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Insects
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Breakthrough Technologies for Future Entomology
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Breakthrough Technologies for Future Entomology

A special issue of Insects (ISSN 2075-4450).

Deadline for manuscript submissions: 15 November 2024 | Viewed by 9228

Special Issue Editors

Dr. Donato Romano

E-Mail Website
Guest Editor
The BioRobotics Institute, Scuola Superiore Sant'Anna, 33, 56127 Pisa, Italy
Interests: applied biology; biorobotics; biohybrid systems; neuroethology; ethorobotics; zoology
Special Issues, Collections and Topics in MDPI journals
Dr. Lloyd T. (Ted) Wilson

E-Mail Website
Guest Editor
1. Texas A&M AgriLife Research, Rice Research Center, Beaumont, TX, USA
2. Department of Entomology, Texas A&M University, College Station, TX, USA
Interests: agricultural ecosystems management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements in different breakthrough technologies (e.g., robotics, bioengineering, biotechnology, AI, and IoT) are broadening the horizons of applied entomology, changing the paradigms for the management and mass rearing of insect species of socio-economic interest.

Precision and automation technologies are significantly increasing our understanding of insect biology and ecology, and are also providing novel approaches for modelling, monitoring, and managing animal populations in agroecosystems, progressing sustainable crop protection based on biocontrol strategies and IPM programs.

This Special Issue welcomes entomology-oriented theoretical, experimental, and real-world application studies including, but not limited to, the following topics:

  • Agricultural robotics;
  • Agtech;
  • Animal–robot interactions;
  • Artificial neural networks;
  • Biohybrid systems;
  • Biotechnologies;
  • Drone and satellite technology;
  • Field robotics;
  • Information and communications technology;
  • Internet of Things;
  • Machine learning;
  • Soft robotics;
  • Super-resolution imaging;
  • Wireless sensor networks.

Dr. Donato Romano
Dr. Lloyd T. (Ted) Wilson
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Insects is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

14 pages, 501 KiB  
Article
Application of Machine Learning Techniques to Discern Optimal Rearing Conditions for Improved Black Soldier Fly Farming
by John Muinde, Chrysantus M. Tanga, John Olukuru, Clifford Odhiambo, Henri E. Z. Tonnang and Kennedy Senagi
Insects 2023, 14(5), 479; https://doi.org/10.3390/insects14050479 - 19 May 2023
Viewed by 2594
Abstract
As the world population continues to grow, there is a need to come up with alternative sources of feed and food to combat the existing challenge of food insecurity across the globe. The use of insects, particularly the black soldier fly (BSF) Hermetia [...] Read more.
As the world population continues to grow, there is a need to come up with alternative sources of feed and food to combat the existing challenge of food insecurity across the globe. The use of insects, particularly the black soldier fly (BSF) Hermetia illucens (L.) (Diptera: Stratiomydiae), as a source of feed stands out due to its sustainability and reliability. Black soldier fly larvae (BSFL) have the ability to convert organic substrates to high-quality biomass rich in protein for animal feed. They can also produce biodiesel and bioplastic and have high biotechnological and medical potential. However, current BSFL production is low to meet the industry’s needs. This study used machine learning modeling approaches to discern optimal rearing conditions for improved BSF farming. The input variables studied include the cycle time in each rearing phase (i.e., the rearing period in each phase), feed formulation type, length of the beds (i.e, rearing platforms) at each phase, amount of young larvae added in the first phase, purity score (i.e, percentage of BSFL after separating from the substrate), feed depth, and the feeding rate. The output/target variable was the mass of wet larvae harvested (kg per meter) at the end of the rearing cycle. This data was trained on supervised machine learning algorithms. From the trained models, the random forest regressor presented the best root mean squared error (RMSE) of 2.91 and an R-squared value of 80.9%, implying that the model can be used to effectively monitor and predict the expected weight of BSFL to be harvested at the end of the rearing process. The results established that the top five ranked important features that inform optimal production are the length of the beds, feed formulation used, the average number of young larvae loaded in each bed, feed depth, and cycle time. Therefore, in that priority, it is expected that tuning the mentioned parameters to fall within the required levels would result in an increased mass of BSFL harvest. These data science and machine learning techniques can be adopted to understand rearing conditions and optimize the production/farming of BSF as a source of feed for animals e.g., fish, pigs, poultry, etc. A high production of these animals guarantees more food for humans, thus reducing food insecurity. Full article
(This article belongs to the Special Issue Breakthrough Technologies for Future Entomology)
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23 pages, 3492 KiB  
Article
Host-Specific Diversity of Culturable Bacteria in the Gut Systems of Fungus-Growing Termites and Their Potential Functions towards Lignocellulose Bioconversion
by Rongrong Xie, Chenchen Dong, Shengjie Wang, Blessing Danso, Mudasir A. Dar, Radhakrishna S. Pandit, Kiran D. Pawar, Alei Geng, Daochen Zhu, Xia Li, Qing Xu and Jianzhong Sun
Insects 2023, 14(4), 403; https://doi.org/10.3390/insects14040403 - 21 Apr 2023
Cited by 11 | Viewed by 1882
Abstract
Fungus-growing termites are eusocial insects that represent one of the most efficient and unique systems for lignocellulose bioconversion, evolved from a sophisticated symbiosis with lignocellulolytic fungi and gut bacterial communities. Despite a plethora of information generated during the last century, some essential information [...] Read more.
Fungus-growing termites are eusocial insects that represent one of the most efficient and unique systems for lignocellulose bioconversion, evolved from a sophisticated symbiosis with lignocellulolytic fungi and gut bacterial communities. Despite a plethora of information generated during the last century, some essential information on gut bacterial profiles and their unique contributions to wood digestion in some fungus-growing termites is still inadequate. Hence, using the culture-dependent approach, the present study aims to assess and compare the diversity of lignocellulose-degrading bacterial symbionts within the gut systems of three fungus-growing termites: Ancistrotermes pakistanicus, Odontotermes longignathus, and Macrotermes sp. A total of 32 bacterial species, belonging to 18 genera and 10 different families, were successfully isolated and identified from three fungus-growing termites using Avicel or xylan as the sole source of carbon. Enterobacteriaceae was the most dominant family represented by 68.1% of the total bacteria, followed by Yersiniaceae (10.6%) and Moraxellaceae (9%). Interestingly, five bacterial genera such as Enterobacter, Citrobacter, Acinetobacter, Trabulsiella, and Kluyvera were common among the tested termites, while the other bacteria demonstrated a termite-specific distribution. Further, the lignocellulolytic potential of selected bacterial strains was tested on agricultural waste to evaluate their capability for lignocellulose bioconversion. The highest substrate degradation was achieved with E. chengduensis MA11 which degraded 45.52% of rice straw. All of the potential strains showed endoglucanase, exoglucanase, and xylanase activities depicting a symbiotic role towards the lignocellulose digestion within the termite gut. The above results indicated that fungus-growing termites harbor a diverse array of bacterial symbionts that differ from species to species, which may play an inevitable role to enhance the degradation efficacy in lignocellulose decomposition. The present study further elaborates our knowledge about the termite-bacteria symbiosis for lignocellulose bioconversion which could be helpful to design a future biorefinery. Full article
(This article belongs to the Special Issue Breakthrough Technologies for Future Entomology)
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14 pages, 6847 KiB  
Article
A Deep-Learning-Based Detection Approach for the Identification of Insect Species of Economic Importance
by Michael Tannous, Cesare Stefanini and Donato Romano
Insects 2023, 14(2), 148; https://doi.org/10.3390/insects14020148 - 31 Jan 2023
Cited by 8 | Viewed by 3434
Abstract
Artificial Intelligence (AI) and automation are fostering more sustainable and effective solutions for a wide spectrum of agricultural problems. Pest management is a major challenge for crop production that can benefit from machine learning techniques to detect and monitor specific pests and diseases. [...] Read more.
Artificial Intelligence (AI) and automation are fostering more sustainable and effective solutions for a wide spectrum of agricultural problems. Pest management is a major challenge for crop production that can benefit from machine learning techniques to detect and monitor specific pests and diseases. Traditional monitoring is labor intensive, time demanding, and expensive, while machine learning paradigms may support cost-effective crop protection decisions. However, previous studies mainly relied on morphological images of stationary or immobilized animals. Other features related to living animals behaving in the environment (e.g., walking trajectories, different postures, etc.) have been overlooked so far. In this study, we developed a detection method based on convolutional neural network (CNN) that can accurately classify in real-time two tephritid species (Ceratitis capitata and Bactrocera oleae) free to move and change their posture. Results showed a successful automatic detection (i.e., precision rate about 93%) in real-time of C. capitata and B. oleae adults using a camera sensor at a fixed height. In addition, the similar shape and movement patterns of the two insects did not interfere with the network precision. The proposed method can be extended to other pest species, needing minimal data pre-processing and similar architecture. Full article
(This article belongs to the Special Issue Breakthrough Technologies for Future Entomology)
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