Deep learning has emerged as a potential tool for crop yield prediction, allowing the model
to automatically extract features and learn from the datasets. Meanwhile, smart farming …

Deep learning (DL) is a robust data-analysis and image-processing technique that has
shown great promise in the agricultural sector. In this study, 129 papers that are based on …

Fruit infected by pests or diseases and fruit harvests with different levels of ripeness cause a
lack of marketability, decrease in economic value, and increase in crop waste. In this study …

Predicting crop yields is a critical issue in agricultural production optimization and
intensification research. Accurate foresights of natural circumstances a year in advance can …

Yield prediction has long been a valuable tool for farmers seeking to enhance crop
production. Among the many ways to predict yield, the integration of machine learning (ML) …

Potato holds significant importance as a staple food crop worldwide, particularly in
addressing the needs of a growing population. Accurate estimation of the potato Leaf Area …

Machine learning (ML) has been widely used worldwide to develop crop yield forecasting
models. However, it is still challenging to identify the most critical features from a dataset …

The growth environment of litchis is complex, and there is a tendency for occlusion between
litchis, which makes litchi detection difficult. The use of UAVs to detect litchi in complex …

Deep learning and computer vision, using remote sensing and drones, are 2 promising
nondestructive methods for plant monitoring and phenotyping. However, their applications …

Yield prediction of citrus provides critical information before harvest to growers and allied
industry to predict the resources required for workers, storage, and transportation of the …