Research opportunities on the coconut (Cocos nucifera L.) using new technologies

Ignacio, Islas-Flores; Tzec-Simá, Miguel

doi:10.1016/j.sajb.2021.05.030

Cited by 22 publications

(18 citation statements)

References 44 publications

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“…This agricultural waste product will be present for as long as the demand for coconuts and coconut products exist. In 2020 alone it was estimated that the global coconut production was 62.5 million tons, with demand set to increase in the coming years [27,28]. The use of biomas waste product for activated carbon sources will not just lower the cost of material, but simultaneously will reduce the cost of dealing with an existing waste product.…”

Section: Introductionmentioning

confidence: 99%

Shape-stabilized phase change materials: Performance of simple physical blending synthesis and the potential of coconut based materials

Muchtar¹,

Hassam²,

Srinivasan³

et al. 2022

Journal of Energy Storage

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Section: Introductionmentioning

confidence: 99%

Shape-stabilized phase change materials: Performance of simple physical blending synthesis and the potential of coconut based materials

Muchtar¹,

Hassam²,

Srinivasan³

et al. 2022

Journal of Energy Storage

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“…16,17 By 2028, it is estimated that the global coconut industry would be worth USD 101.9 billion. [18][19][20][21][22] The rapid increase in global demand for coconut products has resulted in copious amounts of coco coir as an industrial byproduct with no concrete plans to utilize or recycle them effectively. Owing to the ineffective waste management policies, a substantial amount of unprocessed coco coir discarded by the coconut-based agriculture industry is released as industrial waste.…”

Section: Introductionmentioning

confidence: 99%

Eco-friendly fabrication of coco coir composites for hydroponic cultivation: a green chemistry approach

2023

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“…Coconut (Cocos nucifera) is one of the main cash crops grown throughout the year, predominantly along the coastal regions of Asia, South America, and the Pacific Islands. Coconut cultivation globally accounts for about 11.8 million hectares of agricultural land across 93 countries and nearly 63 million tonnes per year [ 1 ]. Among the Asian countries that contribute to almost 75% of the global production (52 million tonnes), Indonesia (17.1 million tonnes), Philippines (14.8 million tonnes), and India (14.7 million tonnes) account for more than 90% of the coconut production [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…Coconut products have become an indispensable part of our day-to-day lives and for numerous industries in many parts of the world due to their high nutritional value and wide range of food and industrial products [ 1 ]. However, farmers are often trapped in financial losses despite increased demand for coconut products due to costly human labour and arduous work in many stages of its production.…”

Section: Introductionmentioning

confidence: 99%

Detection of Coconut Clusters Based on Occlusion Condition Using Attention-Guided Faster R-CNN for Robotic Harvesting

Divyanth

Soni

Pareek

et al. 2022

Foods

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Manual harvesting of coconuts is a highly risky and skill-demanding operation, and the population of people involved in coconut tree climbing has been steadily decreasing. Hence, with the evolution of tree-climbing robots and robotic end-effectors, the development of autonomous coconut harvesters with the help of machine vision technologies is of great interest to farmers. However, coconuts are very hard and experience high occlusions on the tree. Hence, accurate detection of coconut clusters based on their occlusion condition is necessary to plan the motion of the robotic end-effector. This study proposes a deep learning-based object detection Faster Regional-Convolutional Neural Network (Faster R-CNN) model to detect coconut clusters as non-occluded and leaf-occluded bunches. To improve identification accuracy, an attention mechanism was introduced into the Faster R-CNN model. The image dataset was acquired from a commercial coconut plantation during daylight under natural lighting conditions using a handheld digital single-lens reflex camera. The proposed model was trained, validated, and tested on 900 manually acquired and augmented images of tree crowns under different illumination conditions, backgrounds, and coconut varieties. On the test dataset, the overall mean average precision (mAP) and weighted mean intersection over union (wmIoU) attained by the model were 0.886 and 0.827, respectively, with average precision for detecting non-occluded and leaf-occluded coconut clusters as 0.912 and 0.883, respectively. The encouraging results provide the base to develop a complete vision system to determine the harvesting strategy and locate the cutting position on the coconut cluster.

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Research opportunities on the coconut (Cocos nucifera L.) using new technologies

Cited by 22 publications

References 44 publications

Shape-stabilized phase change materials: Performance of simple physical blending synthesis and the potential of coconut based materials

Shape-stabilized phase change materials: Performance of simple physical blending synthesis and the potential of coconut based materials

Eco-friendly fabrication of coco coir composites for hydroponic cultivation: a green chemistry approach

Detection of Coconut Clusters Based on Occlusion Condition Using Attention-Guided Faster R-CNN for Robotic Harvesting

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