Abstract:To solve the problems of a large number of clods remaining in potatoes after mechanized harvesting in northern heavy clay soil planting areas in China and requiring much labor to separate clods from potatoes, which leads to a heavy workload, inefficiency and a low cleaning rate, an RGB-D-based Mask R-CNN dynamic potato identification model is established by using acquired RBG-D image data of untreated potatoes after mechanized harvesting, and a potato cleaning method is presented in this paper. This makes it p… Show more
“…However it was a hypothetical proposal, no potato harvester was actually automated. As such the highest level of automation was achieved by Fu et al. (2022) , with their autonomous potato cleaner.…”
Section: Discussionmentioning
confidence: 99%
“…China, India, Germany, and Australia were all assigned Level 2 due to reviewed papers from these countries discussing fully-mechanised harvesters [ Fu et al. (2022) ; Gulati (2019) ; Schneider et al.…”
Section: Discussionmentioning
confidence: 99%
“…(2019) ; Gulati (2019) ]. Despite this, they have both produced papers in the last five years discussing the use of fully-mechanised harvesters [ Gulati (2019) ; Fu et al. (2022) ].…”
Section: Discussionmentioning
confidence: 99%
“…(2022) ]. Due to the heavy clay soil found in Northern China, their research revolves around removing soil after extraction [ Fu et al. (2022) ; Wei et al.…”
Section: Potato Harvesting: An International Assessmentmentioning
confidence: 99%
“…Since China achieved the top spot in 1993, the nation has been pushing campaigns to increase its consumption of this food group [Devaux et al (2021)]. Harvesting in China is split between fully-and semi-mechanized harvesters, with the majority of harvesting being semi-mechanized [Wei et al (2019); Issa et al (2020); Fu et al (2022)]. Due to the heavy clay soil found in Northern China, their research revolves around removing soil after extraction [Fu et al (2022); Wei et al (2019)].…”
Potatoes are the fourth most important crop for human consumption. In the 18 century, potatoes saved the European population from starvation, and since then, it has become one of the primary crops cultivated in countries such as Spain, France, Germany, Ukraine and the United Kingdom. Potato production worldwide reached 368.8 million tonnes in 2019, 371.1 million tonnes in 2020, and 376.1 million tonnes in 2021, with production expected to grow alongside the worldwide population. However, the agricultural sector is currently suffering from urbanization. With the next generation of farmers relocating to cities, there is a diminishing and ageing agricultural workforce. Consequently, farms urgently need innovation, particularly from a technology perspective. As a result, this work is focused on reviewing the worldwide developments in potato harvesting, with an emphasis on mechatronics, the use of intelligent systems and the opportunities that arise from applications utilising the Internet of Things (IoT). Our work covers worldwide scientific publications in the last five years, sustained by public data made available from different governments. We end our review by providing a discussion on the future trends derived from our analysis.
“…However it was a hypothetical proposal, no potato harvester was actually automated. As such the highest level of automation was achieved by Fu et al. (2022) , with their autonomous potato cleaner.…”
Section: Discussionmentioning
confidence: 99%
“…China, India, Germany, and Australia were all assigned Level 2 due to reviewed papers from these countries discussing fully-mechanised harvesters [ Fu et al. (2022) ; Gulati (2019) ; Schneider et al.…”
Section: Discussionmentioning
confidence: 99%
“…(2019) ; Gulati (2019) ]. Despite this, they have both produced papers in the last five years discussing the use of fully-mechanised harvesters [ Gulati (2019) ; Fu et al. (2022) ].…”
Section: Discussionmentioning
confidence: 99%
“…(2022) ]. Due to the heavy clay soil found in Northern China, their research revolves around removing soil after extraction [ Fu et al. (2022) ; Wei et al.…”
Section: Potato Harvesting: An International Assessmentmentioning
confidence: 99%
“…Since China achieved the top spot in 1993, the nation has been pushing campaigns to increase its consumption of this food group [Devaux et al (2021)]. Harvesting in China is split between fully-and semi-mechanized harvesters, with the majority of harvesting being semi-mechanized [Wei et al (2019); Issa et al (2020); Fu et al (2022)]. Due to the heavy clay soil found in Northern China, their research revolves around removing soil after extraction [Fu et al (2022); Wei et al (2019)].…”
Potatoes are the fourth most important crop for human consumption. In the 18 century, potatoes saved the European population from starvation, and since then, it has become one of the primary crops cultivated in countries such as Spain, France, Germany, Ukraine and the United Kingdom. Potato production worldwide reached 368.8 million tonnes in 2019, 371.1 million tonnes in 2020, and 376.1 million tonnes in 2021, with production expected to grow alongside the worldwide population. However, the agricultural sector is currently suffering from urbanization. With the next generation of farmers relocating to cities, there is a diminishing and ageing agricultural workforce. Consequently, farms urgently need innovation, particularly from a technology perspective. As a result, this work is focused on reviewing the worldwide developments in potato harvesting, with an emphasis on mechatronics, the use of intelligent systems and the opportunities that arise from applications utilising the Internet of Things (IoT). Our work covers worldwide scientific publications in the last five years, sustained by public data made available from different governments. We end our review by providing a discussion on the future trends derived from our analysis.
Pod phenotypic traits are closely related to grain yield and quality. Pod phenotype detection in soybean populations in natural environments is important to soybean breeding, cultivation, and field management. For an accurate pod phenotype description, a dynamic detection method is proposed based on an improved YOLO-v5 network. First, two varieties were taken as research objects. A self-developed field soybean three-dimensional color image acquisition vehicle was used to obtain RGB and depth images of soybean pods in the field. Second, the red–green–blue (RGB) and depth images were registered using an edge feature point alignment metric to accurately distinguish complex environmental backgrounds and establish a red–green–blue-depth (RGB-D) dataset for model training. Third, an improved feature pyramid network and path aggregation network (FPN+PAN) structure and a channel attention atrous spatial pyramid pooling (CA-ASPP) module were introduced to improve the dim and small pod target detection. Finally, a soybean pod quantity compensation model was established by analyzing the influence of the number of individual plants in the soybean population on the detection precision to statistically correct the predicted pod quantity. In the experimental phase, we analyzed the impact of different datasets on the model and the performance of different models on the same dataset under the same test conditions. The test results showed that compared with network models trained on the RGB dataset, the recall and precision of models trained on the RGB-D dataset increased by approximately 32% and 25%, respectively. Compared with YOLO-v5s, the precision of the improved YOLO-v5 increased by approximately 6%, reaching 88.14% precision for pod quantity detection with 200 plants in the soybean population. After model compensation, the mean relative errors between the predicted and actual pod quantities were 2% to 3% for the two soybean varieties. Thus, the proposed method can provide rapid and massive detection for pod phenotyping in soybean populations and a theoretical basis and technical knowledge for soybean breeding, scientific cultivation, and field management.
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