AIMC Topic: Solanum lycopersicum

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Detection and Segmentation of Mature Green Tomatoes Based on Mask R-CNN with Automatic Image Acquisition Approach.

Sensors (Basel, Switzerland)
Since the mature green tomatoes have color similar to branches and leaves, some are shaded by branches and leaves, and overlapped by other tomatoes, the accurate detection and location of these tomatoes is rather difficult. This paper proposes to use...

Machine learning approach for automatic recognition of tomato-pollinating bees based on their buzzing-sounds.

PLoS computational biology
Bee-mediated pollination greatly increases the size and weight of tomato fruits. Therefore, distinguishing between the local set of bees-those that are efficient pollinators-is essential to improve the economic returns for farmers. To achieve this, i...

Cherry Tomato Production in Intelligent Greenhouses-Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality.

Sensors (Basel, Switzerland)
Greenhouses and indoor farming systems play an important role in providing fresh and nutritious food for the growing global population. Farms are becoming larger and greenhouse growers need to make complex decisions to maximize production and minimiz...

Investigating plant uptake of organic contaminants through transpiration stream concentration factor and neural network models.

The Science of the total environment
Uptake of seven organic contaminants including bisphenol A, estriol, 2,4-dinitrotoluene, N,N-diethyl-meta-toluamide (DEET), carbamazepine, acetaminophen, and lincomycin by tomato (Solanum lycopersicum L.), corn (Zea mays L.), and wheat (Triticum aest...

Intact Detection of Highly Occluded Immature Tomatoes on Plants Using Deep Learning Techniques.

Sensors (Basel, Switzerland)
Automatic detection of intact tomatoes on plants is highly expected for low-cost and optimal management in tomato farming. Mature tomato detection has been wildly studied, while immature tomato detection, especially when occluded with leaves, is diff...

Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques.

Computational intelligence and neuroscience
This study develops tomato disease detection methods based on deep convolutional neural networks and object detection models. Two different models, Faster R-CNN and Mask R-CNN, are used in these methods, where Faster R-CNN is used to identify the typ...

Machine Learning Approaches to Improve Three Basic Plant Phenotyping Tasks Using Three-Dimensional Point Clouds.

Plant physiology
Developing automated methods to efficiently process large volumes of point cloud data remains a challenge for three-dimensional (3D) plant phenotyping applications. Here, we describe the development of machine learning methods to tackle three primary...

Design of a tomato classifier based on machine vision.

PloS one
This paper attempts to design an automated, efficient and intelligent tomato grading method that facilitates the graded selling of the fruit. Based on machine vision, the color images of tomatoes with different morphologies were studied, and the colo...

Combined network analysis and machine learning allows the prediction of metabolic pathways from tomato metabolomics data.

Communications biology
The identification and understanding of metabolic pathways is a key aspect in crop improvement and drug design. The common approach for their detection is based on gene annotation and ontology. Correlation-based network analysis, where metabolites ar...

A Mature-Tomato Detection Algorithm Using Machine Learning and Color Analysis.

Sensors (Basel, Switzerland)
An algorithm was proposed for automatic tomato detection in regular color images to reduce the influence of illumination and occlusion. In this method, the Histograms of Oriented Gradients (HOG) descriptor was used to train a Support Vector Machine (...