Machine Learning Techniques for Phenology Assessment of Sugarcane Using Conjunctive SAR and Optical Data

Crop phenology monitoring is a necessary action for precision agriculture. Sentinel-1 and Sentinel-2 satellites provide us with the opportunity to monitor crop phenology at a high spatial resolution with high accuracy. The main objective of this study was to examine the potential of the Sentinel-1 and Sentinel-2 data and their combination for monitoring sugarcane phenological stages and evaluate the temporal behaviour of Sentinel-1 parameters and Sentinel-2 indices. Seven machine learning models, namely logistic regression, decision tree, random forest, artificial neural network, support vector machine, naïve Bayes, and fuzzy rule based systems, were implemented, and their predictive performance was compared. Accuracy, precision, specificity, sensitivity or recall, F score, area under curve of receiver operating characteristic and kappa value were used as performance metrics. The research was carried out in the Indo-Gangetic alluvial plains in the districts of Hisar and Jind, Haryana, India. The Sentinel-1 backscatters and parameters VV, alpha and anisotropy and, among Sentinel-2 indices, normalized difference vegetation index and weighted difference vegetation index were found to be the most important features for predicting sugarcane phenology. The accuracy of models ranged from 40 to 60%, 56 to 84% and 76 to 88% for Sentinel-1 data, Sentinel-2 data and combined data, respectively. Area under the ROC curve and kappa values also supported the supremacy of the combined use of Sentinel-1 and Sentinel-2 data. This study infers that combined Sentinel-1 and Sentinel-2 data are more efficient in predicting sugarcane phenology than Sentinel-1 and Sentinel-2 alone

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Not AvailableCrop phenology monitoring is a necessary action for precision agriculture. Sentinel-1 and Sentinel-2 satellites provide us with the opportunity to monitor crop phenology at a high spatial resolution with high accuracy. The main objective of this study was to examine the potential of the Sentinel-1 and Sentinel-2 data and their combination for monitoring sugarcane phenological stages and evaluate the temporal behaviour of Sentinel-1 parameters and Sentinel-2 indices. Seven machine learning models, namely logistic regression, decision tree, random forest, artificial neural network, support vector machine, naïve Bayes, and fuzzy rule based systems, were implemented, and their predictive performance was compared. Accuracy, precision, specificity, sensitivity or recall, F score, area under curve of receiver operating characteristic and kappa value were used as performance metrics. The research was carried out in the Indo-Gangetic alluvial plains in the districts of Hisar and Jind, Haryana, India. The Sentinel-1 backscatters and parameters VV, alpha and anisotropy and, among Sentinel-2 indices, normalized difference vegetation index and weighted difference vegetation index were found to be the most important features for predicting sugarcane phenology. The accuracy of models ranged from 40 to 60%, 56 to 84% and 76 to 88% for Sentinel-1 data, Sentinel-2 data and combined data, respectively. Area under the ROC curve and kappa values also supported the supremacy of the combined use of Sentinel-1 and Sentinel-2 data. This study infers that combined Sentinel-1 and Sentinel-2 data are more efficient in predicting sugarcane phenology than Sentinel-1 and Sentinel-2 alone.Not Availabl

Machine Learning Techniques for Phenology Assessment of Sugarcane Using Conjunctive SAR and Optical Data

Crop phenology monitoring is a necessary action for precision agriculture. Sentinel-1 and Sentinel-2 satellites provide us with the opportunity to monitor crop phenology at a high spatial resolution with high accuracy. The main objective of this study was to examine the potential of the Sentinel-1 and Sentinel-2 data and their combination for monitoring sugarcane phenological stages and evaluate the temporal behaviour of Sentinel-1 parameters and Sentinel-2 indices. Seven machine learning models, namely logistic regression, decision tree, random forest, artificial neural network, support vector machine, naïve Bayes, and fuzzy rule based systems, were implemented, and their predictive performance was compared. Accuracy, precision, specificity, sensitivity or recall, F score, area under curve of receiver operating characteristic and kappa value were used as performance metrics. The research was carried out in the Indo-Gangetic alluvial plains in the districts of Hisar and Jind, Haryana, India. The Sentinel-1 backscatters and parameters VV, alpha and anisotropy and, among Sentinel-2 indices, normalized difference vegetation index and weighted difference vegetation index were found to be the most important features for predicting sugarcane phenology. The accuracy of models ranged from 40 to 60%, 56 to 84% and 76 to 88% for Sentinel-1 data, Sentinel-2 data and combined data, respectively. Area under the ROC curve and kappa values also supported the supremacy of the combined use of Sentinel-1 and Sentinel-2 data. This study infers that combined Sentinel-1 and Sentinel-2 data are more efficient in predicting sugarcane phenology than Sentinel-1 and Sentinel-2 alone

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Not AvailableThere has been a growing trend for achieving sustainable crop intensification without jeopardizing land productivity through conservation agriculture (CA). The CA has paved the way for cultivation of pulses in diverse cropping systems. A field experiment was conducted at ICAR-Indian Agricultural Research Institute, New Delhi during 2018-19 and 2019-20 cropping cycle with summer greengram in maize-wheat system to assess the effects of CA on weed interference, crop productivity and resource use efficiency. Results showed that CA-based practices with residue retention resulted in a considerable reduction in weed density and biomass when compared to conventional tillage (CT). Greengram yield parameters in CA were higher than in CT. The permanent broad bed (PBB) with residue retention (R) and recommended 100% N application (100N) (∼PBB+R+100N) gave ∼56% higher greengram grain yield than CT with considerably higher water productivity, nutrient-use efficiency and net returns. The adoption of CA practice involving PBB+R in greengram led to higher weed control efficiency and was more productive, remunerative and irrigation water-use efficient. Thus, it could potentially boost up the greengram productivity, profitability and resource-use efficiency under maize-wheat-greengram system in north-western Indo-Gangetic Plains (IGP) of India.Not Availabl

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Not AvailableConservation agriculture (CA) involving minimum mechanical soil disturbance, permanent soil cover with crop residue mulch and diversified crop rotation, plays a crucial role in sustainable crop production. A field experiment was conducted at ICAR-Indian Agricultural Research Institute, New Delhi during rabi seasons (November–April) of 2018–19 and 2019–20 in wheat involving maize-wheat-mungbean system to assess the effects of CA on crop productivity, nutrient uptake and profitability. Results showed that CA-based practices with residue retention resulted in higher yield as well as economic benefits when compared to conventional tillage (CT). Wheat yield parameters in CA were greater than in CT. The CAbased practices improved wheat grain and straw yield to the tune of 7.2–27.1% and 5.7–20.6%, respectively compared to CT practice. The CA-based practices with residue retention with 100% N registered 9.7% higher cost of cultivation, but resulted in 24.3–35.1% higher net returns than CT. Among CA-based practices, the plots under permanent broad bed with residue with 100% N (PBB+R+100N) resulted in ~27% higher wheat grain yield compared to CT. The PBB+R+100N plots also had considerably greater nutrient uptake and net returns than CT plots. The CA practice involving PBB+R+100N was found to be more productive, remunerative and could potentially boost up the wheat productivity and profitability under maize-wheatmungbean system in north-western Indo-Gangetic Plains of India.Not Availabl

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Not AvailableConservation agriculture (CA) can promote sustainable crop intensification. However, weeds are the major constraints under CA, in the initial years. Nitrogen (N) management under CA is also crucial. A field experiment was undertaken to study the effect of conventional tillage (CT) and CA with and without residue using 75 and 100% recommended N dose on weed dynamics and crop productivity during 2018-19 and 2019-20 in maize (Zea mays L.) under maize - wheat (Triticum aestivum L.) - greengram (Vigna radiata (L.) Wilczek) cropping system at ICAR-Indian Agricultural Research Institute, New Delhi. Nine CA-based treatments and one conventional tillage were laid out in a randomized complete block design with three replications. CA-based zero till (ZT) bed planting systems with residue retention resulted in significant reductions in total weed density and biomass compared to CT. Permanent broad bed with residue using 75% N resulted in 34% lesser weed density than CT. Among the CA-based treatments, the permanent broad bed with residue using 100% N resulted in 22% higher maize grain yield than CT (5.72 t/ha) with 36% higher net returns than CT. However, the permanent broad bed with residue using 75% N was found comparable in this regard and may be recommended for sustainable maize production under the maize-wheat-greengram system in north-western Indo Gangetic Plains of India.Not Availabl