AIMC Topic: Fruit

Clear Filters Showing 141 to 150 of 219 articles

Central Object Segmentation by Deep Learning to Continuously Monitor Fruit Growth through RGB Images.

Sensors (Basel, Switzerland)
Monitoring fruit growth is useful when estimating final yields in advance and predicting optimum harvest times. However, observing fruit all day at the farm via RGB images is not an easy task because the light conditions are constantly changing. In t...

On the Prediction of Biogas Production from Vegetables, Fruits, and Food Wastes by ANFIS- and LSSVM-Based Models.

BioMed research international
This study is aimed at modeling biodigestion systems as a function of the most influencing parameters to generate two robust algorithms on the basis of the machine learning algorithms, including adaptive network-based fuzzy inference system (ANFIS) a...

Polydopamine and silica nanoparticles magnetic solid phase extraction coupled with liquid chromatography-tandem mass spectrometry to determine phenolic acids and flavonoids in fruit wine.

Journal of food and drug analysis
Magnetic solid phase extraction (MSPE) have been widely applied in a variety of sample preparation techniques. Herein, FeO@pDA as the sorbents for MSPE, were developed for the determination of phenolic acids and flavonoids in fruit wine samples in co...

Development of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits.

PloS one
This investigation aimed to develop a method to predict the total soluble solids (TSS), titratable acidity, TSS/titratable acidity, vitamin C, anthocyanin, and total carotenoids contents using surface color values (L*, Hue and chroma), single fruit w...

Identifying the Branch of Kiwifruit Based on Unmanned Aerial Vehicle (UAV) Images Using Deep Learning Method.

Sensors (Basel, Switzerland)
It is important to obtain accurate information about kiwifruit vines to monitoring their physiological states and undertake precise orchard operations. However, because vines are small and cling to trellises, and have branches laying on the ground, n...

A System Using Artificial Intelligence to Detect and Scare Bird Flocks in the Protection of Ripening Fruit.

Sensors (Basel, Switzerland)
Flocks of birds may cause major damage to fruit crops in the ripening phase. This problem is addressed by various methods for bird scaring; in many cases, however, the birds become accustomed to the distraction, and the applied scaring procedure lose...

Application of a multilayer perceptron artificial neural network for identification of peach cultivars based on physical characteristics.

PeerJ
In the fresh fruit industry, identification of fruit cultivars and fruit quality is of vital importance. In the current study, nine peach cultivars (Dixon, Early Grande, Flordaprince, Flordastar, Flordaglo, Florda 834, TropicSnow, Desertred, and Swel...

The Concept of the Constructional Solution of the Working Section of a Robot for Harvesting Strawberries.

Sensors (Basel, Switzerland)
Strawberry fruits are products of high commercial and consumption value, and, at the same time, they are difficult to harvest due to their very low mechanical strength and difficulties in identifying them within the bush. Therefore, robots collecting...

In-Field Automatic Detection of Grape Bunches under a Totally Uncontrolled Environment.

Sensors (Basel, Switzerland)
An early estimation of the exact number of fruits, flowers, and trees helps farmers to make better decisions on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on ma...

Multimodal Deep Learning and Visible-Light and Hyperspectral Imaging for Fruit Maturity Estimation.

Sensors (Basel, Switzerland)
Fruit maturity is a critical factor in the supply chain, consumer preference, and agriculture industry. Most classification methods on fruit maturity identify only two classes: ripe and unripe, but this paper estimates six maturity stages of papaya f...