This study highlights the intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance in the context of plant disease identification. It emphasizes the importance of rigorous dataset evaluation and selection protocols to ensure reliable and generalizable classification outcomes. Through a comprehensive examination of publicly available plant disease datasets, focusing on their performance as measured by GLCM metrics, this research identified dataset_2 (D2), a database of leaf images, as the top performer across all GLCM analyses. These datasets were then utilized to train the DarkNet19 deep learning model, with D2 exhibiting superior performance in both GLCM analysis and DarkNet19 training (achieving about 91% testing accuracy) according to performance metrics such as accuracy, precision, recall, and F1-score. The datasets other than dataset_1 and 2 exhibited significantly low classification performance, particularly in supporting GLCM analysis. The findings underscore the need for transparency and rigor in dataset selection, particularly given the abundance of similar datasets in the literature and the growing trend of utilizing deep learning methods in future scientific research.

Tanning industries have been so important since ancient times because it helps to produce essential goods such as garment and footwear. However, these industries have been polluting the environment, so there was a need for investigations, observations, and analysis by researchers for better management and environmental sustainability. In this study, solid wastes from tanning industries in Kano State, Nigeria were quantitatively analyzed. Both questionnaires and physical measurements were used in the Bompai, Sharada, and Challawa industrial areas. The average weight of raw skin was 0.96 kg and the generation rate was 27.5% of the total weight of processed skin. It was found that 85% of the generated waste was organic, while only 15% was inorganic. The peak season for a solid waste generation was the holy season of the Muslim-majority communities, known as "Eid al-Adha", (10th-13th Zulhajj of the lunar calendar). While the off-peak generation season was during the rainy season, generated solid wastes from the tanning industries could have the potential for sustainable utilization in terms of renewable energy generation and some agricultural applications. It was therefore recommended that future researches should analyze the potentiality of these industries for the benefit of Kano State.

The growing global demand for sustainable energy has intensified interest in biomass residues as viable feedstocks for biofuels and bio-based production. This review systematically examines advances in the utilization of biomass residues, spanning upstream assessment through downstream conversion pathways. Using the PRISMA framework, 543 peer-reviewed articles published between 1990 and 2025 were analyzed from the Scopus and Web of Science databases. The review reveals a clear methodological evolution from early residue characterization and physicochemical analyses toward integrated techno-economic, environmental, and system-level assessments. Upstream research increasingly addresses feedstock identification, spatial dispersion, logistics optimization, and pretreatment efficiency, while downstream advances focus on biochemical, thermochemical, and hybrid conversion technologies. Although artificial intelligence and machine learning constitute approximately 2.5–3% of the total historical literature, they account for nearly 18–22% of recent studies in process modeling and yield prediction, achieving predictive accuracies frequently exceeding R² > 0.95. Despite these advances, persistent challenges remain in biomass logistics, feedstock heterogeneity, and technology scaling. Emerging trends highlight hybrid frameworks that integrate data-driven and mechanistic models to enhance efficiency, circularity, and commercial feasibility in bioenergy systems.

The intricate relationship between Gray-Level Co-occurrence Matrix (GLCM) metrics and machine learning model performance underscores the need for rigorous dataset evaluation and selection protocols to ensure the reliability and generalizability of classification outcomes. This study involved a thorough examination of selected publicly available plant diseases datasets, with an emphasis on how well they performed as measured by GLCM metrics. After first classifying the datasets according to their GLCM metrics, dataset_2 (D2) and dataset_5 (D5) were found, respectively, to be the best-performing dataset in all GLCM analyses. The same datasets were then used to train deep learning models, and their classification performances were assessed. A noteworthy association was observed between the results of training deep learning models and the performance ratings derived from GLCM studies. More specifically, dataset_2 (D2) performed best in both GLCM analysis and deep learning model performance, indicating a strong correlation between the accuracy of classification and the textural qualities that GLCM captured. In the context of plant disease identification, in particular, these results highlight the significance of clearly defined dataset selection criteria in deep learning applications. Scholars can improve the accuracy and dependability of deep learning models for diagnosing plant diseases by giving preference to datasets with favorable GLCM metrics. The research also emphasizes the importance of texture features being taken into account in addition to conventional image features, highlighting the necessity of transparency and rigor in dataset selection procedures.