Accurate detection of full-surface ear rot in maize using hyperspectral imaging and deep learning.
Maize ear rot severely restricts maize yield and quality, making the breeding of disease-resistant varieties the core strategy for disease prevention and control. It also achieved higher overall accuracy than traditional machine learning models such as random forest (RF), indicating that CNN-Bi-LSTM can achieve high-precision pixel-level detection of lesion regions showing Fusarium-associated maize ear rot symptoms.
Maize ear rot severely restricts maize yield and quality, making the breeding of disease-resistant varieties the core strategy for disease prevention and control. It also achieved higher overall accuracy than traditional machine learning models such as random forest (RF), indicating that CNN-Bi-LSTM can achieve high-precision pixel-level detection of lesion regions showing Fusarium-associated maize ear rot symptoms.
What the study was
- Study design
- Not specified
- Category
- Diagnostics
- Maturity
- Exploratory
- Journal
- Food research international (Ottawa, Ont.)
Why it surfaced
Relevant AI/ML in clinical diagnostics and imaging study (promising preliminary signal) meets standard-priority threshold.
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