The growing disparity between maize crop demand and actual production is concerning for both the food industry and farmers. Worldwide production of 1147.7 million MT of maize is insufficient to meet the demand of approximately 1149.96 million MT. Diseases like Turcicum Leaf Blight and Rust significantly hamper maize production. Manual disease detection, classification, severity calculation, and estimating crop loss are time-consuming and demand specific expertise. Hence, there’s a pressing need for automatic disease detection, severity prediction, and crop loss estimation. Machine learning and deep learning techniques, known for their success in pattern recognition and data analysis, have encouraged researchers to apply them in detecting diseases and estimating crop losses in maize. While existing literature showcases potential in disease detection, there’s a lack of reliable, real-world labeled datasets for training these models. Also, the focus on severity prediction and crop loss estimation is lacking in previous works. The paper provides a comprehensive overview of deep-learning approaches for Crop Loss Estimation in Maize Agriculture.

The rapidly increasing demand for energy and the consequent depletion of non-renewable energy sources pose significant challenges. Seeking alternatives, renewable sources like solar cells come into focus. Nevertheless, their limited efficiency hinders practical application and motivates researchers to develop more efficient solar cells. Through an examination of effectiveness, design viability, and fabrication costs, Dye-Sensitized Solar Cells (DSSC) emerge as superior to other photovoltaic solar cells. In particular the dye component is crucial for how well the DSSC works as it absorbs light from the sun.The paper investigates the topic related to forecasting the absorption maxima (λmax) of dyes and presents an overview of the proposals in the literature that use neural networks to forecast it. In addition, it discusses the main challenges related to this relevant topic, evidencing the need to address these challenges.

Agriculture is a vital industry for both the world’s food supply and economic health. The increasing global population and the demand for sustainable food production have led to the emergence of Artificial Intelligence (AI) as a game-changing technology to tackle agricultural issues. In this article, we explore the opportunities and challenges of AI-driven agriculture and provide an overview of the most promising applications and related ethical and practical issues.