The use of satellite-based Remote Sensing (RS) is a well-developed field of research. RS techniques have been successfully utilized to evaluate the chlorophyll content for the monitoring of sugarcane crops. This research provides a new framework for inferring the chlorophyll content in sugarcane crops at the canopy level using unmanned aerial vehicles (UAVs) and spectral vegetation indices processed with multiple machine learning algorithms. Studies were conducted in a sugarcane field located in Sugarcane Research Institute (SRI, Uda Walawe, Sri Lanka), with various fertilizer applications over the entire growing season from 2020 to 2021. An UAV with multispectral camera was used to collect the aerial images to generate the vegetation indices. Ground measurements of leaf chlorophyll were used as indications for fertilizer status in the sugarcane field. Different machine learning (ML) algorithms were used ground-truthing data of chlorophyll content and spectral vegetation indices to forecast sugarcane chlorophyll content. Several machine learning algorithms such as MLR, RF, DT, SVR, XGB, KNN and ANN were applied in two ways: before feature selection (BFS) by training the algorithms with all twenty-four (24) vegetation indices with five (05) spectral bands and after feature selection (AFS) by training algorithms with fifteen (15) vegetation indices. All the algorithms with both BFS and AFS methods were compared with an estimated coefficient of determination (R²) and root mean square error (RMSE). Spectral indices such as RVI and DVI were shown to be the most reliable indices for estimating chlorophyll content in sugarcane fields, with coefficients of determination (R²) of 0.94 and 0.93, respectively. XGB model shows the highest validation score (R²) and lowest RMSE in both methods of BFS (0.96 and 0.14) and AFS (0.98 and 0.78), respectively. However, KNN and SVR algorithms show the lowest validation accuracy than other models. According to the results, the AFS validation score is higher than BFS in MLR, SVR, XGB and KNN. Even though, validation score of the ANN model is decreased in AFS. The findings demonstrated that the use of multispectral UAV could be utilized to estimate chlorophyll content and measure crop health status over a larger sugarcane field. This methodology will aid in real-time crop nutrition management in sugarcane plantations by reducing the need for conventional measurement of sugarcane chlorophyll content.

This study investigated the potential of unmanned aerial vehicle (UAV) based multispectral imagery (MI) to predict the leaf nitrogen (N) content of sugarcane (Saccharum officinarum L.). MI of canopy cover of two sugarcane varieties (Co 775 and SL 96 128) applied with different doses of N (0 – 550 kg/ha) were captured at 4½ months after planting. These images were used to calculate 10 different vegetation indices (VIs). Five machine learning (ML) models were tested for their potential to predict leaf N status using the most appropriate VIs. The correlation analysis showed that DVI (Difference Vegetation Index) was the most powerful VI for the prediction of leaf N (r = 0.81), followed by the RVI (Ratio Vegetation Index) and NDVI (Normalized Difference Vegetation Index) (R2= 0.78 and 0.77, respectively). A threshold correlation (r > 0.6) was applied to select predictive variables for ML models and performance was evaluated using a validation data set of leaf N content. Individual variety testing revealed that PLSR (Partial Least Squares Regression) and SVR (Support Vector Regression) models as the best prediction models with the highest Coefficient of determination (R2>0.72) and the lowest Root Mean Square Error values (RMSE<0.11). When both variety data were pooled, RF (Random Forest) demonstrated the highest predictive performance on the validation dataset, with an R2 value of 0.66 with a RMSE value of 0.12. Generally, the prediction accuracy of models was less when data from both varieties were pooled. This study postulated the potential for the fusion of UAV MI and ML approaches to predict leaf N states and the importance of developing varietal-specific prediction models for the sugarcane vegetation.

Long-term sugarcane monoculture degrades soil. Applying sugarcane by-products can improve soil properties. This study compared the effects of sugarcane bagasse-based boiler ash versus bagasse-based biochar on soil physicochemical properties and initial growth of sugarcane variety SL 96 128. A pot experiment was conducted under shade house conditions using a randomized complete block design with four replications. Treatments consisted of four application rates of biochar and boiler ash (1, 2, 4, and 6 t ha⁻¹) along with the recommended N: P: K rates for sugarcane, recommended N: P: K rates only, and a zero-amendment as control. Plant growth performance and soil physicochemical properties were assessed three months after planting. The results indicated that the application of biochar and boiler ash improved nutrient availability to sugarcane plants. Both biochar and boiler ash amendments exhibited similar effects, while the zero-amendment control was found to be inadequate for the sugarcane variety SL 96 128. Regarding pH buffering capacity, the highest application rate of boiler ash (6 t ha⁻¹) resulted in low buffering capacity, suggesting that boiler ash should be applied under well-monitored conditions. Nevertheless, the application of boiler ash up to 6 t ha⁻¹ combined with recommended chemical fertilizers showed no negative impact on sugarcane plants within the first three months. Further studies are required to evaluate the effects of boiler ash on soil physicochemical properties and sugarcane growth under field conditions.

This study examined the patterns of nitrogen, phosphorus, and potassium absorption and dry biomass partitioning in the sugarcane variety SL 96 128 grown under controlled shade house conditions over a 180-day period. Plants were sampled destructively at 20-day intervals to assess nutrient content and biomass distribution in leaf and stalk tissues. Dry biomass was initially concentrated in leaves but shifted to the stalks during the grand growth phase, with stalks comprising over 50% of the total dry biomass from 120 days after planting. The maximum biomass accumulation rate was observed between 120 and 140 days after planting at 3.04 g/plant/day. Relative absorption of K and P peaked at 80 days after planting, while nitrogen absorption was highest at 100 days after planting. Potassium showed the greatest total accumulation, followed by nitrogen and phosphorus. Nutrients accumulated predominantly in leaves during early growth, then progressively shifted to the stalks as plants matured. These results highlight the critical nutrient demand period between 40 and 160 days after planting, emphasizing the need for well-timed nutrient applications. The findings contribute to the development of improved fertilizer strategies and support the formulation of nutrient management practices for sugarcane cultivation in Sri Lanka