GREEN SIGHT: AI ENHANCED PLANT CARE CROP RECOMMENDATIONS AND TAILORED FERTILISATION
Author(s):
Manish Vutukuru, Rahul Bhargav, Yarkala Vikas Goud
Keywords:
Abstract
The project aims to develop an agricultural decision-support system that utilizes convolutional neural networks (CNNs) and machine learning (ML) algorithms to revolutionize crop management practices. Employing CNN architectures like VGG16, ResNet9, and EfficientNetV2, the system extracts meaningful features from crop images, enabling it to accurately classify crops, identify diseases, and assess nutrient deficiencies. Additionally, ML algorithms like logistic regression, random forest, decision tree, SVM, and KNN are employed to build predictive models for crop selection and fertilizer recommendation, trained on extensive datasets. The system is deployed as a user-friendly website, allowing farmers to seamlessly upload crop images from their mobile devices. The system then analyses the images and provides comprehensive recommendations, including crop suggestions, disease identification, and fertilizer application advice, aiming to optimize yields and minimize environmental impact in precision agriculture. The crop recommendation module employing XG Boost algorithm gives an accuracy score 0f 99.3% and disease prediction module with Resnet 9 architecture gives an accuracy score of 99.2%.
Article Details
Unique Paper ID: 164580

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 1731 - 1738
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