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Plant Leaves

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Electronic thygmonasty model inbiomimetic robot.

Bioinspiration & biomimetics
Direct contact of random objects from the open environment to the panel surface of an electronic device may reduce the work efficiency and cause permanent damage. However, there is a possible way to solve this problem, notably by implementing an adap...

Imaging and Deep Learning Based Approach to Leaf Wetness Detection in Strawberry.

Sensors (Basel, Switzerland)
The Strawberry Advisory System (SAS) is a tool developed to help Florida strawberry growers determine the risk of common fungal diseases and the need for fungicide applications. Leaf wetness duration (LWD) is one of the important parameters in SAS di...

A robust deep learning approach for tomato plant leaf disease localization and classification.

Scientific reports
Tomato plants' disease detection and classification at the earliest stage can save the farmers from expensive crop sprays and can assist in increasing the food quantity. Although, extensive work has been presented by the researcher for the tomato pla...

Detection of Tip-Burn Stress on Lettuce Grown in an Indoor Environment Using Deep Learning Algorithms.

Sensors (Basel, Switzerland)
Lettuce grown in indoor farms under fully artificial light is susceptible to a physiological disorder known as tip-burn. A vital factor that controls plant growth in indoor farms is the ability to adjust the growing environment to promote faster crop...

An Improved Deep Residual Convolutional Neural Network for Plant Leaf Disease Detection.

Computational intelligence and neuroscience
In this research, we proposed a novel deep residual convolutional neural network with 197 layers (ResNet197) for the detection of various plant leaf diseases. Six blocks of layers were used to develop ResNet197. ResNet197 was trained and tested using...

Multimodal Hybrid Deep Learning Approach to Detect Tomato Leaf Disease Using Attention Based Dilated Convolution Feature Extractor with Logistic Regression Classification.

Sensors (Basel, Switzerland)
Automatic leaf disease detection techniques are effective for reducing the time-consuming effort of monitoring large crop farms and early identification of disease symptoms of plant leaves. Although crop tomatoes are seen to be susceptible to a varie...

Estimation of Greenhouse Lettuce Growth Indices Based on a Two-Stage CNN Using RGB-D Images.

Sensors (Basel, Switzerland)
Growth indices can quantify crop productivity and establish optimal environmental, nutritional, and irrigation control strategies. A convolutional neural network (CNN)-based model is presented for estimating various growth indices (i.e., fresh weight...

Learning-Based Slip Detection for Robotic Fruit Grasping and Manipulation under Leaf Interference.

Sensors (Basel, Switzerland)
Robotic harvesting research has seen significant achievements in the past decade, with breakthroughs being made in machine vision, robot manipulation, autonomous navigation and mapping. However, the missing capability of obstacle handling during the ...

Application of image processing and soft computing strategies for non-destructive estimation of plum leaf area.

PloS one
Plant leaf area (LA) is a key metric in plant monitoring programs. Machine learning methods were used in this study to estimate the LA of four plum genotypes, including three greengage genotypes (Prunus domestica [subsp. italica var. claudiana.]) and...

Diagnosis of Alternaria disease and leafminer pest on tomato leaves using image processing techniques.

Journal of the science of food and agriculture
BACKGROUND: Diseases such as Alternaria and pests such as leafminer threaten tomato as one of the most widely used agricultural products. These pests and diseases first damage the leaves of tomatoes, then the flowers, and finally the fruit. Therefore...