Agriculture is one of the most crucial sectors, meeting the fundamental food needs of humanity. Plant diseases increase food economic and food security concerns for countries and disrupt their agricultural planning. Traditional methods for detecting plant diseases require a lot of labor and time. Consequently, many researchers and institutions strive to address these issues using advanced technological methods. Deep learning-based plant disease detection offers considerable progress and hope compared to classical methods. When trained with large and high-quality datasets, these technologies robustly detect diseases on plant leaves in early stages. This study systematically reviews the application of deep learning techniques in plant disease detection by analyzing 160 research articles from 2020 to 2024. The studies are examined in three different areas: classification, detection, and segmentation of diseases on plant leaves, while also thoroughly reviewing publicly available datasets. This systematic review offers a comprehensive assessment of the current literature, detailing the most popular deep learning architectures, the most frequently studied plant diseases, datasets, encountered challenges, and various perspectives. It provides new insights for researchers working in the agricultural sector. Moreover, it addresses the major challenges in the field of disease detection in agriculture. Thus, this study offers valuable information and a suitable solution based on deep learning applications for agricultural sustainability.

In this research, we investigated the yields of biochar, bio-oil, and synthesis gas under various pyrolysis conditions, as well as their impact on the elemental composition, FTIR, EDX, SEM, and HCV values of biochar. This study utilized three different pyrolysis temperatures (400 °C, 500 °C, 600 °C), two holding times (30 and 60 min), and two N₂ gas flow rates (0.2 and 0.5 L min⁻¹). We observed that an increase in pyrolysis temperature led to a decrease in the yields of biochar and bio-oil, while synthesis gas yield increased, as expected. Additionally, a higher gas flow rate resulted in a reduction of biochar yield from 34.07% to 32.72%. A longer residence time diminished the bio-oil yield but increased the synthesis gas yield. The FTIR, EDX, and elemental analysis of biochar produced at a pyrolysis temperature of 600 °C, with a 60-min holding time and a 0.2 L min⁻¹ N₂ gas flow rate, indicated maximized carbon content. Moreover, a more porous structure was observed at higher pyrolysis temperatures. The research also revealed that increases in pyrolysis temperature, residence time, and gas flow rate enhanced the energy content of the biochar.

Lignin is a polymer found in the cell walls of plants and is an important component of wood. Lignin-derived fuels have attracted attention as a means of producing biofuels from biomass in recent years. There are two basic methods for converting lignin into fuel: thermochemical and catalytic. Lignin-derived fuels have the potential to reduce dependency on fossil fuels and reduce greenhouse gas emissions. However, more research is needed to optimize the production of lignin-derived fuels and to determine their environmental impact. This review aims to evaluate the development of lignin-derived fuels from an economic and environmental point of view while presenting a broad perspective.