Machine learning approaches for estimation of the fraction of absorbed photosynthetically active radiation and net photosynthesis rate of maize using multi-spectral sensor.

The fraction of absorbed photosynthetically active radiation (FAPAR) and the photosynthesis rate (Pn) of maize canopies were identified as essential photosynthetic parameters for accurately estimating vegetation growth and productivity using multispectral vegetation indices (VIs). Despite their importance, few studies have compared the effectiveness of multispectral imagery and various machine learning techniques in estimating these photosynthetic traits under high vegetation coverage. In this study, seventeen multispectral VIs and four machine learning (ML) algorithms were utilized to determine the most suitable model for estimating maize FAPAR and Pn during the and seasons at Tamil Nadu Agricultural University, Coimbatore, India.

Post-flood nitrogen and basal phosphorus management affects survival, metabolic changes and anti-oxidant enzyme activities of submerged rice (Oryza sativa).

Flooding is one of the major harmful abiotic stresses in the low lying areas of Asia and crop losses due to submergence are considerably high. Along with plant breeding techniques, agronomic management options in general and nutrient management in particular should be taken into consideration. Response of Sub 1 and non-Sub1 cultivars of rice to post-flood nitrogen (N) management under variable flood water was compared at maximum tillering stage.