AIMC Topic: Fruit

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An intelligent agriculture management system for rainfall prediction and fruit health monitoring.

Scientific reports
Contrary to popular belief, agriculture is becoming more data-driven with artificial intelligence and Internet-of-Things (IoT) playing crucial roles. In this paper, the integrated processing executed by various sensors combined as an IoT pack and dri...

How to use machine learning and fuzzy cognitive maps to test hypothetical scenarios in health behavior change interventions: a case study on fruit intake.

BMC public health
BACKGROUND: Intervention planners use logic models to design evidence-based health behavior interventions. Logic models that capture the complexity of health behavior necessitate additional computational techniques to inform decisions with respect to...

Blackberry Fruit Classification in Underexposed Images Combining Deep Learning and Image Fusion Methods.

Sensors (Basel, Switzerland)
Berry production is increasing worldwide each year; however, high production leads to labor shortages and an increase in wasted fruit during harvest seasons. This problem opened new research opportunities in computer vision as one main challenge to a...

Rapid detection of residual chlorpyrifos and pyrimethanil on fruit surface by surface-enhanced Raman spectroscopy integrated with deep learning approach.

Scientific reports
Chlorpyrifos and pyrimethanil are widely used insecticides/fungicides in agriculture. The residual pesticides/fungicides remaining in fruits and vegetables may do harm to human health if they are taken without notice by the customers. Therefore, it i...

Bifurcations of a delayed fractional-order BAM neural network via new parameter perturbations.

Neural networks : the official journal of the International Neural Network Society
This paper makes a new breakthrough in deliberating the bifurcations of fractional-order bidirectional associative memory neural network (FOBAMNN). In the beginning, the corresponding bifurcation results are established according to self-regulating p...

Artificial neural network-based shelf life prediction approach in the food storage process: A review.

Critical reviews in food science and nutrition
The prediction of food shelf life has become a vital tool for distributors and consumers, enabling them to determine storage and optimal edible time, thus avoiding unexpected food waste. Artificial neural network (ANN) have emerged as an effective, f...

Deep learning supported machine vision system to precisely automate the wild blueberry harvester header.

Scientific reports
An operator of a wild blueberry harvester faces the fatigue of manually adjusting the height of the harvester's head, considering spatial variations in plant height, fruit zone, and field topography affecting fruit yield. For stress-free harvesting o...

Fruit Sizing in Orchard: A Review from Caliper to Machine Vision with Deep Learning.

Sensors (Basel, Switzerland)
Forward estimates of harvest load require information on fruit size as well as number. The task of sizing fruit and vegetables has been automated in the packhouse, progressing from mechanical methods to machine vision over the last three decades. Thi...

Real-Time Recognition and Localization Based on Improved YOLOv5s for Robot's Picking Clustered Fruits of Chilies.

Sensors (Basel, Switzerland)
Chili recognition is one of the critical technologies for robots to pick chilies. The robots need locate the fruit. Furthermore, chilies are always planted intensively and their fruits are always clustered. It is a challenge to recognize and locate t...

Three-dimensional continuous picking path planning based on ant colony optimization algorithm.

PloS one
Fruit-picking robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology, people are demanding higher picking efficiency from fruit-picki...