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Crops, Agricultural

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A dual-branch model combining convolution and vision transformer for crop disease classification.

PloS one
Computer vision holds tremendous potential in crop disease classification, but the complex texture and shape characteristics of crop diseases make disease classification challenging. To address these issues, this paper proposes a dual-branch model fo...

Origin traceability of agricultural products: A lightweight collaborative neural network for spectral information processing.

Food research international (Ottawa, Ont.)
The natural conditions of various regions, including climate, soil, and water quality, significantly influence the nutrient composition and quality of agricultural products. Identifying the origin of agricultural products can prevent adulteration, im...

Fine extraction of multi-crop planting area based on deep learning with Sentinel- 2 time-series data.

Environmental science and pollution research international
Accurate and timely access to the spatial distribution of crops is crucial for sustainable agricultural development and food security. However, extracting multi-crop areas based on high-resolution time-series data and deep learning still faces challe...

Deep learning based abiotic crop stress assessment for precision agriculture: A comprehensive review.

Journal of environmental management
Abiotic stresses are a leading cause of crop loss and a severe peril to global food security. Precise and prompt identification of abiotic stresses in crops is crucial for effective mitigation strategies. In recent years, Deep learning (DL) technique...

Novel hybrid transfer neural network for wheat crop growth stages recognition using field images.

Scientific reports
Wheat is one of the world's most widely cultivated cereal crops and is a primary food source for a significant portion of the population. Wheat goes through several distinct developmental phases, and accurately identifying these stages is essential f...

Hybrid vision GNNs based early detection and protection against pest diseases in coffee plants.

Scientific reports
Agriculture is an essential foundation that supports numerous economies, and the longevity of the coffee business is of paramount significance. Controlling and safeguarding coffee farms from harmful pests, including the Coffee Berry Borer, Mealybugs,...

Hybrid deep learning model for density and growth rate estimation on weed image dataset.

Scientific reports
Agriculture research is particularly essential since crop production is a challenge for farmers in India and around the world. 37% of the crop is impacted by invasive plants (weeds). Those unwelcome plants that interbreed with cultivated crops and de...

Predicting land suitability for wheat and barley crops using machine learning techniques.

Scientific reports
Ensuring food security to meet the demands of a growing population remains a key challenge, especially for developing countries like Ethiopia. There are various policies and strategies designed by the government and stakeholders to confront the chall...

AI-IoT based smart agriculture pivot for plant diseases detection and treatment.

Scientific reports
There are some key problems faced in modern agriculture that IoT-based smart farming. These problems such shortage of water, plant diseases, and pest attacks. Thus, artificial intelligence (AI) technology cooperates with the Internet of Things (IoT) ...

Segmentation-based lightweight multi-class classification model for crop disease detection, classification, and severity assessment using DCNN.

PloS one
Leaf diseases in Zea mays crops have a significant impact on both the calibre and volume of maize yield, eventually impacting the market. Prior detection of the intensity of an infection would enable the efficient allocation of treatment resources an...