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Classification. Agronomy, 10, 1, 143, 2020.
30. Zhu, M., Liu, S., Xia, Z., Wang, G., Hu, Y., Liu, Z., Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN. Agriculture, 10, 8, 318, 2020.
31. Sharma, N., Chakrabarti, A., Balas, V.E., Advances in Intelligent Systems and Computing, vol. 1016, pp. 311–324, Springer, Singapore, 2020.
32. Liu, S., Peng, Y., Xia, Z., Hu, Y., Wang, G., Zhu, A.-X., Liu, Z., The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data. Sensors, 19, 5127, 2019.
33. Martineau, M., Conte, D., Raveaux, R., Arnault, I., Munier, D., Venturini, G., A survey on image-based insect classification. Pattern Recognit., 65, 273–284, 2017.
34. Wang, J., Lin, C., Ji, L., Liang, A., A new automatic identification system of insect images at the order level. Knowl. Based Syst., 33, 102–110, 2012.
35. Ferentinos, K., Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric., 145, 311–318, 2018.
36. Lu, Y., Yi, S., Zeng, N., Liu, Y., Zhang, Y., Identification of rice diseases using deep convolutional neural networks. Neurocomputing, 267, 378–384, 20172017.
37. Knoll, F.J., Czymmek, V., Harders, L.O., Hussmann, S., Real-time classification of weeds in organic carrot production using deep learning algorithms. Comput. Electron. Agric., 167, 105097, 2019.
38. Przybylak, A., Kozłowski, R., Osuch, E., Osuch, A., Rybacki, P., Przygodzinski, P., Quality Evaluation of Potato Tubers Using Neural Image Analysis Method. Agriculture, 10, 112, 2020.
39. Li, Y. and Yang, J., Few-shot cotton pest recognition and terminal realization. Comput. Electron. Agric., 169, 105240, 2020.
40. Chatterjee, S., Dey, N., Sen, S., Soil moisture quantity prediction using optimized neural supported model for sustainable agricultural applications. Sustain. Comput. Inform. Syst., 28, 100279, 2018.
41. Almomani, F., Prediction of biogas production from chemically treated co-digested agricultural waste using artificial neural network. Fuel, 280, 118573, 2020.
42. Abraham, E.R., dos Reis, J.G.M., Colossetti, A.P., de Souza, A.E., Toloi, R.C.,Neural Network System to Forecast the Soybean Exportation on Brazilian Port of Santos. Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing, pp. 83–90, 2017.
43. Abraham, E.R., Mendes dos Reis, J.G., Vendrametto, O., Oliveira Costa Neto, P.L.D., Carlo Toloi, R., Souza, A.E.D., Oliveira Morais, M.D., Time Series Prediction with Artificial Neural Networks: An Analysis Using Brazilian Soybean Production. Agriculture, 10, 10, 475, 2020.
44. Liu, G., Yang, X., Li, M., An Artificial Neural Network Model for Crop Yield Responding to Soil Parameters, in: Advances in Neural Network, vol. 3498, pp. 1017–1021, 2020.
45. García-Martínez, H., Flores-Magdaleno, H., Ascencio-Hernández, R., Khalil-Gardezi, A., Tijerina-Chávez, L., Mancilla-Villa, O.R., Vázquez-Pena, M.A., Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles. Agriculture, 10, 7, 277, 2020.
46. Fieuzal, R., Marais Sicre, C., Baup, F., Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks. Int. J. Appl. Earth Obs. Geoinf., 57, 14–23, 20172017.
47. Michelon, G.K., Menezes, P.L., de Bazzi, C.L., Jasse, E.P., Magalhães, P.S.G., Borges, L.F., Artificial neural networks to estimate the productivity of soybeans and corn by chlorophyll readings. J. Plant Nutr., 41, 1285–1292, 2018.
48. Olson, D., Chatterjee, A., Franzen, D.W., Day, S.S., Relationship of Drone-Based Vegetation Indices with Corn and Sugarbeet Yields. Agron. J., 111, 2545–2557, 2019.
49. Khaki, S., Khalilzadeh, Z., Wang, L., Predicting yield performance of parents in plant breeding: A neural collaborative filtering approach. PloS One, 15, 5, e0233382, 2020.
50. Jeong, J.H., Resop, J.P., Mueller, N.D., Fleisher, D.H., Yun, K., Butler, E.E., et al., Random forests for global and regional crop yield predictions. PloS One, 11, e0156571, 2016.
51. Krupavathi, K., Raghu Babu, M., Mani, A., Parasad, P.R.K., Edukondal, L., Seed to Seed: Application of Remote Sensing in Complete Monitoring of Sugarcane Crop at Regional Level, in: Research Trends in Agriculture Sciences, vol. 25, pp. 35–59, Akinik publications, Delhi, India, 2020.
52. Ferencz, C., Bognár, P., Lichtenberger, J., Hamar, D., Tarcsai, G., Timár, G., Molnár, G., Pásztor, S., Steinbach, P., Székely, B., Ferencz, O.E., FerenczÁrkos, I., Crop yield estimation by satellite remote sensing. Int. J. Remote Sens., 25, 4113–4149, 2004.
53. Prasad, A.K., Chai, L., Singh, R.P., Kafatos, M., Crop yield estimation model for Iowa using remote sensing and surface parameters. Int. J. Appl. Earth Obs. Geoinf., 8, 1, 26–33, 2006.
54. Singh, R. A. N. D. H. I. R., Semwal, D.P., Rai, A., Chhikara, R.S., Small area estimation of crop yield using remote sensing satellite data. Int. J. Remote Sens., 23, 1, 49–56, 2002.
55. Quarmby, N.A., Milnes, M., Hindle, T.L., Silleos, N., The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. Int. J. Remote Sens., 14, 2, 199–210, 1993.
56.Unganai, L.S. and Kogan, F.N., Drought monitoring and corn yield estimation in Southern Africa from AVHRR data. Remote Sens. Environ., 63, 3, 219–232, 1998.
57. Zhou, X., Zheng, H.B., Xu, X.Q., He, J.Y., Ge, X.K., Yao, X., Cheng, T., Zhu, Y., Cao, W.X., Tian, Y.C., “Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS J. Photogramm. Remote Sens., 130, 246–255, 2017.
58. Bastiaanssen, W.G. and Ali, S., A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan. Agric. Ecosyst. Environ., 94, 3, 321–340, 2003.
59. Sapkota, T.B., Jat, M.L., Jat, R.K., Kapoor, P., Stirling, C., Yield estimation of food and non-food crops in smallholder production systems, in: Methods for measuring greenhouse gas balances and evaluating mitigation options in smallholder agriculture, pp. 163–174, 2016.
60. Hooda, R.S., Yadav, M., Kalubarme, M.H., Wheat production estimation using remote sensing data: An Indian experience, in: Workshop Proceedings: Remote Sensing Support to Crop Yield Forecast and Area Estimates, Stresa, Italy, vol. 30, pp. 85–89, 2006.
61. Kumhalova, J., Zemek, F., Novak, P., Brovkina, O., Mayerovaa, M., Use of Landsat images for yield evaluation within a small plot. Plant Soil Environ., 60, 11, 501–506, 2014.
62. Kaul, M., Hill, R.L., Walthall, C., Artificial neural networks for corn and soybean yield prediction. Agric. Syst., 85, 1, 1–18, 2005.
63. Jiang, D., Yang, X., Clinton, N., Wang, N., An artificial neural network model for estimating crop yields using remotely sensed information. Int. J. Remote Sens., 25, 9, 1723–1732, 2004.
64. Patel, N.R., Bhattacharjee, B., Mohammed, A.J., Tanupriya, B., Saha, S.K., Remote sensing of regional yield assessment of wheat in Haryana, India. Int.J. Remote Sens., 27, 19, 4071–4090, 2006.
65. Sims, D.A., Parallel adjustments in vegetation greenness and ecosystem CO2 exchange in response to drought in a Southern California chaparral ecosystem. Remote Sens. Environ., 103, 3, 289–303, 2005.
66. Peng, Z., Hu, M., Liu, Y., Application of RS and GIS Technique to Estimate Regional Water-saving Potentiality, 2007.
67. Singh, R.K. and Prajneshu, Artificial Neural