AIMC Topic: Plant Diseases

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Challenges in explaining deep learning models for data with biological variation.

PloS one
Much machine learning research progress is based on developing models and evaluating them on a benchmark dataset (e.g., ImageNet for images). However, applying such benchmark-successful methods to real-world data often does not work as expected. This...

Hybrid deep learning for smart paddy disease diagnosis using self supervised hierarchical reconstruction and attention based temporal analysis.

Scientific reports
Accurate and early disease detection in paddy crops is essential for maximizing crop yield which ensures food security. Traditional methods are often labor-intensive, time-consuming, and domain-specific expertise. Feed-forward deep-learning models wi...

Structure-guided secretome analysis of gall-forming microbes offers insights into effector diversity and evolution.

eLife
Phytopathogens secrete effector molecules to manipulate host immunity and metabolism. Recent advances in structural genomics have identified fungal effector families whose members adopt similar folds despite sequence divergence, highlighting their im...

Predicting crop disease severity using real time weather variability through machine learning algorithms.

Scientific reports
Integrating disease severity with real-time meteorological variables and advanced machine learning techniques has provided valuable predictive insights for assessing disease severity in wheat. This study emphasizes the potential of machine learning m...

Haplotype stacking to improve stability of stripe rust resistance in wheat.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Genotype-by-environment interaction analysis and haplotype-level characterisation provide novel insights into the stability of stripe rust resistance. Breeding selection strategies are proposed to achieve rapid and stable genetic gains across environ...

Evaluating the effectiveness of the forest pests and diseases control methods on the industrial wood production using deep learning.

Scientific reports
Industrial wood production plays a vital role in the economies of many countries by supplying raw materials for a wide range of sectors, including construction, paper, and pulp industries. However, the industry is increasingly challenged by the detri...

EDDet: efficient deep-fusion and dynamic optimization for small target detection in eggplant diseases.

BMC plant biology
With the development of smart agriculture and the growth of the global population, vegetable production is facing the dual challenges of diversified planting environments and increased concealment of diseases. Eggplant, as an important economic crop,...

Multi-model machine learning for automated identification of rice diseases using leaf image data.

PloS one
Rice, a staple meal for about half of the world's population, is critical to global food security, especially in Asia. However, diseases have a severe impact on rice production, resulting in significant yield losses or outright crop failure. Traditio...

A robust hydroponic system for horticulture farming using deep learning, IoT, and mobile application.

PloS one
Due to limited literacy among root-level farmers, hydroponic farming in Bangladesh faces significant challenges. Therefore, there is a demand for easy-to-use technical systems to help farmers to monitor and operate smart systems. To address the issue...

A lightweight hybrid model for scalable and robust plant leaf disease classification.

Scientific reports
Plant leaf diseases significantly impact crop yield and quality, causing substantial economic loss and risking food security. Despite significant progress in the field of automated plant disease diagnosis, there are still several challenges that need...