首页|Researchers at School of Computer Science and Engineering Release New Data on Ma chine Learning (Improved tomato leaf disease classification through adaptive ens emble models with exponential moving average fusion and enhanced weighted gradie nt ...)
Researchers at School of Computer Science and Engineering Release New Data on Ma chine Learning (Improved tomato leaf disease classification through adaptive ens emble models with exponential moving average fusion and enhanced weighted gradie nt ...)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news reporting out of Chennai, India, by New sRx editors, research stated, “Tomato is one of the most popular and most import ant food crops consumed globally. The quality and quantity of yield by tomato pl ants are affected by the impact made by various kinds of diseases.” Our news correspondents obtained a quote from the research from School of Comput er Science and Engineering: “Therefore, it is essential to identify these diseas es early so that it is possible to reduce the occurrences and effect of the dise ases on tomato plants to improve the overall crop yield and to support the farme rs. In the past, many research works have been carried out by applying the machi ne learning techniques to segment and classify the tomato leaf images. However, the existing machine learning-based classifiers are not able to detect the new t ypes of diseases more accurately. On the other hand, deep learning-based classif iers with the support of swarm intelligence-based optimization techniques are ab le to enhance the classification accuracy, leading to the more effective and acc urate detection of leaf diseases. This research paper proposes a new method for the accurate classification of tomato leaf diseases by harnessing the power of a n ensemble model in a sample dataset of tomato plants, containing images pertain ing to nine different types of leaf diseases. This research introduces an ensemb le model with an exponential moving average function with temporal constraints a nd an enhanced weighted gradient optimizer that is integrated into fine-tuned Vi sual Geometry Group-16 (VGG-16) and Neural Architecture Search Network (NASNet) mobile training methods for providing improved learning and classification accur acy.”
School of Computer Science and Engineeri ngChennaiIndiaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
