Early leaf diseases prediction in Paddy crop using Deep learning model

Bajpai, Abhishek; Tiwari, Naveen; Tripathi, Ashutosh; Tripathi, Vibha; Katiyar, Devesh

doi:10.1109/pcems58491.2023.10136038

Cited by 3 publications

(1 citation statement)

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“…We have examined four classes in this paper for the leaf category classification. To identify the true disease affecting the afflicted plants, we employed deep learning techniques [11]. We put three different architectures into practice: VGGNet16, RenNet101, and AlexNet.…”

Section: Literature Surveymentioning

confidence: 99%

Zone-II & III: Machine Learning based Rice Yield Prediction in Andhra Pradesh

Ramisetty,

Bansode,

Atmakur

et al. 2024

MATEC Web Conf.

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In a unique approach, this research predicts rice yield in Zones II and III of Andhra Pradesh using machine learning algorithms. Because food security and agricultural sustainability are becoming more and more important, an accurate assessment of crop yields is necessary for agricultural planning and decision-making. By using machine learning approaches, we hope to develop robust prediction models that can forecast rice yields depending on a variety of agronomic and environmental conditions. Our plan includes collecting a lot of data on variables such as soil qualities, weather patterns, crop management practices, and historical yield records from the target regions. Using careful feature engineering and data preprocessing, we find relevant predictors and derive significant insights for rice yield estimation. Next, we employ state-of-the-art machine learning methods, like Support Vector Machines, Random Forest, and Gradient Boosting, to build trustworthy prediction models. These models are trained on historical data and then cross-validated to guarantee accuracy and generalizability. Our goal is to combine domain expertise in agriculture with cutting-edge machine learning techniques to provide farmers, policymakers, and other agricultural stakeholders with insightful understandings of the dynamics of rice production. In Zones II and III of Andhra Pradesh, this will make it easier to allocate resources and make improve agricultural output and well-informed decisions sustainability.

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Section: Literature Surveymentioning

confidence: 99%