Viewpoint Analysis for Maturity Classification of Sweet Peppers

Abstract

<jats:p>The effect of camera viewpoint and fruit orientation on the performance of a sweet pepper maturity level classification algorithm was evaluated. Image datasets of sweet peppers harvested from a commercial greenhouse were collected using two different methods, resulting in 789 RGB—Red Green Blue (images acquired in a photocell) and 417 RGB-D—Red Green Blue-Depth (images acquired by a robotic arm in the laboratory), which are published as part of this paper. Maturity level classification was performed using a random forest algorithm. Classifications of maturity level from different camera viewpoints, using a combination of viewpoints, and different fruit orientations on the plant were evaluated and compared to manual classification. Results revealed that: (1) the bottom viewpoint is the best single viewpoint for maturity level classification accuracy; (2) information from two viewpoints increases the classification by 25 and 15 percent compared to a single viewpoint for red and yellow peppers, respectively, and (3) classification performance is highly dependent on the fruit’s orientation on the plant.</jats:p>

A comparative analysis of the discrimination of pepper (Capsicum annuum L.) based on the cross‐section and seed textures determined using image processing

“…Among others, Hernández‐Carrión, Hernando, Sotelo‐Díaz, Quintanilla‐Carvajal, and Quiles (2015) reported the usefulness of morphometric and texture image analysis for the evaluation of microstructure of red sweet pepper subjected to pasteurization and high hydrostatic pressure. Processing of images of sweet pepper may be also applied, for example, to classify fruit with different maturity (Elhariri et al, 2014; Harel, van Essen, Parmet, & Edan, 2020). The own results and data in the available literature prove that image analysis is very useful in pepper examination and various aspects of research may be continued and expanded.…”

Optimization Model for Selective Harvest Planning Performed by Humans and Robots

“…In these studies, the harvest date was set for non-selective harvesting, i.e., harvesting the whole field at once. A different planning model for wine grape harvesting involving uncertainty combined integer linear programming and robust stochastic optimization [26]. Other studies have used dynamic programming [10] and simulation models [46] to solve problems in harvest planning.…”

“…First, methods for maturity classification must be developed [24,25]. Second, since many fruits are hidden or partially obscured, the best camera viewpoints for maturity classification must be selected [26]. Since additional viewpoints cost time, an intelligent decision as to the necessity for an additional viewpoint and its location is needed [27].…”

Selective fruit harvesting: Research, trends and developments towards fruit detection and localization – A review

“…Analysis about the orientation and camera viewpoint from the images of RGB camera with photocell for constant illumination and RGB-D cameras has been presented. 31 The images are processed in HSV colour space and classified using random forest algorithm. It has been found that orientation of fruit with plant plays a main role in performance improvement and in the view points, bottom view is the best one for estimating the maturity level of sweet pepper.…”