually straightened highest leaf (PH_S) and, on the final date, to the top of the tassel (PH_T). The best results were obtained for estimating both PH_L (0.44 ≤ R² ≤ 0.51) and PH_S (0.50 ≤ R² ≤ 0.61) from 39 to 68 days after sowing (DAS). After calibration the mean absolute percentage error (MAPE) between PH_UAV and PH_S was in a range from 12.07% to 19.62%. It is recommended to apply UAV-based maize height estimation from 0.2 m average plant height to maturity before the plants start to senesce and change the leaf color.

As an essential element, the effect of Phosphorus (P) on plant growth is very significant. In the early growth stage of maize, it has a high sensitivity to the deficiency of phosphorus. The main purpose of this paper is to monitor the maize status under two phosphorus levels in soil by a nondestructive testing method and identify different phosphorus treatments by spectral data. Here, the Analytical Spectral Devices (ASD) spectrometer was used to obtain canopy spectral data of 30 maize inbred lines in two P-level fields, whose reflectance differences were compared and the sensitive bands of P were discovered. Leaf Area Index (LAI) and yield under two P levels were quantitatively analyzed, and the responses of different varieties to P content in soil were observed. In addition, the correlations between 13 vegetation indexes and eight phenotypic parameters were compared under two P levels so as to find out the best vegetation index for maize characteristics estimation. A Back Propagation (BP) neural network was used to evaluate leaf area index and yield, and the corresponding prediction model was established. In order to classify different P levels of soil, the method of support vector machine (SVM) was applied. The results showed that the sensitive bands of P for maize canopy included 763 nm, 815 nm, and 900–1000 nm. P-stress had a significant effect on LAI and yield of most varieties, whose reduction rate reached 41% as a whole. In addition, it was found that the correlations between vegetation indexes and phenotypic parameters were weakened under low-P level. The regression coefficients of 0.75 and 0.5 for the prediction models of LAI and yield were found by combining the spectral data under two P levels. For the P-level identification in soil, the classification accuracy could reach above 86%. These abilities potentially allow for phenotypic parameters prediction of maize plants by spectral data and different phosphorus contents identification with unknown phosphorus fertilizer status.

In the coffee industry, approximately 60% of coffee fruit components are discarded after harvest, posing potential environmental challenges. To repurpose these organic by-products for human consumption, fresh coffee husks from wet coffee processing at a plantation in Tres Ríos, Cartago, Costa Rica, were used to prepare fruit spreads. Three formulations were developed, plain coffee pulp, coffee pulp with pectin, and coffee pulp with guava, each in high- and low-sugar versions (1:1 and 3:1 fruit-to-sugar ratio, respectively). The stability (moisture content, water activity, Brix, and pH), and key nutritional values (sugar profile, minerals, dietary fiber, and fat content) of these spreads were evaluated. Secondary plant metabolites, such as caffeine and tannins, and color were also analyzed. In addition, consumer acceptability was assessed using a hedonic test of the three formulations. The results showed that the high-sugar versions met stability requirements better than the low-sugar ones. Furthermore, both sugar variants had high mineral and dietary fiber contents, qualifying them for the “source of fiber” label according to European Union Food Regulation No. 1924/2006. Caffeine content was within safe limits for adult consumption at 60 mg per 20 g serving, with minimal tannin content (<0.7 mg/g dry basis). Sensory evaluation revealed a preference for the high-sugar guava formulation, followed by the high-sugar plain coffee pulp, and the lowest ratings were received for the low-sugar plain recipe. These results support the feasibility of converting coffee by-products into useful consumer products with potential health benefits and reduced environmental impact.

The coffee supply chain generates over 10 million tons of waste annually, with 70% comprising the fruit pulp and skin. This study investigates using coffee cherry flour from fresh coffee husk as an alternative ingredient in pastries with baking powder and baker’s yeast. We characterized the nutritional and physicochemical properties of this flour, developed high-fiber recipes, and evaluated the effects of substituting wheat flour with varying proportions of coffee cherry flour in plain cakes and sweet yeast breads. We found that coffee cherry flour reduced yeast dough volume increase and pastry specific volume in the coffee cherry flour pastries but enriched them with higher mineral and dietary fiber content compared to the wheat flour pastries.