Changes in paddy cropping system enhanced economic profit and ecological sustainability in central China

In China,the traditional early and late season double rice(DR)system is declining accompanied by the fast increase of two newly developed cropping systems:ratoon rice(RR)and rice-crawfish(RC).Three methodologies:economic analysis,emergy evaluation and life cycle assessment(LCA)were employed to evaluate the economics and sustainability of this paddy cropping system change.Economic analysis indicated that the income and profit of the RC system were far larger than those of RR and DR.The income to costs ratio of RR and RC increased by 25.5 and 122.7%compared with that of DR,respectively.RC had the highest emergy input thanks to increasing irrigation water,electricity,juvenile crawfish and forage input while RR showed a lower total emergy and nonrenewable emergy input,such as irrigation water,electricity,fertilizers and pesticides than DR.The environmental loading ratios decreased by 16.7-50.4%when cropping system changed from DR to RR or from DR to RC while the emergy sustainability indexes increased by 22.6-112.9%.The life cycle assessment indicated lower potential environmental impacts of RR and RC,whose total environmental impact indexes were 35.0-61.0%lower than that of DR.Grain yield of RR was comparable with that of DR in spite of less financial and emergy input of RR,but RC had a much lower grain yield(a 53.6%reduction compared to DR).These results suggested that RR is a suitable cropping system to achieve the food security,economic and environmental goals.

Classification of yield affecting biotic and abiotic paddy crop stresses using field images

On-time recognition and early control of the stresses in the paddy crops at the booting growth stage is the key to prevent qualitative and quantitative loss of agricultural yield.The conventional paddy crop stress identification and classification activities invariably rely on human experts to identify visual symptoms as a means of categorization.This process is admittedly subjective and error-prone,which in turn may lead to incorrect action in stress management decisions.The proposed work presented in this paper aims to develop an automated computer vision system for the recognition and classification of paddy crop stress types from the field images using the state-of-the-art color features.The work examines the impact of eleven stress types,two biotic and nine abiotic stresses,on five different paddy crop varieties during the booting growth stage using field images and analyzes the stress responses in terms of color variations using lower-order color moments and two visual color descriptors defined by the MPEG-7 standard,the Dominant Color Descriptor(DCD)and Color Layout Descriptor(CLD).The Sequential Forward Floating Selection(SFFS)algorithm has been employed to reduce the overlapping between the features.Three different classifiers,the Back Propagation Neural Network(BPNN),the Support Vector Machine(SVM),and the k-Nearest Neighbor(k-NN)have been deployed to distinguish among stress types.The average stress classification accuracies of 89.12%,84.44%and 76.34%have been achieved using the BPNN,SVM,and k-NN classifiers,respectively.The proposed work finds application in the development of decision support systems and mobile apps for the automation of crop and resource management practices in the field of agricultural science.

A comparative analysis of paddy crop biotic stress classification using pre-trained deep neural networks

The agriculture sector is no exception to the widespread usage of deep learning tools and techniques.In this paper,an automated detection method on the basis of pre-trained Convolutional Neural Network(CNN)models is proposed to identify and classify paddy crop biotic stresses from the field images.The proposed work also provides the empirical comparison among the leading CNN models with transfer learning from the ImageNet weights namely,Inception-V3,VGG-16,ResNet-50,DenseNet-121 and MobileNet-28.Brown spot,hispa,and leaf blast,three of the most common and destructive paddy crop biotic stresses that occur during the flowering and ripening growth stages are considered for the experimentation.The experimental results reveal that the ResNet-50 model achieves the highest average paddy crop stress classification accuracy of 92.61%outperforming the other considered CNN models.The study explores the feasibility of CNN models for the paddy crop stress identification as well as the applicability of automated methods to non-experts.

Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images

On-time recognition and early control of the stresses in the paddy crops at the booting growth stage is the key to prevent qualitative and quantitative loss of agricultural yield.The conventional paddy crop stress recognition and classification activities invariably rely on human experts identifying visual symptoms as a means of categorization.This process is admittedly subjective and error-prone,which in turn can lead to incorrect actions being taken in stress management decisions.The work presented in this paper aims to design a deep convolutional neural network(DCNN)framework for automatic recognition and classification of various biotic and abiotic paddy crop stresses using the field images.Thework has adopted the pre-trained VGG-16 CNN model for the automatic classification of stressed paddy crop images captured during the booting growth stage.The trained models achieve an average accuracy of 92.89%on the held-out dataset,demonstrating the technical feasibility of using the deep learning approach utilizing 30,000 field images of 5 different paddy crop varieties with 12 different stress categories(including healthy/normal).The proposed work finds applications in developing the decision support systems and mobile applications for automating the field crop and resource management practices.

Study on a new grain-cash cropping pattern in paddy field

We studied a new grain-cash cropping pattern composed of "barley/watermelon + waize-rice", comparing with "barley / watermelon-rice" (CKI), "watermelon-rice", (CK2). The experiment was conducted in CNRRI’s field for 3 yr, employing completely randomized design with three replications, and each plot occupied 207 m~2. The soil in the experiment field contained: N, 2.48 g. kg, quick effective P 4 mg. kg, and quick effective K 53 mg·kg. The varieties tested were Zhemai 2 (barley), Zhemi 1 (watermelon), Suyu 1 (maize), and Shanyou 63 (hybrid rice).

Multiple cropping and soil fertility in sustainable production of paddy fields

Multiple cropping has been popularized on morethan two thirds of the total area of paddy fields inSouth China.It demands more nutrients due tohigher cropping index.Therefore,how to keepmoderately higher yields of multiple crops and to