Tadiparthi et al. (2027) reviewed 29 studies using supervised learning to detect or identify grain infesting insects to species. Two of these studies counted insects (Qi et al. 2025, Zhu et al. 2025). Li et al. (2024a) was able use deep learning to accurately count Lasioderma serricorne. Sitophilus oryzae was included in 20 of 29 studies (Tadiparthi et al. 2027), Oryzaephilus surinamensis and Rhyzopertha dominica each in 19, Tribolium castaneum in 18, Cryptolestes ferrugineus in 14, Sitophilus zeamais in 13, Lasioderma serricorne in 12, Cryptolestes pusillus in 9, Tribolium confusum in 8, Stegobium paniceum and Sitophilus granarius each in 5, Cryptolestes turcicus, Gnathocerus cornutus, Oryzaephilus mercator and Tribolium madens each in 4, Plodia interpunctella and Trogoderma variabile each in 3 and Ahasverus advena, Callosobruchus maculatus, Cynaeus angustus, Sitotroga cerealella, Tribolium brevicornis, Tribolium freeman and Zabrotes subfasciatus each in 2. Another 11 species were included in only one study (Araecerus fasciculatus (<80%), Attagenus unicolor (<80%), Cathartus quadricollis (<80%), Ephestia elutella (92.4%), Gibbium aequinoctiale (97.3%), Latheticus oryzae (>80%), Lophocateres pusillus (>80%), Palorus ratzeburgii (>80%), Tribolium destructor >80%), Trogoderma granarium (>90%) and Trogoderma inclusum (>80%).
Tuda and Luna-Maldonado (2020) found that image analysis can determine both species and sex of Callosobruchus chinensis and two of its parasitoids, Anisopteromalus calandrae and Heterospilus prosopidis. Data have been collected for 38 stored-product insect species. Park et al. (2016) elytra images were reused by Bisgin et al. 2018 and Wu et al. 2019 and Li et al. 2024b images were reused by Tian et al. (2025) and Zhu et al. (2025). Only 4 of 29 studies released public datasets (*Bisgin et al., (2022), Li et al. (2019, 2024) Park et al., 2016) and only three demonstrated edge (**Chen et al. 2022, Mendoza et al. 2023) or mobile deployment (**Badgujar et al. 2023b). Chen et al. 2022 used a mobile robot to locate insects. Machine learning identified species and geographical strains of Sitophilus oryzae and Sitophilus zeamais (Cao et al. 2015) and has also used the sounds of five insect species moving and feed to identify species (Balingbing et al. 2024, Banga et al. 2020).
In addition to monitoring, artificial intelligence and machine learning have been used to forecast insect problems (Balingbing et al. 2024, Banga et al. 2020, Nyabako et al. 2020, Ogungbite and Ogungbite 2026, Siaho et al. 2023, Wu et al. 2016) and improve pest management with aeration (Junior et al, 2024, Luo et al. 2026, Zhang et al. 2026), heat disinfestation (Abdelsamea et al. 2023, Adler et al. 2026, Rossos et al. 2025), botanical insecticides (Casadei et al. 2026, Erdogan et al. 2026). Machine learning has been used to predict phototaxis of almond moth, Cadra cautella (Guru et al. 2025) and the life history of Callosobruchus chinensis (Gu and Tuda 2026). Coupled with the Internet of Things remote monitoring is possible, because equipment for this is commercially available (Aivision 2025, Anonymous 2018, Direct-to-satellite IoT sensors 2026, HARDWARIO and GrainLink 2026, TeleSense 2026). Other reviews include Abdullah (2026), Adeyeye (2027), Anukiruthika and Jayas. (2025), Devi and Singhrova (2026).
References (For the 29 studies reviewed by Tadiparthi et al. (2027), the number of species studied by each are given in front of author names)
Abdelsamea, Mohammed M., Mohamed Medhat Gaber, Aliyuda Ali, Marios Kyriakou, and Shams Fawki 2023. A Logarithmically Amortising Temperature Effect for Supervised Learning of Wheat Solar Disinfestation of Rice Weevil Sitophilus oryzae (Coleoptera: Curculionidae) Using Plastic Bags. Scientific Reports 13(1), 2655. Predicts whether a certain combination of parameters will be effective in the treatment of insects using thermal control. Random forest model achieved 99.5% accuracy in predicting lethal temperatures for pest control.
Abdullah, Hafiz Muhammad, Farhan Saeed, Muhammad Bilal Hussain, Ali Raza, Amar Shankar, Atreyi Pramanik, Gunjan Garg et al. 2026. Use of Artificial Intelligence in post-harvest losses management: a current insight. European Food Research and Technology 252(8): 305.
Adeyeye, Samuel Ayofemi Olalekan 2027. From reactive to predictive stored-product protection: AI, digital twins, biosensors, and multi-omics for smart grain storage J. Stored Prod. Res. 120, 103220.
Adler, Cornel, Gunnar Böttger, Kirko Große, Christian Hentschel, Dirk Höpfner, and Peter Kern. 2026. Artificial intelligence for stored product insect detection and control with laser beams: the Insect Laser project. Journal of Plant Diseases and Protection 133(3): 85.
2 Agarwal, M., Al-Shuwaili, T., Nugaliyadde, A., Wang, P., Wong, K.W., Ren, Y. 2020. Identification and diagnosis of whole body and fragments of Trogoderma granarium and Trogoderma variabile using visible near infrared hyperspectral imaging technique coupled with deep learning. Comput. Electron. Agric. 173, 105438. https://doi.org/ 10.1016/j.compag.2020.105438 Both >90%. (Bold only in this study).
Aivision 2025. Wireless smartprobe systems for smart, data-driven IPM practices. www. aivisionfood.comon
Anonymous 2018. Internet of Things Revolutionizes Farming and Grain Storage. Feb 20, 2018 Advanced grain monitoring systems like those available from Tri-States Grain Conditioning rely on the IoT. https://tsgcinc.com/internet-of-things-grain-temperature-monitoring/
Anukiruthika, T., and D. S. Jayas 2025. AI-Driven Grain Storage Solutions: Exploring Current Technologies, Applications, and Future Trends. J. Stored Prod. Res. 111, 102588.
5 Badgujar, C.M., Armstrong, P.R., Gerken, A.R., Pordesimo, L.O., Campbell, J.F. 2023a. Identifying common stored product insects using automated deep learning methods. J. Stored Prod. Res. 103, 102166. https://doi.org/10.1016/j.jspr.2023.102166. Cryptolestes ferrugineus 100% = Rhyzopertha dominica 100% = Tribolium castaneum 100% > Sitophilus oryzae 99.9% > Oryzaephilus surinamensis 99.3%
6** Badgujar, C.M., Armstrong, P.R., Gerken, A.R., Pordesimo, L.O., Campbell, J.F., 2023b. Real-time stored product insect detection and identification using deep learning: system integration and extensibility to mobile platforms. J. Stored Prod. Res. 104, 102196. https://doi.org/10.1016/j.jspr.2023.102196 Lasioderma serricorne, Oryzaephilus surinamensis, Sitophilus oryzae, Stegobium paniceum, Tribolium castaneum, Trogoderma variabile. Model often confused and misclassified Lasioderma serricorne and Stegobium paniceum due to their similar appearance. Tribolium castaneum were misidentified due to body having similar shaped to wheat kernels. Accuracy was similar for other species.
Balingbing, C.B.; Kirchner, S.; Siebald, H.; Kaufmann, H.H.; Gummert, M.; Van Hung, N. and Hensel, O. 2024. Application of a multi-layer convolutional neural network model to classify major insect pests in stored rice detected by an acoustic device. Comput. Electron. Agric. 225, 109297. Tribolium castaneum 86.6% Rhyzopertha dominica 85.2%, Sitophilus oryzae 81.8%.
Banga, K.S., Mohapatra, D., Babu, V.B., Giri, S.K., and Bargale, P.C. 2020. Assessment of bruchids density through bioacoustic detection and artificial neural network (ANN) in bulk stored chickpea and green gram. Journal of Stored Products Research, 88: 101667. https://doi.org/10.1016/j.jspr.2020.101667 Artificial neural network (ANN) was applied to predict Callosobruchus chinensis and Callosobruchus maculatus density in bulk stored green gram and chickpea with R2 of 0.99, 0.98 and 0.90, 0.89, respectively.
1 Barboza da Silva, C., Silva, A.A.N., Barroso, G., Yamamoto, P.T., Arthur, V., Toledo, C.F. M., and Mastrangelo, T. de A. 2021. Convolutional neural networks using enhanced radiographs for real-time detection of Sitophilus zeamais in maize grain. Foods 10 (4), 879. MobileNetV2 had the best performance (0.91) on the validation dataset and Xception had best performance on test database (1.00).
27* Bisgin, H., Bera, T., Wu, L., Ding, H., Bisgin, N., Liu, Z., Pava-Ripoll, M., Barnes, A., Campbell, J.F., Vyas, H., Furlanello, C., Tong, W., Xu, J. 2022. Accurate species identification of food-contaminating beetles with quality-improved elytral images and deep learning. Front. Artif. Intell. 5, 952424. https://doi.org/10.3389/ frai.2022.952424. (Callosobruchus maculatus, Cryptolestes pusillus, Cryptolestes turcicus, Gnathocerus cornutus, Lasioderma serricorne, Latheticus oryzae, Lophocateres pusillus, Oryzaephilus mercator, Oryzaephilus surinamensis, Palorus ratzeburgii, Rhyzopertha dominica, Sitophilus granarius, Sitophilus zeamais, Stegobium paniceum, Tribolium castaneum, Tribolium confusum, Tribolium destructor, Tribolium madens, Trogoderma inclusum >80%), (Ahasverus advena, Araecerus fasciculatus, Attagenus unicolor, Cathartus quadricollis, Cryptolestes ferrugineus, Cynaeus angustus, Sitophilus oryzae, <80%) (Bold only in this study).
15 Bisgin, H., Bera, T., Ding, H., Semey, H.G., Wu, L., Liu, Z., Barnes, A.E., Langley, D.A., Pava-Ripoll, M., Vyas, H.J., Tong, W., Xu, J. 2018. Comparing SVM and ANN based machine learning methods for species identification of food contaminating beetles. Sci. Rep. 8, 6532. https://doi.org/10.1038/s41598-018-24926-7. (Ahasverus advena, Cryptolestes pusillus, Gnathocerus cornutus, Lasioderma serricorne, Rhyzopertha dominica, Sitophilus oryzae, Tribolium castaneum, Tribolium madens >80%), (Oryzaephilus mercator, Oryzaephilus surinamensis, Sitophilus granarius, Stegobium paniceum, Tribolium confusum <80%).
1 Boniecki, P., Piekarska-Boniecka, H., ´Swierczynski, K., Koszela, K., Zaborowicz, M., Przybył, J. 2014. Detection of the granary weevil based on X-ray images of damaged wheat kernels. J. Stored Prod. Res. 56, 38–42. https://doi.org/10.1016/j. jspr.2013.11.001 Sitophilua granarius The proposed model identified 100% of the infested kernels correctly, and 98.4% of the healthy ones.
Cao Y, Zhang CJ, Chen QS, Li YY, Qi S, Tian L et al. 2015. Identification of species and geographical strains of Sitophilus oryzae and Sitophilus zeamais using the visible/near-infrared hyperspectral imaging technique. Pest Manag Sci 71:1113–1121 Backpropagation neural network (BPNN) technique classified two species with accuracy of 98%, and distinguish each geographical strain at an accuracy greater than 77.78%.
Casadei, Anita, Maria C. Boukouvala, Gianluca Manduca, Nickolas G. Kavallieratos, Filippo Maggi, Marta Ferrati, Eleonora Spinozzi, Cesare Stefanini, Antonio DeSimone, and Donato Romano 2026. Using deep learning to assess the toxicological effects of sublethal exposure of a novel green pesticide in a stored‐product beetle. Pest Management Science 82(5): 4319-4331.
2** Chen, C., Liang, Y., Zhou, L., Tang, X., and Dai, M. 2022. An automatic inspection system for pest detection in granaries using YOLOv4. Comput. Electron. Agric. 201, 107302. https://doi.org/10.1016/j.compag.2022.107302. Sitophilus oryzae 100% > Tribolium castaneum 95%.
7 Chu, J., Li, Y., Feng, H., Weng, X., Ruan, Y. 2023. Research on multi-scale pest detection and identification method in granary based on improved YOLOv5. Agriculture 13 (2), 364. Bruchidae 99.4% > Sitophilus oryzae 99.3% > Rhyzopertha dominica 99.0% > Plodia interpunctella 98.4% >Tribolium castaneum 98.0% > Cryptolestes ferrugineus 97.3% > Oryzaephilus surinamensis 95.4%.
Devi, Jyoti, and Anita Singhrova 2026. A Bibliometric Study of IoT and Artificial Intelligence for Grain Storage Management. In 2026 Eight International Conference on Computational Intelligence and Communication Technologies (CCICT), pp. 483-490. BM Institute of Engineering & Technology. 211 papers show significant progress in research in this field since 2019 in India, China and the United States.
1 Divyanth, L.G., Chelladurai, V., Loganathan, M., Jayas, D.S., and Soni, P. 2022. Identification of green gram (Vigna radiata) grains infested by Callosobruchus maculatus through X-ray imaging and GAN-based image augmentation. Journal of Biosystems Engineering/ J. Biosyst. Eng. 47 (3), 302–317. https://doi.org/10.1007/s42853-022-00147-9 The overall F1-score produced by the model was improved from 0.86 to 0.91 when the GAN-synthesized dataset additionally supported the training data. Also, the classification accuracy for detecting the stage of internal infestation improved by 5.5%.
Direct-to-satellite IoT sensors 2026. https://lacuna-space.com/remote-grain-monitoring/. Operators receive early alerts on temperature, humidity, and CO₂ changes to stop spoilage before it destroys commercial value.
Erdogan, Pervin, Muhammad Aasim, Zemran Mustafa, Amjad Ali, … and Abdul Waheed 2026. Integrated optimization of Lantana camara-based biopesticide for Sitophilus granarius control: Chemical profiling, residual efficacy, and machine-learning-driven decision support. J. Stored Prod. Res. 119, 103190
Gu, Xiangpeng, and Midori Tuda. 2026. Predicting life-history traits in a stored bean pest beetle Callosobruchus chinensis (Coleoptera: Chrysomelidae: Bruchinae) using machine learning. bioRxiv 2026-03
Guru, P. N., Dhritiman Saha, Yogesh B. Kalnar, Monika Sharma, Ruchika Zalpouri, Virinder Kumar, and Nivedita Shettigar 2025. Predicting phototaxis of almond moth, Cadra cautella (Walker) using ANN models: insights for wavelength and intensity as key factors. Pest Management Science 81(8): 4264-4274. DOI: 10.1002/ps.8787
HARDWARIO and GrainLink 2026. Revolutionizing Grain Storage with IoT Technology https://www.hardwario.com/cases/hardwario-and-grainlink/. Over 1,000 monitoring systems deployed across Canada and the USA for smart grain-silo monitoring in demanding climatic conditions.
5 Juan, R.O. Serfa and Gerken, A.R. 2025. Green AI solutions for automated insect monitoring to improve stored product management systems. 2025 IEEE Green Technologies Conference (GreenTech), pp. 143–147. https://doi.org/10.1109/ GreenTech62170.2025.10977676. Tribolium castaneum 99.0% > Oryzaephilus surinamensis 98.8% > Rhyzopertha dominica 98.6% > Sitophilus zeamais 98.5% > Cryptolestes ferrugineus 98.4%
Junior, W.N.F., Resende, O., de Oliveira, D.C., de Oliveira, D.E.C., and Rosa, E. dos S. 2024. Estimating energy efficiency of the aeration process of stored grains through machine learning. Brazilian J. Agric. Environ. Eng. 28, e281001.
Li, Boyang, Li Liu, Haijiang Jia, Zhaoyang Zang, …, and Jiaqin Xi. 2024a. YOLO-TP: A Lightweight Model for Individual Counting of Lasioderma Serricorne. J. Stored Prod. Res. 109, 102456. High accuracy rate of 99.5%.
6* Li, Dandan, Jida Tian, Jiangtao Li, Muyi Sun, Huiling Zhou, and Yili Zheng 2024b. Establishment of a dataset for detecting pests on the surface of grain bulks. Applied Engineering in Agriculture 40(3): 363-376. Cryptolestes ferrugineus 97.0% = (Sitophilus oryzae, Sitophilus zeamais 97.0%) > Rhizopertha dominica 91.0% > Oryzaephilus surinamensis 88.0% > Tribolium castaneum 85.0%.
3 Li, Jiangtao, Su, Yuwei, Cui, Zhaojun, Tian, Jida, and Zhou, Huiling 2022. A method to establish a synthetic image dataset of stored-product insects for insect detection. IEEE Access 10, 70269–70278. https://doi.org/10.1109/ACCESS.2022.3188282. Tribolium castaneum 93.3% > Sitophilus oryzae 87.9% > Cyptolestes ferrugineus 76.6%.
10 Li, Jiangtao, Zhou, H., Wang, Z., and Jia, Q. 2020. Multi-scale detection of stored-grain insects for intelligent monitoring. Comput. Electron. Agric. 168, 105114. https://doi.org/ 10.1016/j.compag.2019.105114 Lasioderma serricorne 97.0% > (Tribolium castaneum, Tribolium confusum 95.7%) > Oryzaephilus surinamensis 95.1% > (Sitophilus oryzae, Sitophilus zeamais 95.3%) > (Cryptolestes ferrugineus, Cryptolestes pusillus, Cryptolestes turcicus 93.3%) > Rhyzopertha dominica 92.6%.
10* Li, Zhou, H., Jayas, D.S., Jia, Q. 2019. Construction of a dataset of stored-grain insects images for intelligent monitoring. Applied Engineering in Agriculture/ Appl. Eng. Agric. 35(4): 647–655. (Sitophilus oryzae, Sitophilus zeamais 96%) > Lasioderma serricorne 94% > (Cryptolestes ferrugineus, Cryptolestes pusillus, Cryptolestes turcicus 92%) = (Tribolium castaneum, Tribolium confusum 92%) > Rhizopertha dominica 90% > Oryzaephilus surinamensis 89%.
Luo, Yuanyi, Dandan Li, Jinying Chen, Yanguang Zhu, Yiming Ma, Jie Lin, Kun Hu et al. 2026. AI-driven technologies for pest monitoring, unsound kernel detection, and intelligent aeration in grain storage. International Journal of Agricultural and Biological Engineering 19(1): 1-10.
2 Liu, Zhang, G., Yang, H., Sun, M., Dang, H., Zhou, X., 2018. Application of object detection algorithm in identification of rice weevils and maize weevils. Proceedings of the 2018 2nd International Conference on Deep Learning Technologies, ICDLT ’18, pp. 76–80. https://doi.org/10.1145/3234804.3234820 Sitophilus oryzae 85.6%, Sitophilus zeamais 85.8%
Luo, Yuanyi, Dandan Li, Jinying Chen, Yanguang Zhu, Yiming Ma, Jie Lin, Kun Hu et al. 2026. AI-driven technologies for pest monitoring, unsound kernel detection, and intelligent aeration in grain storage. International Journal of Agricultural and Biological Engineering 19(1): 1-10.
5 Lyu, Z., Jin, H., Zhen, T., Sun, F., Xu, H., 2021a. Small object recognition algorithm of grain pests based on SSD feature fusion. IEEE Access 9, 43202–43213. https://doi. org/10.1109/ACCESS.2021.3066510. Rhyzopertha dominica 95.2% > Tribolium castaneum 94.5% > Cryptolestes ferrugineus 94.0%, Plodia interpunctella 93.0%
2** Mendoza, Q.A., Pordesimo, L., Neilsen, M., Armstrong, P., Campbell, J., Mendoza, P.T. 2023. Application of machine learning for insect monitoring in grain facilities. AI 4(1): 348–360. https://doi.org/10.3390/ai4010017. Trogoderma variabile 91.4% > Lasioderma serricorne 90.6%.
Nyabako, Tinashe, Brighton M. Mvumi, Tanya Stathers, Shaw Mlambo, and Macdonald Mubayiwa 2020. Predicting Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) Populations and Associated Grain Damage in Smallholder Farmers’ Maize Stores: A Machine Learning Approach. J. Stored Prod. Res. 87, 101592. Using decision-tree algorithms and weather-correlated data, the grain damage prediction model achieved a strong correlation of 0.93.
Ogungbite, Olaniyi Charles, and Alaba Bukola Ogungbite 2026. Artificial intelligence in stored-product pest monitoring and decision support: Implications for reduced-risk and botanical control. Journal of Stored Products Research 118, 103074. Artificial Intelligence can strengthe stored-product pest monitoring and management by linking sensing, classification, forecasting, and decision support. AI may improve infestation-risk prediction, spatial targeting, and the timing of reduced-risk interventions. AI close to closing the longstanding gap between monitoring outputs and useable management decisions.
15* Park, S.I., Bisgin, H., Ding, H., Semey, H.G., Langley, D.A., Tong, W., Xu, J. 2016. Species identification of food contaminating beetles by recognizing patterns in microscopic images of elytra fragments. PLoS One 11 (6), e0157940. https://doi. org/10.1371/journal.pone.0157940. (Cryptolestes pusillus, Gnathocerus cornutus, Oryzaephilus mercator, Rhyzopertha dominica, Sitophilus granarius, Tribolium brevicornis, Tribolium castaneum, Tribolium madens, Zabrotes subfasciatus >80%), (Lasioderma serricorne, Oryzaephilus surinamensis, Sitophilus oryzae, Stegobium paniceum, Tribolium confusum, Tribolium freemani <80%).
4 Qi, R., Li, R., Zhang, J., Xia, Y., Du, J., Sun, J., chen, L., Xie, C., Zhang, H., Li, G., 2025. A density-point network for dense tiny stored grain pest counting. J. Stored Prod. Res. 111, 102536. https://doi.org/10.1016/j.jspr.2024.102536 Cryptolestes ferrugineus, Liposcelididae, Oryzaephilus surinamensis, Rhyzopertha dominica Our method consistently outperforms others, achieving the lowest MAE and MSE scores across four pest species captured using traps. All methods show high error metrics due to the translucent and small size of booklice. Point-based (PB) methods demonstrate relatively good performance by relying on single-point annotations for individual insects, effectively mitigating the issue of overlapping insects.
Rossos, Stavros, Paraskevi Agrafioti, Vasilis Sotiroudas, Christos G. Athanassiou, and Efstathios Kaloudis 2025. Predicting Heat Treatment Duration for Pest Control Using Machine Learning on a Large-Scale Dataset. Agronomy 15(5), 1254. Highlight the importance of tailored heat treatment protocols and the potential of data-driven approaches to optimize pest control strategies, reduce energy consumption, and improve operational efficiency in industrial settings.
Siaho, Diomande, Pandry Koffi Ghislain, Kadjo Tanon Lambert, Kakou Kouassi Ernest, Oumtanaga Souleymane, and Assidjo Nogbou Emmanuel 2023. Modeling Artificial Neural Network of Insect’s Proliferation During Cocoa Beans Storage. Ingenierie des Systemes d’Information 28(2): 291. Tribolium castaneum, Ephestia cautella, Araecerus faciculatus, Lasioderma serricorne, R²= 0.9982. shows a good correlation of the experimental values and those predicted.
6 Shen, Y., Zhou, H., Li, J., Jian, F., Jayas, D.S., 2018. Detection of stored-grain insects using deep learning. Computers and Electronics in Agriculture/Comput. Electron. Agric. 145, 319–325. https://doi.org/ 10.1016/j.compag.2017.11.039. Tribolium confusum 96.9% > Sitophilus oryzae 95.3% > Rhyzopertha dominica 93.8% > Cryptolestes pusillus 86.2% > Oryzaephilus surinamensis 79.9% >Lasioderma serricorne 72.9%.
8 Shi, Z., Dang, H., Liu, Z., Zhou, X. 2020. Detection and identification of stored-grain insects using deep learning: a more effective neural network. IEEE Access 8, 163703–163714 https://doi.org/10.1109/ACCESS.2020.3021830 Rhyzopertha dominica 93.71% > Lasioderma serricorne 91.05% > Oryzaephilus surinamensis 89.32% > Tribolium confusum 88.94% > Cryptolestes ferrugineus 87.25% > Sitophilus oryzae 86.36% > Sitophilus zeamais 84.63% Cryptolestes turcicus 83.24%. For Sitophilus, average accuracy was 6% lower in simulated grain storage compared to laboratory.
TeleSense 2026, a California-based company, recently introduced its new monitoring system the TeleSense GrainSafe™. For storage units that do not have existing temperature sensing cables, TeleSense developed a portable solution: the SensorBall™. A number of SensorBalls can be tossed into a grain pile or a horizontal storage (or a truck or a railcar) to collect the data wirelessly. https://millingandgrain.com/wireless-grain-monitoring-using-iot-technology-19532/
6 Tian, J., Sun, M., Zhou, H., Li, J. 2025. PestDet: a unified detection framework for accurate and efficient stored-grain pest detection. Ecol. Inform. 88, 103145. https:// doi.org/10.1016/j.ecoinf.2025.103145. (Sitophilus oryzae, Sitophilus zeamais 96.3%) > Rhizopertha dominica 95.9% > Oryzaephilus surinamensis 93.5% > Cryptolestes ferrugineus 93.3% > Tribolium castaneum 91.3%. Our code and data are available at (https://github.com/IntelligentsystemlabTian/PestDet).
Tuda, M., and Luna-Maldonado, A.I. 2020. Image-based insect species and gender classification by trained supervised machine learning algorithms. Ecol. Inform. 60, 101135. Callosobruchus chinensis, Anisopteromalus calandrae, Heterospilus prosopidis, Average true positive rates (prediction accuracy) of 88.5–98.5% were achieved for within-species sexing of beetles or wasps. For three-species classification and no sexing, the average TP rate reached 99.67–100% by all algorithms.
Wu, Jian-Jun, Hao Dang, Miao Li, et al. 2016. The Prediction Research of Population Density Based on Deep Learning in Grain Stored Insects. International Journal of Hybrid Information Technology 9(10): 251–58. Liposcelis entomophila
15 Wu, Leihong, Liu, Z., Bera, T., Ding, H., Langley, D.A., Jenkins-Barnes, A., Furlanello, C., Maggio, V., Tong, W., Xu, J., 2019. A deep learning model to recognize food contaminating beetle species based on elytra fragments. Comput. Electron. Agric. 166, 105002. https://doi.org/10.1016/j.compag.2019.105002. Cryptolestes pusillus, Lasioderma serricorne, Gnathocerus cornutus, Oryzaephilus mercator, Oryzaephilus surinamensis, Rhyzopertha dominica, Sitophilus granarius, Sitophilus oryzae, Stegobium paniceum, Tribolium brevicornis, Tribolium castaneum, Tribolium confusum, Tribolium freemani, and Tribolium madens, Zabrotes subfasciatus.
2 Yang, Zhao, H., Zhang, D., Cao, Y., Teng, S.W., Pang, S., Zhou, X., Li, Y., 2022. Auto- identification of two Sitophilus sibling species on stored wheat using deep convolutional neural network. Pest Manag. Sci. 78 (5), 1925–1937. https://doi.org/ 10.1002/ps.6810. Sitophilus oryzae 92.6% > Sitophilus zeamais 88.1%. S. zeamais adults are darker and shinier than S. oryzae in visible light. Upgraded industrial camera system have been installed in over 100 000 grain depots of China.
Zhang, Xiaoyu, Jiale Guo, Yanhong Liu, Yuanyi Luo, … and Xiaoping Yan 2026. Intelligent aeration technologies in grain storage: A comprehensive review of multi-physics modeling and AI-driven control. J. Stored Prod. Res. 119, 103155.
6 Zhang, Zhong, W., Pan, H., 2021. Identification of stored grain pests by modified residual network. Comput. Electron. Agric. 182, 105983. https://doi.org/10.1016/j. compag.2021.105983. Sitotroga cerealella 100% > Cryptolestes pusillus 98% > Rhizopertha dominica 97% > Oryzaephilus surinamensis 95% > Sitophilus oryzae 95% = Sitophilus zeamais 95%.
12 Zhao, C., Bai, C., Yan, L., Xiong, H., Suthisut, D., Pobsuk, P., Wang, D., 2024a. AC-YOLO: multi-category and high-precision detection model for stored grain pests based on integrated multiple attention mechanisms. Expert Syst. Appl. 255, 124659. https:// doi.org/10.1016/j.eswa.2024.124659. Plodia interpunctella 98.3% > Cynaeus angustus 98.2% > Sitophilus zeamais 97.4% > Gibbium aequinoctiale 97.3% > Tribolium castaneum 96.4% > Ephestia elutella 92.4% > Oryzaephilus surinamensis 91.0% > Cryptolestes ferrugineus 90.0% > Rhyzopertha dominica 89.1% > Cryptolestes pusillus 86.3% > Lasioderma serricorne 84.8% > Sitotroga cerealella 81.8% (Bold only in this study). Similarity in color between wheat and Sitotroga cerealella makes accurate detection challenging.
5 Zhou, Miao, H., Li, J., Jian, F., Jayas, D.S., 2019. A low-resolution image restoration classifier network to identify stored-grain insects from images of sticky boards. Comput. Electron. Agric. 162, 593–601. https://doi.org/10.1016/j. compag.2019.05.015. Lasioderma serricorne, Oryzaephilus surinamensis, Rhizopertha dominica, Sitophilus oryzae, Tribolium castaneum
6 Zhu, Li, D., Zheng, Y., Ma, Y., Yan, X., Zhou, Q., Wang, Q., Zheng, Y. 2025a. A YOLO- based model for detecting stored-grain insects on surface of grain bulks. Insects 16 (2), 210. Cryptolestes ferrugineus, Oryzaephilus surinamensis, Rhyzopertha dominica, Sitophilus zeamais, Sitophilus oryzae, Tribolium castaneum. Similarities of shape and color of T. castaneum and R. dominica make them more difficult to distinguish from one another.
