AIMC Topic: Crops, Agricultural

Clear Filters Showing 11 to 20 of 243 articles

Lightweight dual-stage feature refinement for black gram leaf disease classification using ConViTSE.

Scientific reports
Black gram, also known as urad bean, is an economically crucial crop widely cultivated in India, particularly in the central and southern regions. However, black gram is highly prone to multiple leaf diseases, resulting in considerable crop losses an...

Detection of commercial crop weeds using machine learning algorithms.

Scientific reports
This work investigates the YOLOv5 object detection algorithms for classifying commercial crops such as tomatoes, chili, and cotton. The data sets comprise 707 images of green chillies, 200 images of tomato crops and 130 images of weeds from Ponnandag...

An interpretable crop leaf disease and pest identification model based on prototypical part network and contrastive learning.

Scientific reports
The disease and pest recognition algorithms based on computer vision can automatically process and analyze a large amount of disease and pest images, thereby achieving rapid and accurate identification of disease and pest categories on crop leaves. C...

IoT integrated CNN framework for automated detection and quantification of rice and potato crop diseases.

Scientific reports
In modern precision agriculture, early and accurate identification of crop diseases is crucial for reducing yield loss and minimizing pesticide overuse. This study proposes an IoT-enabled framework that integrates convolutional neural networks (CNNs)...

A neural architecture search optimized lightweight attention ensemble model for nutrient deficiency and severity assessment in diverse crop leaves.

Scientific reports
The growth and productivity of banana crops are critically affected by micronutrient deficiencies, which are often difficult to detect at early stages. Lightweight deep learning models, optimized through neural architecture search (NAS) and attention...

Harnessing multi-omics and genome-editing technologies for climate-resilient agriculture: bridging AI-driven insights with sustainable crop improvement.

Plant molecular biology
Environmental challenges such as drought, salinity, heavy metal contamination, and nutrient deficiencies threaten global agricultural productivity and food security. These stressors drastically reduce crop yields, necessitating innovative solutions. ...

A comparative study highlights superiority of LSTM in crop genomic prediction.

Planta
We systematically evaluated three key determinants affecting prediction accuracy and the algorithm performance differences based on fifteen state-of-the-art GP methods, and found LSTM suitable for capturing additive and epistatic effects. Genomic pre...

An interpretable machine learning approach based on SHAP, Sobol and LIME values for precise estimation of daily soybean crop coefficients.

Scientific reports
Increasing water scarcity and climate variability have intensified the need for precise agricultural irrigation management. Accurate estimation of crop coefficients (Kc) is critical for determining crop water requirements, especially in arid and semi...

Rapid and accurate detection of crop viruses by nano-electrochemical sensors.

Analytical methods : advancing methods and applications
This review aims to critically examine the development, capabilities, and future prospects of nano-electrochemical sensors as a next-generation solution for the rapid and accurate detection of crop viruses. The motivation stems from the urgent need t...

Optimizing selective breeding of livestock and forage crops to reduce the environmental impacts of grass-based dairy production by combining life cycle assessment and machine learning.

The Science of the total environment
Global demand for ruminant milk-based products is increasing, contributing to increases in associated environmental impacts. Yet, most efforts to reduce the total environmental impact of dairy production are based on livestock breeding and manipulati...