Volumetric mass flow sensor for citrus mechanical harvesting machines

State‐of‐the‐art technologies in precision agriculture: a systematic review

“…A systematic literature search following the PRISMA guideline was performed on 20 February 2019 in two digital scientific journal database: Science Direct and IEEE Xplore. Except searching these databases, some reference search was also done from sources like from the reference of selected papers in Science Direct and IEEE Xplore, and articles suggested by the expert which were not available in the searched database with the search string . Relevant literatures were identified based on the eligibility criteria fixed by the authors.…”

“…Except searching these databases, some reference search was also done from sources like from the reference of selected papers in Science Direct and IEEE Xplore, and articles suggested by the expert which were not available in the searched database with the search string. [12][13][14][15][16][17][18][19][20][21][22][23][24][25] Relevant literatures were identified based on the eligibility criteria fixed by the authors.…”

“…According to this protocol a total 4104 number of articles were searched primarily from Science Direct and IEEE Xplore database. Another 14 articles [12][13][14][15][16][17][18][19][20][21][22][23][24][25] were identified from sources such as from the reference of selected papers, and articles suggested by the expert which were not available in the searched database with the search string. Therefore, a total of 4118 articles were first selected, then 65 duplicate articles were removed.…”

Evaluation of a LiDAR-based 3D-stereoscopic vision system for crop-monitoring applications

“…For example, LiDAR was used to: determine the volume of perennial biomass of Vitis vinifera L. to facilitate the calculation of annual biomass production, accumulation and carbon cycle within the vineyard (Keightley and Bawden, 2010); evaluate the vegetative density of Miscanthus  giganteus (Zhang and Grift, 2012); quantify the biomass of winter rye (Ehlert and Heisig, 2013) and other species (Ehlert et al, 2008); obtain the vertical plant area density profiles of wheat (Triticum aestivum L.) canopy at different growth stages (Hosoi and Omasa, 2009); estimate the crop density in front of a combine harvester, together with different methods of online data-processing (Saeys et al, 2009); estimate the vertical profile of plant area density of rice (Hosoi and Omasa, 2012). In all the examples above, scans are aimed at increasing the information content of the operator/ end-user with respect to the crop in the field, but there are also examples where a LiDAR device was used on plants that are antagonistic of the cultivation, for example to characterize the weed vegetation in the inter-row area of a maize field (Andújar et al, 2013), or on the harvested fruits on a conveyor, to estimate their bulk volume and total mass (Jadhav et al, 2014). In this work, following the approach of Sanz-Cortiella et al (2011) and Lee and Ehsani (2009) about a ground-level tractorbased scanning, the development of a LiDAR system integrated in an autonomous or tele-operated mobile vehicle is presented.…”

Semi-supervised learning algorithm to estimate mass flow from sparsely annotated images using a vision system (SLEM)

“…In this chapter we describe a volumetric-based approach to estimate mass flow of flowing material in a controlled environment setting (laboratory setup). To our knowledge, there has not been research that used an indirect volumetric-based approach with point clouds to estimate the yield of sugarcane; however, Jadhav et al [33] developed a volumetric mass flow sensor that uses a LiDAR sensor to estimate the total mass of citrus. Their system was tested on an inclined and a horizontal conveyors like those used on mechanical harvester and debris removal system.…”