AIMC Topic: Plant Diseases

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Potato plant disease detection: leveraging hybrid deep learning models.

BMC plant biology
Agriculture, a crucial sector for global economic development and sustainable food production, faces significant challenges in detecting and managing crop diseases. These diseases can greatly impact yield and productivity, making early and accurate d...

Research on agricultural disease recognition methods based on very large Kernel convolutional network-RepLKNet.

Scientific reports
Agricultural diseases pose significant challenges to plant production. With the rapid advancement of deep learning, the accuracy and efficiency of plant disease identification have substantially improved. However, conventional convolutional neural ne...

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) ...

A novel hybrid fruit fly and simulated annealing optimized faster R-CNN for detection and classification of tomato plant leaf diseases.

Scientific reports
Modern agriculture increasingly relies on technologies that enhance farmers' efficiency and economic growth. One challenge is the accurate identification of disease-affected plants, whose characteristics like structure, size, texture, and color can v...

Smart plant disease net: Adaptive Dense Hybrid Convolution network with attention mechanism for IoT-based plant disease detection by improved optimization approach.

Network (Bristol, England)
Plant diseases are rising nowadays. Plant diseases lead to high economic losses. Internet of Things (IoT) technology has found its application in various sectors. This led to the introduction of smart farming, in which IoT has been utilized to help i...

Multi-convolutional neural networks for cotton disease detection using synergistic deep learning paradigm.

PloS one
Cotton is a major cash crop, and increasing its production is extremely important worldwide, especially in agriculture-led economies. The crop is susceptible to various diseases, leading to decreased yields. In recent years, advancements in deep lear...

Lightweight wavelet-CNN tea leaf disease detection.

PloS one
Tea diseases can significantly impact crop yield and quality, necessitating accurate and efficient recognition methods. This study presents WaveLiteNet, a lightweight model designed for tea disease recognition, addressing the challenge of inadequate ...

Apple varieties, diseases, and distinguishing between fresh and rotten through deep learning approaches.

PloS one
Apples are one of the most productive fruits in the world, in addition to their nutritional and health advantages for humans. Even with the continuous development of AI in agriculture in general and apples in particular, automated systems continue to...

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...

Early and late blight disease identification in tomato plants using a neural network-based model to augmenting agricultural productivity.

Science progress
Computer-advanced technologies have a significant impact across various fields. It is widely recognized that diseases have a detrimental effect on crop productivity and can significantly impact the economy, particularly in agricultural countries. Tom...