Syed Tahir Ata-Ul-Karim

Leaf area index (LAI) is a fundamental indicator of plant growth status in agronomic and environmental studies. Due to rapid advances in unmanned aerial vehicle (UAV) and sensor technologies, UAV-based remote sensing is emerging as a promising solution for monitoring crop LAI with great flexibility and applicability. This study aimed to determine the feasibility of combining color and texture information derived from UAV-based digital images for estimating LAI of rice (Oryza sativa L.). Rice field trials were conducted at two sites using different nitrogen application rates, varieties, and transplanting methods during 2016 to 2017. Digital images were collected using a consumer-grade UAV after sampling at key growth stages of tillering, stem elongation, panicle initiation and booting. Vegetation color indices (CIs) and grey level co-occurrence matrix-based textures were extracted from mosaicked UAV ortho-images for each plot. As a solution of using indices composed by two different textures, normalized difference texture indices (NDTIs) were calculated by two randomly selected textures. The relationships between rice LAIs and each calculated index were then compared using simple linear regression. Multivariate regression models with different input sets were further used to test the potential of combining CIs with various textures for rice LAI estimation. The results revealed that the visible atmospherically resistant index (VARI) based on three visible bands and the NDTI based on the mean textures derived from the red and green bands were the best for LAI retrieval in the CI and NDTI groups, respectively. Independent accuracy assessment showed that random forest (RF) exhibited the best predictive performance when combining CI and texture inputs (R2 = 0.84, RMSE = 0.87, MAE = 0.69). This study introduces a promising solution of combining color indices and textures from UAV-based digital imagery for rice LAI estimation. Future studies are needed on finding the best operation mode, suitable ground resolution, and optimal predictive methods for practical applications.

The Soil Plant Analysis Development (SPAD) chlorophyll meter is one of the most commonly used diagnostic tools to measure crop nitrogen status. However, the measurement method of the meter could significantly affect the accuracy of the final estimation. Thus, this research was undertaken to develop a new methodology to optimize SPAD meter measurements in rice (Oryza sativa L.). A flatbed color scanner was used to map the dynamic chlorophyll distribution and irregular leaf shapes. Calculus algorithm was adopted to estimate the potential positions for SPAD meter measurement along the leaf blade. Data generated by the flatbed color scanner and SPAD meter were analyzed simultaneously. The results suggested that a position 2/3 of the distance from the leaf base to the apex (2/3 position) could represent the chlorophyll content of the entire leaf blade, as indicated by the relatively low variance of measurements at that position. SPAD values based on di-positional leaves and the extracted chlorophyll a and b contents were compared. This comparison showed that the 2/3 position on the lower leaves tended to be more sensitive to changes in chlorophyll content. Finally, the 2/3 position and average SPAD values of the fourth fully expanded leaf from the top were compared with leaf nitrogen concentration. The results showed the 2/3 position on that leaf was most suitable for predicting the nitrogen status of rice. Based on these results, we recommend making SPAD measurements at the 2/3 position on the fourth fully expanded leaf from the top. The coupling of dynamic chlorophyll distribution and irregular leaf shapes information can provide a promising approach for the calibration of SPAD meter measurement, which can further benefit the in situ nitrogen management by providing reliable estimation of crops nitrogen nutrition status.

Efficient nitrogen (N) nutrition has the potential to alleviate drought stress in crops by maintaining metabolic activities even at low tissue water potential. This study was aimed to understand the potential of N to minimize the effects of drought stress applied/occur during tillering (Feekes stage 2) and jointing (Feekes stage 6) growth stages of wheat by observing the regulations and limitations of physiological activities, crop growth rate during drought periods as well as final grain yields at maturity. In present study, pot cultured plants of a wheat cultivar Yangmai-16 were exposed to three water levels [severe stress at 35-40% field capacity (FC), moderate stress at 55-60% FC and well-watered at 75-80% FC] under two N rates (0.24 g and 0.16 g/kg soil). The results showed that the plants under severe drought stress accompanied by low N exhibited highly downregulated photosynthesis, and chlorophyll (Chl) fluorescence during the drought stress periods, and showed an accelerated grain filling rate with shortened grain filling duration (GFD) at post-anthesis, and reduced grain yields. Severe drought-stressed plants especially at jointing, exhibited lower Chl and Rubisco contents, lower efficiency of photosystem II and greater grain yield reductions. In contrast, drought-stressed plants under higher N showed tolerance to drought stress by maintaining higher leaf water potential, Chl and Rubisco content; lower lipid peroxidation associated with higher superoxide dismutase and ascorbate peroxidase activities during drought periods. The plants under higher N showed delayed senescence, increased GFD and lower grain yield reductions. The results of the study suggested that higher N nutrition contributed to drought tolerance in wheat by maintaining higher photosynthetic activities and antioxidative defense system during vegetative growth periods.