Maize Yield Prediction with Machine Learning, Spectral Variables and Irrigation Management

Predicting maize yield using spectral information, temperature, and different irrigation management through machine learning algorithms provide information in a fast, accurate, and non-destructive way. The use of multispectral sensor data coupled with irrigation management in maize allows further exploration of water behavior and its relationship with changes in spectral bands presented by the crop. Thus, the objective of this study was to evaluate, by means of multivariate statistics and machine learning techniques, the relationship between irrigation management and spectral bands in predicting maize yields. Field experiments were carried out over two seasons (first and second seasons) in a randomized block design with four treatments (control and three additional irrigation levels) and eighteen sample repetitions. The response variables analyzed were vegetation indices (IVs) and crop yield (GY). Measurement of spectral wavelengths was performed with the Sensefly eBee RTK, with autonomous flight control. The eBee was equipped with the Parrot Sequoia multispectral sensor acquiring reflectance at the wavelengths of green (550 nm ± 40 nm), red (660 nm ± 40 nm), red-edge (735 nm ± 10 nm), and NIR (790 nm ± 40 nm). The blue length (496 nm) was obtained by additional RGB imaging. Data were subjected to Pearson correlations (r) between the evaluated variables represented by a correlation and scatter plot. Subsequently, the canonical analysis was performed to verify the interrelationship between the variables evaluated. Data were also subjected to machine learning (ML) analysis, in which three different input dataset configurations were tested: using only irrigation management (IR), using irrigation management and spectral bands (SB+IR), and using irrigation management, spectral bands, and temperature (IR+SB+Temp). ML models used were: Artificial Neural Network (ANN), M5P Decision Tree (J48), REPTree Decision Tree (REPT), Random Forest (RF), and Support Vector Machine (SVM). A multiple linear regression (LR) was tested as a control model. Our results revealed that Random Forest has higher accuracy in predicting grain yield in maize, especially when associated with the inputs SB+IR and SB+IR+Temp

Flavonoids and their relationship with the physiological quality of seeds from different soybean genotypes

Abstract Flavonoids are compounds that result from the secondary metabolism of plants and play a crucial role in plant development and mitigating biotic and abiotic stresses. The highest levels of flavonoids are found in legumes such as soybean. Breeding programs aim to increase desirable traits, such as higher flavonoid contents and vigorous seeds. Soybeans are one of the richest sources of protein in the plant kingdom and the main source of flavonoid derivatives for human health. In view of this, the hypothesis of this study is based on the possibility that the concentration of isoflavones in soybean seeds contributes to the physiological quality of the seeds. The aim of this study was to analyze the content of flavonoids in soybean genotypes and their influence on the physiological quality of the seeds. Seeds from thirty-two soybean genotypes were obtained by carrying out a field experiment during the 2021/22 crop season. The experimental design was randomized blocks with four replications and thirty-two F3 soybean populations. The seeds obtained were subjected to germination, first germination counting, electrical conductivity and tetrazolium vigor and viability tests. After drying and milling the material from each genotype, liquid chromatography analysis was carried out to obtain flavonoids, performed at UPLC level. Data were submitted to analysis of variance and, when significant, the means were compared using the Scott-Knott test at 5% probability. The results found here show the occurrence of genotypes with higher amounts of flavonoids when compared to their peers. The flavonoid FLVD_G2 had the highest concentration and differed from the others. Thus, we can assume that the type and concentration of flavonoids does not influence the physiological quality of seeds from different soybean genotypes, but it does indirectly contribute to viability and vigor, since the genotypes with the highest FLVD_G2 levels had better FGC values. The findings indicate that there is a difference between the content of flavonoids in soybean genotypes, with a higher content of genistein. The content of flavonoids does not influence the physiological quality of seeds, but contributes to increasing viability and vigor