Recent Trends and Advances in Utilizing Digital Image Processing for Crop Nitrogen Management - Bhashitha Konara; Manokararajah Krishnapillai; Lakshman Galagedara (Remote Sensing)
Crop nitrogen (N) management in agricultural fields is crucial in preventing various environmental and socio-economic issues arising from excess N use. However, precise crop N management (PNM) is hindered by its intensive data requirements, high cost, and time requirements. Digital image processing (DIP) offers a promising approach to overcoming these challenges, and numerous studies have explored its application in N management. This review aims to analyze research trends in applying DIP for N management over the past 5 years, summarize the most recent studies, and identify challenges and opportunities. Web of Science, Scopus, IEEE Xplore, and Engineering Village were referred to for literature searches. A total of 95 articles remained after the screening and selection process. Interest in integrating machine learning and deep learning algorithms with DIP has increased, with the frequently used algorithms—Random Forest, Support Vector Machine, Extreme Gradient Boost, and Convolutional Neural Networks—achieving higher prediction accuracy levels. In addition, image data using more variables as model inputs, including agriculture sensors and meteorological data, have increased prediction accuracy. Nonetheless, several challenges associated with DIP, including obtaining high-quality datasets, complex image processing steps, costly infrastructure, and a user-unfriendly technical environment, still need to be addressed.