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A Convolutional Neural Network Approach to Robust Crop Health Monitoring
  • Soham Jariwala,
  • Reetu Jain
Soham Jariwala
Fazlani lacademie globale School

Corresponding Author:[email protected]

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Reetu Jain
Mentor, On my own technology


Convolutional Neural Networks (CNNs) have opened up new possibilities in a variety of fields, including plant pathology. Through image analysis, this study aims to take advantage of CNNs' capacity to recognize and categorize plant illnesses. Automated disease identification has potential for prompt intervention and crop preservation in modern times, when agricultural output assumes critical relevance. This work elaborates on the application and assessment of a CNN-based model designed for the detection of plant diseases. The technology separates healthy plants from those with problems by taking pictures of plants and putting them through the trained model. The growing demand for effective disease monitoring and management in the agriculture industry highlights the urgency of this research. The suggested model shows promising accuracy and dependability by utilising a large dataset and stringent evaluation metrics. This research highlights the potential of CNNs as a workable tool for upgrading agricultural practices by using a systematic approach. The results of this study shed light on both the strengths and weaknesses of CNNs in the context of automated plant disease diagnosis.
05 Mar 2024Submitted to TechRxiv
06 Mar 2024Published in TechRxiv