Crop yield prediction is a fundamental component of global food security and sustainable agricultural planning. Potato, a key staple and cash crop in Atlantic Canada, presents challenges for yield prediction due to the complex nature of the relationships between soil parameters and crop production. In this research, we developed a machine learning (ML) framework that integrates multi-method feature selection and stacked ensemble learning to predict potato yield. The datasets consisted of soil analytical, geospatial, and productivity parameters from potato fields across Prince Edward Island (PEI) and New Brunswick (NB) during the 2017–2018 growing seasons. Stepwise regression-based feature selection identified eight significant features from 33 initial features that were included for training 16 baseline ML algorithms. The five top-performing models were used to further develop the proposed ensemble and stacked ensemble learning methods. Overall, the results indicated that the CatBoost, XGBoost, LightGBM, random forest (RF), and bagged trees outperformed other traditional ML methods. The proposed hybrid yield decision and regression approach- stacked ensemble (HYDRA-SE) framework achieved the highest accuracy (R 2 = 0.88 and RMSE = 3.75) . These results illustrate the significant potential of advanced ensemble learning methods to produce reliable and robust predictive capacity for potato yield prediction at a field level, regional level, and perhaps at the national level and can provide a decision-support system for potato precision agriculture (PA) in Atlantic Canada.

Drone-based imaging is a non-invasive technique that plays a critical role in monitoring plant health, stress levels, and soil moisture. Accurate transformation of RGB images to thermal maps is a challenging task due to various factors, including environmental noise, crop density, and variable lighting. Existing RGB-to-thermal translation approaches typically depend on implicit convolutional representations that inadequately extract directional crop structures, soil background variations, and multi-scale spatial variability, leading to reduce generalization across agricultural environments. To address these limitations, we advance a novel RGB to thermal map transformation approach (DTBQW-2BCLIFT) that integrates Dual-Tree Biquaternion Wavelet Transform (DTBQWT) with a newly designed two-branch convolutional attention layers framework based on Continual Learning with Informed Feature Transfer (2-BCLIFT). RGB drone images are decomposed into 18 feature maps to capture multi-resolution spatial and directional frequency information. Then, DTBQWT coefficients and raw RGB image are fused to 2BCLIFT. Unlike traditional deep learning approaches, the proposed 2-BCLIFT model is specifically optimized for field-level variation and is evaluated on drone imagery collected from four different agricultural fields in Prince Edward Island, Canada, under potato crop cultivation and various environmental conditions. This enables the proposed model to generalize effectively across diverse fields. Several metrics, including RMSE, PSNR, SSIM, and per-pixel accuracy, are employed to evaluate the proposed model against U-Net, Pix2Pix-GAN, Sparse GAN, Thermal-GAN, Modified Pix2Pix, and Cycle GAN. The results demonstrated that the proposed DTBQW-2BCLIFT model provided performance that is well suited for real world agricultural applications and compares favourably with existing state-of-the-art approaches. It can be combined with drones to detect early signs of water stress and nutrient deficiencies through variations in canopy temperature

SUMMARY: SomaticSiMu is an in silico simulator of single and double base substitutions, and single base insertions and deletions in an input genomic sequence to mimic mutational signatures. SomaticSiMu outputs simulated DNA sequences and mutational catalogues with imposed mutational signatures. The tool is the first mutational signature simulator featuring a graphical user interface, control of mutation rates and built-in visualization tools of the simulated mutations. Simulated datasets are useful as a ground truth to test the accuracy and sensitivity of DNA sequence classification tools and mutational signature extraction tools under different experimental scenarios. The reliability of SomaticSiMu was affirmed by (i) supervised machine learning classification of simulated sequences with different mutation types and burdens, and (ii) mutational signature extraction from simulated mutational catalogues. AVAILABILITY AND IMPLEMENTATION: SomaticSiMu is written in Python 3.8.3. The open-source code, documentation and tutorials are available at https://github.com/HillLab/SomaticSiMu under the terms of the CreativeCommonsAttribution4.0InternationalLicense. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Machine Learning with Digital Signal Processing and Graphical User Interface (MLDSP-GUI) is an open-source, alignment-free, ultrafast, computationally lightweight, and standalone software tool with an interactive GUI for comparison and analysis of DNA sequences. MLDSP-GUI is a general-purpose tool that can be used for a variety of applications such as taxonomic classification, disease classification, virus subtype classification, evolutionary analyses, among others. AVAILABILITY AND IMPLEMENTATION: MLDSP-GUI is open-source, cross-platform compatible, and is available under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/). The executable and dataset files are available at https://sourceforge.net/projects/mldsp-gui/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.