AnarMitra: Enhancing Pomegranate Farming with CNN Technology

Abstract

This study introduces AnarMitra, a mobile application designed to revolutionize pomegranate cultivation practices. AnarMitra employs machine learning algorithms to swiftly assess ripeness levels and detect prevalent diseases in pomegranates, such as bacterial blight and fungal infections. Utilizing smartphone cameras, farmers can effortlessly capture images for immediate feedback on fruit quality and health. The paper details AnarMitra\'s development process, including model training with diverse datasets and algorithmic methodologies for classification and disease detection. Field trials validate AnarMitra\'s efficacy in enhancing productivity and fostering sustainable agricultural practices, empowering farmers with timely insights for informed decision-making and crop management.

Introduction

I. INTRODUCTION

Pomegranates, celebrated for their delicious flavor and abundant health benefits, are cultivated globally, yet growers often face challenges such as unpredictable ripening patterns and disease prevalence. Accurate assessment of fruit ripeness and timely disease detection are crucial for ensuring high-quality yields and minimizing crop losses. Traditional methods for evaluating pomegranate ripeness and identifying diseases are labor-intensive, subjective, and prone to errors. Hence, there is a pressing need for innovative technologies to streamline these processes and provide reliable insights to farmers. Addressing this need, we introduce AnarMitra, an advanced mobile application utilizing convolutional neural network (CNN) technology to revolutionize pomegranate farming practices. AnarMitra offers a novel approach to ripeness assessment and disease detection, empowering growers to make informed decisions in real time and optimize their farming operations.

In this paper, we introduce AnarMitra, a mobile application poised to transform the pomegranate industry by addressing significant challenges faced by growers. We emphasize the importance of pomegranates as a valuable crop and the obstacles encountered in effective orchard management. Leveraging CNN technology, we discuss the efficiency, accuracy, and scalability advantages of AnarMitra for ripeness assessment and disease detection in agriculture. Key features such as a user-friendly interface and integration with smartphone cameras are highlighted. Through field trials and performance evaluations, we demonstrate AnarMitra's effectiveness in accurately assessing ripeness levels and identifying diseases. Overall, AnarMitra emerges as a transformative tool for enhancing productivity, sustainability, and profitability in pomegranate cultivation, bridging the gap between technology and agriculture in the digital era.

Conclusion

In conclusion, our research demonstrates the effectiveness of utilizing a Convolutional Neural Network (CNN) model for the detection of pomegranate fruit diseases. Through rigorous evaluation using metrics such as Precision, Recall, and F1-Score, we have confirmed the model\'s ability to accurately identify diseased pomegranate fruits across diverse conditions. This validation underscores the practical applicability of CNN technology in agricultural contexts, offering farmers a powerful tool for early disease detection and crop management. By harnessing the capabilities of CNN-based systems, we pave the way for more efficient and precise interventions in disease management, ultimately leading to improved yields and sustainability in pomegranate cultivation. Our findings highlight the potential of CNN models to revolutionize agricultural practices and contribute to the global effort towards food security and agricultural sustainability.

References

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Copyright

Copyright © 2024 Prof. Sayali Dhumal, Tushar Sable, Yash Patil, Lambodar Waghmare, Vrushabh Pawar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.