Red-Green-Blue and Multispectral Imaging as Potential Tools for Estimating Growth and Nutritional Performance of Cassava under Deficit Irrigation and Potassium Fertigation

Abstract

Cassava has high energy value and rich nutritional content, yet its productivity in the tropics is seriously constrained by abiotic stresses such as water deficit and low potassium (K) nutrition. Systems that allow evaluation of genotypes in the field and greenhouse for nondestructive estimation of plant performance would be useful means for monitoring the health of plants for crop-management decisions. We investigated whether the red–green–blue (RGB) and multispectral images could be used to detect the previsual effects of water deficit and low K in cassava, and whether the crop quality changes due to low moisture and low K could be observed from the images. Pot experiments were conducted with cassava cuttings. The experimental design was a split-plot arranged in a completely randomized design. Treatments were three irrigation doses split into various K rates. Plant images were captured beginning 30 days after planting (DAP) and ended at 90 DAP when plants were harvested. Results show that biomass, chlorophyll, and net photosynthesis were estimated with the highest accuracy (R2 = 0.90), followed by leaf area (R2 = 0.76). Starch, energy, carotenoid, and cyanide were also estimated satisfactorily (R2 > 0.80), although cyanide showed negative regression coefficients. All mineral elements showed lower estimation accuracy (R2 = 0.14–0.48) and exhibited weak associations with the spectral indices. Use of the normalized difference vegetation index (NDVI), green area (GA), and simple ratio (SR) indices allowed better estimation of growth and key nutritional traits. Irrigation dose 30% of pot capacity enriched with 0.01 mM K reduced most index values but increased the crop senescence index (CSI). Increasing K to 16 mM over the irrigation doses resulted in high index values, but low CSI. The findings indicate that RGB and multispectral imaging can provide indirect measurements of growth and key nutritional traits in cassava. Hence, they can be used as a tool in various breeding programs to facilitate cultivar evaluation and support management decisions to avert stress, such as the decision to irrigate or apply fertilizers.

Prediction of Biometric Variables Through Multispectral Images Obtained From Uav in Beans (<em>Phaseolus vulgaris L.</em>) During Ripening Stage

“…It was possible to predict the height of this crop with high performance (R 2 = 0.79) using model I. Recent studies [2,17] reported similar results for beans and cassava crops, respectively, demonstrating the utility of UAVs and vegetation indices for the compilation of biometric variables. The use of these tools becomes more important due to the need to conduct more agronomic evaluations in a timely manner in crops and mainly in those that are cultivated in arid environments such as the Peruvian coast.…”

“…Similarly, other researchers, predicted aerial biomass in various crops such as sunflower, corn and wheat [13][14][15][16]. Similarly, in Kenya RGB images were used to estimate growth and nutritional yield of cassava [17]. Since evaluations are necessary during the development of crops and as beans are one of the main foods in the basic family basket, it is essential that research is conducted to guarantee food security under the context of climate change.…”

Image-based phenotyping of cassava roots for diversity studies and carotenoids prediction

“…The potential for implementing rapid phenotyping based on digital images for TCC prediction was demonstrated in this study. In addition, image analysis allows the analysis and incorporation of other characteristics of agronomic importance in cassava cultivation, such as the evaluation of canopy and root yield-related traits, assessment of the linkage between root architecture and micronutrient (zinc and calcium) concentration, and estimation of the growth and nutritional performance of cassava under poor irrigation and potassium fertigation [ 51 – 53 ]. Therefore, these variables can be used in the composition of multi-trait selection indexes for further optimization of cassava breeding programs.…”

Application of Sentinel Images 2-A in Estimation of Potassium in Potatoes

“…Formula yang didapatkan untuk estimasi K pada tanaman y = 1,8003 + (0,5716*NDVI CS). Menurut Wasonga et al, (2021), penggunaan NDVI dapat memperolah pendugaan yang akurat karena mengamati melalui klorofil daun. Nilai NDVI tinggi ditunjukkan pada tanaman dengan irigasi penuh yang menandakan tanaman tersebut mengandung klorofil yang tinggi sehingga dapat dikatakan tanaman yang sehat.…”