CPDMS: a database system for crop physiological disorder management

TL;DR

This paper introduces a system for collecting and analyzing tomato crop images to manage physiological disorders using AI.

Contribution

The novel contribution is a database system with 58,479 tomato images under stress conditions for AI training and agricultural research.

Findings

A deep learning model achieved a mean Average Precision (mAP) of 0.46 and a recall rate of 0.60.

The system includes 24,000 images for AI model training and 13,037 for testing.

Data augmentation and hyperparameter tuning strategies were explored to improve AI performance.

Abstract

As the importance of precision agriculture grows, scalable and efficient methods for real-time data collection and analysis have become essential. In this study, we developed a system to collect real-time crop images, focusing on physiological disorders in tomatoes. This system systematically collects crop images and related data, with the potential to evolve into a valuable tool for researchers and agricultural practitioners. A total of 58 479 images were produced under stress conditions, including bacterial wilt (BW), Tomato Yellow Leaf Curl Virus (TYLCV), Tomato Spotted Wilt Virus (TSWV), drought, and salinity, across seven tomato varieties. The images include front views at 0 degrees, 120 degrees, 240 degrees, and top views and petiole images. Of these, 43 894 images were suitable for labeling. Based on this, 24 000 images were used for AI model training, and 13 037 images for model testing. By training a deep learning model, we achieved a mean Average Precision (mAP) of 0.46 and a recall rate of 0.60. Additionally, we discussed data augmentation and hyperparameter tuning strategies to improve AI model performance and explored the potential for generalizing the system across various agricultural environments. The database constructed in this study will serve as a crucial resource for the future development of agricultural AI.

Database URL: https://crops.phyzen.com/

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