Survival and Comparative study on Different Artificial Intelligence Techniques for Crop Yield Prediction

Agriculture is an essential, important sector in the wide-reaching context. Farming helps to satisfy the basic need of food for every living being. Agriculture is considered the broadest economic sector. The crop yield is a significant part of food security and improves the drastic manner by human population. The quality and quantity of the yield touch the high rate of production. Farmers require timely advice to predict crop productivity. The strategic analysis also helps to increase crop production to meet the growing food demand. The forecasting of crop yield is a process of forecasting crop yield by using historical data. Machine learning provides a revolution in the agricultural field by changing the income scenario and growing an optimum crop. Many researchers carried out their research to deal with forecasting crop yield. In this way, accurate prediction of crop yield was improved. But, failed to reduce the crop yield prediction time and the accuracy level was not enhanced by existing methods

Learned Dual-View Reflection Removal

Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient for users. We instead propose a learning-based dereflection algorithm that uses stereo images as input. This is an effective trade-off between the two extremes: the parallax between two views provides cues to remove reflections, and two views are easy to capture due to the adoption of stereo cameras in smartphones. Our model consists of a learning-based reflection-invariant flow model for dual-view registration, and a learned synthesis model for combining aligned image pairs. Because no dataset for dual-view reflection removal exists, we render a synthetic dataset of dual-views with and without reflections for use in training. Our evaluation on an additional real-world dataset of stereo pairs shows that our algorithm outperforms existing single-image and multi-image dereflection approaches.Comment: http://sniklaus.com/dualre

Deep Reflection Prior

Reflections are very common phenomena in our daily photography, which distract people's attention from the scene behind the glass. The problem of removing reflection artifacts is important but challenging due to its ill-posed nature. Recent learning-based approaches have demonstrated a significant improvement in removing reflections. However, these methods are limited as they require a large number of synthetic reflection/clean image pairs for supervision, at the risk of overfitting in the synthetic image domain. In this paper, we propose a learning-based approach that captures the reflection statistical prior for single image reflection removal. Our algorithm is driven by optimizing the target with joint constraints enhanced between multiple input images during the training stage, but is able to eliminate reflections only from a single input for evaluation. Our framework allows to predict both background and reflection via a one-branch deep neural network, which is implemented by the controllable latent code that indicates either the background or reflection output. We demonstrate superior performance over the state-of-the-art methods on a large range of real-world images. We further provide insightful analysis behind the learned latent code, which may inspire more future work