干旱区生态环境知识资源中心(Arid): Integration of Spectral Reflectance Indices and Adaptive Neuro-Fuzzy Inference System for Assessing the Growth Performance and Yield of Potato under Different Drip Irrigation Regimes

Arid

DOI	10.3390/chemosensors9030055
	Integration of Spectral Reflectance Indices and Adaptive Neuro-Fuzzy Inference System for Assessing the Growth Performance and Yield of Potato under Different Drip Irrigation Regimes
	Elsayed, Salah; El-Hendawy, Salah; Khadr, Mosaad; Elsherbiny, Osama; Al-Suhaibani, Nasser; Dewir, Yaser Hassan; Tahir, Muhammad Usman; Mubushar, Muhammad; Darwish, Waleed
通讯作者	El-Hendawy, S (corresponding author), King Saud Univ, Coll Food & Agr Sci, Dept Plant Prod, POB 2460, Riyadh 11451, Saudi Arabia. ; El-Hendawy, S (corresponding author), Suez Canal Univ, Dept Agron, Fac Agr, Ismailia 41522, Egypt.
来源期刊	CHEMOSENSORS
EISSN	2227-9040
出版年	2021
卷号	9 期号:3
英文摘要	Simultaneous and timely assessment of growth and water status-related plant traits is critical for precision irrigation management in arid regions. Here, we used proximal hyperspectral sensing tools to estimate biomass fresh weight (BFW), biomass dry weight (BDW), canopy water content (CWC), and total tuber yield (TTY) of two potato varieties irrigated with 100%, 75%, and 50% of the estimated crop evapotranspiration (ETc). Plant traits were assessed remotely using published and newly constructed vegetation and water spectral reflectance indices (SRIs). We integrated genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS) models to predict the measured traits based on all SRIs. The different plant traits and SRIs varied significantly (p < 0.05) between the three irrigation regimes for the two varieties. The values of plant traits and majority SRIs showed a continuous decrease from the 100% ETc to the 50% ETc. Water-SRIs performed better than vegetation-SRIs for estimating the four plant traits. Almost all indices of the two SRI types had a weak relationship with the four plant traits (R2 = 0.00-0.37) under each irrigation regime. However, the majority of vegetation-SRIs and all water-SRIs showed strong relationships with BFW, CWC, and TTY (R2 >= 0.65) and moderate relationships with BDW (R2 >= 0.40) when the data of all irrigation regimes and varieties were analyzed together for each growing season or the data of all irrigation regimes, varieties, and seasons were combined together. The ANFIS-GA model predicted plant traits with satisfactory accuracy in both calibration (R-2 = 1.0) and testing (R-2 = 0.72-0.97) modes. The results indicate that SRI-based ANFIS models can improve plant trait estimation. This analysis also confirmed the benefits of applying GA to ANFIS to estimate plant responses to different growth conditions.
英文关键词	ANFIS biomass data driven genetic algorithm proximal remote sensing spectral indices tuber yield
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000633476600001
WOS关键词	WATER-STRESS DETECTION ; GRAIN-YIELD ; VEGETATION INDEXES ; CANOPY TEMPERATURE ; USE-EFFICIENCY ; CHLOROPHYLL CONTENT ; DROUGHT STRESS ; WHEAT ; NITROGEN ; LEAF
WOS类目	Chemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation
WOS研究方向	Chemistry ; Electrochemistry ; Instruments & Instrumentation
来源机构	King Saud University
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/349836
作者单位	[Elsayed, Salah; Darwish, Waleed] Univ Sadat City, Environm Studies & Res Inst, Agr Engn Evaluat Nat Resources Dept, Menoufia 32897, Egypt; [El-Hendawy, Salah; Al-Suhaibani, Nasser; Dewir, Yaser Hassan; Tahir, Muhammad Usman; Mubushar, Muhammad] King Saud Univ, Coll Food & Agr Sci, Dept Plant Prod, POB 2460, Riyadh 11451, Saudi Arabia; [El-Hendawy, Salah] Suez Canal Univ, Dept Agron, Fac Agr, Ismailia 41522, Egypt; [Khadr, Mosaad] Univ Bisha, Dept Civil Engn, Coll Engn, Bisha 61922, Saudi Arabia; [Khadr, Mosaad] Tanta Univ, Irrigat & Hydraul Dept, Fac Engn, Tanta 31734, Egypt; [Elsherbiny, Osama] Mansoura Univ, Dept Agr Engn, Fac Agr, Mansoura 35516, Egypt; [Dewir, Yaser Hassan] Kafrelsheikh Univ, Dept Hort, Fac Agr, Kafr Al Sheikh 33516, Egypt
推荐引用方式 GB/T 7714	Elsayed, Salah,El-Hendawy, Salah,Khadr, Mosaad,et al. Integration of Spectral Reflectance Indices and Adaptive Neuro-Fuzzy Inference System for Assessing the Growth Performance and Yield of Potato under Different Drip Irrigation Regimes[J]. King Saud University,2021,9(3).
APA	Elsayed, Salah.,El-Hendawy, Salah.,Khadr, Mosaad.,Elsherbiny, Osama.,Al-Suhaibani, Nasser.,...&Darwish, Waleed.(2021).Integration of Spectral Reflectance Indices and Adaptive Neuro-Fuzzy Inference System for Assessing the Growth Performance and Yield of Potato under Different Drip Irrigation Regimes.CHEMOSENSORS,9(3).
MLA	Elsayed, Salah,et al."Integration of Spectral Reflectance Indices and Adaptive Neuro-Fuzzy Inference System for Assessing the Growth Performance and Yield of Potato under Different Drip Irrigation Regimes".CHEMOSENSORS 9.3(2021).