Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring
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- Jian Wang & Haiping Si & Zhao Gao & Lei Shi, 2022. "Winter Wheat Yield Prediction Using an LSTM Model from MODIS LAI Products," Agriculture, MDPI, vol. 12(10), pages 1-13, October.
- Yinghao Lin & Qingjiu Tian & Baojun Qiao & Yu Wu & Xianyu Zuo & Yi Xie & Yang Lian, 2022. "A Synthetic Angle Normalization Model of Vegetation Canopy Reflectance for Geostationary Satellite Remote Sensing Data," Agriculture, MDPI, vol. 12(10), pages 1-13, October.
- Qianjing Li & Jia Tian & Qingjiu Tian, 2023. "Deep Learning Application for Crop Classification via Multi-Temporal Remote Sensing Images," Agriculture, MDPI, vol. 13(4), pages 1-19, April.
- Xueqin Jiang & Shanjun Luo & Qin Ye & Xican Li & Weihua Jiao, 2022. "Hyperspectral Estimates of Soil Moisture Content Incorporating Harmonic Indicators and Machine Learning," Agriculture, MDPI, vol. 12(8), pages 1-17, August.
- Huishan Li & Lei Shi & Siwen Fang & Fei Yin, 2023. "Real-Time Detection of Apple Leaf Diseases in Natural Scenes Based on YOLOv5," Agriculture, MDPI, vol. 13(4), pages 1-19, April.
- Jingyu Hu & Jibo Yue & Xin Xu & Shaoyu Han & Tong Sun & Yang Liu & Haikuan Feng & Hongbo Qiao, 2023. "UAV-Based Remote Sensing for Soybean FVC, LCC, and Maturity Monitoring," Agriculture, MDPI, vol. 13(3), pages 1-19, March.
- Shanjun Luo & Xueqin Jiang & Weihua Jiao & Kaili Yang & Yuanjin Li & Shenghui Fang, 2022. "Remotely Sensed Prediction of Rice Yield at Different Growth Durations Using UAV Multispectral Imagery," Agriculture, MDPI, vol. 12(9), pages 1-17, September.
- Hui Zhang & Zhi Wang & Yufeng Guo & Ye Ma & Wenkai Cao & Dexin Chen & Shangbin Yang & Rui Gao, 2022. "Weed Detection in Peanut Fields Based on Machine Vision," Agriculture, MDPI, vol. 12(10), pages 1-15, September.
- Qi Wang & Peng Guo & Shiwei Dong & Yu Liu & Yuchun Pan & Cunjun Li, 2023. "Extraction of Cropland Spatial Distribution Information Using Multi-Seasonal Fractal Features: A Case Study of Black Soil in Lishu County, China," Agriculture, MDPI, vol. 13(2), pages 1-19, February.
- Sergey S. Yurochka & Igor M. Dovlatov & Dmitriy Y. Pavkin & Vladimir A. Panchenko & Aleksandr A. Smirnov & Yuri A. Proshkin & Igor Yudaev, 2023. "Technology of Automatic Evaluation of Dairy Herd Fatness," Agriculture, MDPI, vol. 13(7), pages 1-19, July.
- Chunfeng Gao & Xingjie Ji & Qiang He & Zheng Gong & Heguang Sun & Tiantian Wen & Wei Guo, 2023. "Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral Imagery," Agriculture, MDPI, vol. 13(2), pages 1-16, January.
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