![]() ![]() He has 15+ years of experience in helping clients across industries in designing and implementing AI solutions. The ZS AI team ranked in the top 6% on the final leaderboard with a prediction accuracy of 95%.īio: Srinivas Chilukuri leads the ZS New York AI Center of Excellence. When scaled, this approach can help in digitally monitoring crop health and could lead to significant improvement in the agriculture productivity and yield. In addition to good prediction accuracy, we have also demonstrated that the model is able to effectively learn the right representations through the explanations inferred from class activation maps. In this article, we have shown how deep learning techniques can be applied to detect wheat rust in crops based on close shot images. ![]() Stem Rust: Has tube-like pattern and there are rust-related colors (yellow/orange) on tube-like pattern.As a result, the scope of the project must be. Leaf Rust: Has mixture of colors, has leaf-like pattern and there are rust-related colors (yellow/orange) on leaf-like pattern When it comes to detecting plant disease, a variety of algorithms are built around these four stages.This study focused to develop a model to boost the. Healthy Wheat: Major color is green, has leaf-like features, no rust-related colors such as yellow/orange The existing method for plant disease detection is simply naked eye observation by experts through which identification and detection of plant diseases is done. Mostly, these constraints are identified as diseases and pests that are hard to detect with bare eyes. ![]() We notice for different classes, our model has found important distinguishing patterns: Deep Dream Results for all classes based on our model ![]()
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