Foxtail Millet Growth Prediction Using Hybrid Model of Machine Learning and Deep Learning with Efficient Feature Extraction
Keywords:
Foxtail Millet, hybrid model, DT, RF, MobileNet, SqueezeNet, VGG16Abstract
Agriculture is the mainstay of the Indian economy and it is important to enhance the production with the help of technology. Crop production is a complex phenomenon that is influenced by various parameters like climatic conditions, fertilizers, production, rainfall, etc. Recent advancements in agricultural technology have embraced image annotation through Machine Learning techniques. The surge in image data has significantly fueled the interest in image annotation. Leveraging deep learning for image annotation facilitates the extraction of image features and has proven effective in analyzing vast datasets. Deep learning, drawing inspiration from the human brain's structure and relying on artificial neural networks, stands as a powerful machine learning method. This paper presents Foxtail Millet growth prediction using hybrid model of Machine Learning and Deep Learning with efficient Feature Extraction. VGG16 network used to extract features from images of the Foxtail Millet plants at different stages of growth. Decision Tree (DT), Random Forest (RF) are used machine learning models and MobileNet, SqueezeNet are used deep learning models. Hybrid model involves the combination of machine learning and deep learning models. An experimental result clears that described model is efficiently predicts the growth of Foxtail Millet in terms of Accuracy, Precision, Recall and F1-Score parameters than other classification models.
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Urvashi Sugandh, Swati Nigam, Manju Khari, “Ecosystem of Technologies for Smart Agriculture to Improve the Efficiency and Profitability of Indian Farmers”, 2023 10th International Conference on Computing for Sustainable Global Development (INDIACom), Year: 2023
1. Li, S.; Liu, F.; Liu, M.; Cheng, R.; Xia, E.; Diao, X. Current status and future prospective of foxtail millet production and seed industry in China. Sci. Agric. Sin. 2021, 54, 459–470.
Theodora Sanida, Dimitris Tsiktsiris, Argyrios Sideris, Minas Dasygenis, “A Heterogeneous Lightweight Network for Plant Disease Classification”, 2021 10th International Conference on Modern Circuits and Systems Technologies (MOCAST), Year: 2021
Thesiya NM, Dobariya JB, Patel JG. Effect of integrated nutrient management on growth and yield parameters of kharif little millet under little millet-green gram cropping sequence. International Journal of Pure Applied Bioscience, 2019, 7(3):294-298.
Prof. D.S. Zingade, Omkar Buchade, Nilesh Mehta, Shubham Ghodekar, Chandan, Crop Prediction System using Machine Learning ,Mehta,International Journal of Advance Engineering and Research Development Special Issue on Recent Trends in Data Engineering Volume 4, Special Issue 5, Dec.-2017
Umesh MR, Sharanappa Shrinivasa KR, Kirankumar KC. Effect of cropping systems and integrated nutrient management on growth, yield and nutrient uptake of finger millet under rainfed conditions. Crop Research, 2006, 31(3):366-369
Jyothi KN, Sumathi V, Sunitha N. Productivity, nutrient balance and profitability of foxtail millet (Setaria italica L.) varieties as influenced by levels of nitrogen. Journal of Agriculture and Veterinary Science, 2016; 9(4):18-22.
Yang, F.; Lei, X.; Liu, Z.; Fan, P.; Yan, B. “Fast Recognition Method for Multiple Apple Targets in Dense Scenes Based on CenterNet”, Trans. Chin. Soc. Agric. Mach. 2022, 53, 265–273.
Li, X.; Pan, J.; Xie, F.; Zeng, J.; Li, Q.; Huang, X.; Liu, D.; Wang, X. “Fast and accurate green pepper detection in complex backgrounds via an improved Yolov4-tiny model”, Comput. Electron. Agric. 2021, 191, 106503
Patil, B. V., &Patil, P. S. (2021). “Computational method for Cotton Plant disease detection of crop management using deep learning and internet of things platforms”, In Evolutionary Computing and Mobile Sustainable Networks (pp. 875- 885), Springer, Singapore
Dos Santos Ferreira, A.; Freitas, D.M.; da Silva, G.G.; Pistori, H.; Folhes, M.T. “Unsupervised deep learning and semi-automatic data labeling in weed discrimination”, Comput. Electron. Agric. 2019, 165, 104963.
Onishi, Y.; Yoshida, T.; Kurita, H.; Fukao, T.; Arihara, H.; Iwai, A. “An automated fruit harvesting robot by using deep learning”, ROBOMECH J. 2019, 6, 13
S. Das Choudhury, A. Samal, and T. Awada, “Leveraging Image Analysis for High-Throughput Plant Phenotyping,” Frontiers in Plant Science, vol. 10, Article no. 508, April 2019.
Pooja, V., Das, R., &Kanchana, V. (2017, April). “Identification of plant leaf diseases using image processing techniques”, In 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR) (pp. 130-133), IEEE
D Ramesh, B Vishnu Vardhan, ”Analysis of Crop Yield Prediction Using Data Mining Techniques”, IJRET, 2015
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