Hybrid-AI and Model Ensembling to Exploit UAV-Based RGB Imagery: An Evaluation of Sorghum Crop’s Nitrogen Content

Hajar Hammouch, Suchitra Patil, Sunita Choudhary (), Mounim A. El-Yacoubi, Jan Masner (), Jana Kholová, Krithika Anbazhagan, Jiří Vaněk, Huafeng Qin, Michal Stočes, Hassan Berbia, Adinarayana Jagarlapudi, Magesh Chandramouli, Srinivas Mamidi, Prasad Kvsv and Rekha Baddam
Additional contact information
Hajar Hammouch: SAMOVAR, Telecom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France
Suchitra Patil: Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, Maharashtra, India
Sunita Choudhary: Crop Physiology and Modeling, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Telangana, India
Mounim A. El-Yacoubi: SAMOVAR, Telecom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France
Jan Masner: Department of Information Technologies, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic
Jana Kholová: Crop Physiology and Modeling, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Telangana, India
Krithika Anbazhagan: International Livestock Research Institute (ILRI), Patancheru, Hyderabad 502 324, Telangana, India
Jiří Vaněk: Department of Information Technologies, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic
Huafeng Qin: National Research base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China
Michal Stočes: Department of Information Technologies, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic
Hassan Berbia: SSLAB, Ecole Nationale Supérieure d’Informatique et d’Analyse des Systèmes, Mohamed V University, Rabat 10100, Morocco
Adinarayana Jagarlapudi: Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, Maharashtra, India
Magesh Chandramouli: Computer Graphics Technology, Purdue University NW, Hammond, IN 46323, USA
Srinivas Mamidi: Marut Dronetech Private Limited, Gachibowli, Hyderabad 500 032, Telangana, India
Prasad Kvsv: International Livestock Research Institute (ILRI), Patancheru, Hyderabad 502 324, Telangana, India
Rekha Baddam: Crop Physiology and Modeling, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru 502 324, Telangana, India

Agriculture, 2024, vol. 14, issue 10, 1-15

Abstract: Non-invasive crop analysis through image-based methods holds great promise for applications in plant research, yet accurate and robust trait inference from images remains a critical challenge. Our study investigates the potential of AI model ensembling and hybridization approaches to infer sorghum crop traits from RGB images generated via unmanned aerial vehicle (UAV). In our study, we cultivated 21 sorghum cultivars in two independent seasons (2021 and 2022) with a gradient of fertilizer and water inputs. We collected 470 ground-truth N measurements and captured corresponding RGB images with a drone-mounted camera. We computed five RGB vegetation indices, employed several ML models such as MLR, MLP, and various CNN architectures (season 2021), and compared their prediction accuracy for N-inference on the independent test set (season 2022). We assessed strategies that leveraged both deep and handcrafted features, namely hybridized and ensembled AI architectures. Our approach considered two different datasets collected during the two seasons (2021 and 2022), with the training set from the first season only. This allowed for testing of the models’ robustness, particularly their sensitivity to concept drifts, in the independent season (2022), which is fundamental for practical agriculture applications. Our findings underscore the superiority of hybrid and ensembled AI algorithms in these experiments. The MLP + CNN-VGG16 combination achieved the best accuracy (R 2 = 0.733, MAE = 0.264 N% on an independent dataset). This study emphasized that carefully crafted AI-based models applied to RGB images can achieve robust trait prediction with accuracies comparable to the similar phenotyping tasks using more complex (multi- and hyper-spectral) sensors presented in the current literature.

Keywords: AI; machine learning; UAV; RGB; nitrogen; phenotyping (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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