Crop Loss Estimation in Maize Agriculture: A Deep Learning Perspective

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.