Towards Intelligent Detection and Classification of Rice Plant Diseases Based on Leaf Image Dataset

The detection of rice leaf disease is significant because,as an agricultural and rice exporter country,Pakistan needs to advance in production and lower the risk of diseases.In this rapid globalization era,information technology has increased.A sensing system is mandatory to detect rice diseases using Artificial Intelligence(AI).It is being adopted in all medical and plant sciences fields to access and measure the accuracy of results and detection while lowering the risk of diseases.Deep Neural Network(DNN)is a novel technique that will help detect disease present on a rice leave because DNN is also considered a state-of-the-art solution in image detection using sensing nodes.Further in this paper,the adoption of the mixed-method approach Deep Convolutional Neural Network(Deep CNN)has assisted the research in increasing the effectiveness of the proposed method.Deep CNN is used for image recognition and is a class of deep-learning neural networks.CNN is popular and mostly used in the field of image recognition.A dataset of images with three main leaf diseases is selected for training and testing the proposed model.After the image acquisition and preprocessing process,the Deep CNN model was trained to detect and classify three rice diseases(Brown spot,bacterial blight,and blast disease).The proposed model achieved 98.3%accuracy in comparison with similar state-of-the-art techniques.

Spatio-Temporal Changes in the Rice Planting Area and Their Relationship to Climate Change in Northeast China: A Model-Based Analysis

Rice is one of the most important grain crops in Northeast China （NEC） and its cultivation is sensitive to climate change. This study aimed to explore the spatio-temporal changes in the NEC rice planting area over the period of 1980-2010 and to analyze their relationship to climate change. To do so, the CLUE-S （conversion of land use and its effects at small region extent） model was ifrst updated and used to simulate dynamic changes in the rice planting area in NEC to understand spatio-temporal change trends during three periods： 1980-1990, 1990-2000 and 2000-2010. The changing results in individual periods were then linked to climatic variables to investigate the climatic drivers of these changes. Results showed that the NEC rice planting area expanded quickly and increased by nearly 4.5 times during 1980-2010. The concentration of newly planted rice areas in NEC constantly moved northward and the changes were strongly dependent on latitude. This conifrmed that climate change, increases in temperature in particular, greatly inlfuenced the shift in the rice planting area. The shift in the north limit of the NEC rice planting area generally followed a 1＆#176;C isoline migration pattern, but with an obvious time-lag effect. These ifndings can help policy makers and crop producers take proper adaptation measures even when exposed to the global warming situation in NEC.

An Optimal Classification Model for Rice Plant Disease Detection

Internet of Things(IoT)paves a new direction in the domain of smart farming and precision agriculture.Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent.In smart farming,IoT devices are linked among one another with new technologies to improve the agricultural practices.Smart farming makes use of IoT devices and contributes in effective decision making.Rice is the major food source in most of the countries.So,it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT devices.The development and application of Deep Learning(DL)models in agriculture offers a way for early detection of rice diseases and increase the yield and profit.This study presents a new Convolutional Neural Network-based inception with ResNset v2 model and Optimal Weighted Extreme Learning Machine(CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart farming environment.The proposed CNNIR-OWELM method involves a set of IoT devices which capture the images of rice plants and transmit it to cloud server via internet.The CNNIROWELM method uses histogram segmentation technique to determine the affected regions in rice plant image.In addition,a DL-based inception with ResNet v2 model is engaged to extract the features.Besides,in OWELM,the Weighted Extreme Learning Machine(WELM),optimized by Flower Pollination Algorithm(FPA),is employed for classification purpose.The FPA is incorporated into WELM to determine the optimal parameters such as regularization coefficient C and kernelγ.The outcome of the presented model was validated against a benchmark image dataset and the results were compared with one another.The simulation results inferred that the presented model effectively diagnosed the disease with high sensitivity of 0.905,specificity of 0.961,and accuracy of 0.942.

RDA- CNN: Enhanced Super Resolution Method for Rice Plant Disease Classification

In thefield of agriculture,the development of an early warning diagnostic system is essential for timely detection and accurate diagnosis of diseases in rice plants.This research focuses on identifying the plant diseases and detecting them promptly through the advancements in thefield of computer vision.The images obtained from in-field farms are typically with less visual information.However,there is a significant impact on the classification accuracy in the disease diagnosis due to the lack of high-resolution crop images.We propose a novel Reconstructed Disease Aware–Convolutional Neural Network(RDA-CNN),inspired by recent CNN architectures,that integrates image super resolution and classification into a single model for rice plant disease classification.This network takes low-resolution images of rice crops as input and employs the super resolution layers to transform low-resolution images to super-resolution images to recover appearance such as spots,rot,and lesion on different parts of the rice plants.Extensive experimental results indicated that the proposed RDA-CNN method performs well under diverse aspects generating visually pleasing images and outperforms better than other con-ventional Super Resolution(SR)methods.Furthermore,these super-resolution images are subsequently passed through deep classification layers for disease classi-fication.The results demonstrate that the RDA-CNN significantly boosts the clas-sification performance by nearly 4–6%compared with the baseline architectures.

Rice plant growth and yield: foliar application of plasma activated water

Plasma activated water(PAW)was prepared for 10 min to be applied one tofive times as a foliar spray to rice plants,to investigate plant growth,yield and the concentrations of total soluble protein and sugar in the rice grains produced.The results reveal that(1)the plant height,stem diameter,dry weight,chlorophyll and total carotene concentrations were improved by∼15%,∼25%,∼24%,∼47%and∼45%,respectively,with respect to control,(2)defense mechanisms of the plants treated with PAW were improved,(3)concentrations of total soluble protein and sugar were enhanced in the rice grains of PAW treated plants and(4)yield was increased by∼14%.

THE INFLUENCE OF FERTILIZERS, NaHCO_3,SOIL TYPE ON CHEMISTRY OF FLOODED SOILS AND RICE PLANT GROWTH

This research was conducted to study the influence of NaHCO3and fertilizer treatments on flooded soils tothe chemistry of soil and rice plant growth.The experiment was done in a greenhouse using a split-split plot design ofthree factors: two types of soil,three doses of fertilizers and three levels of NaHCO3with three replications.The firstfactor was two kind of soils Fluvaquent and Calciquert,the factor was without nutrient(blank),macro nutrients,andcompletely nutrients(second macro and micro nutrients),and the third factor was 0 mg/kg,200 mg/kg and 400 mg/kgof NaHCO3or equivalent with 0 mg,1 200 mg and 2 400 mg NaHCO3per pot.Each experimental unit used a pot of 10L capacity and filled with soil mud equivalent with 6 kg of dried soil.After each of treatment combination mixed into themud soil,two plants of IR-32 variety were grown,and flooded at5 cm high accordingly with growth period.Parametersobserved were the growth of tillers,electrical conductivity(E.C.) and pH.The experiment results showed that the treatment of NaHCO3increased the soluble salt content(E.C.) and soilacidity(pH) during flooding on Calciquerts and Fluvaquents.The content of soluble salt decreased to 2.0 mmhos/cm,while pHon Calciquert decreased towards the neutral pH.Anyhowthe pHon Fluvaquents increased approach to neutral.The treatments of NaHCO3inhibited the tiller growth,either macro fertilizer and completely fertilizer increased thetillers,but did not improve the negative effect of alkalinity due to of NaHCOtreatment.

Free proline accumulation in rice plants under different leaf water potential

Seedlings of drought-tolerance rice varieties Han 501and Han A03,and the drought sensitive varietiesNanjing 11 and Yanjing 2 were raised in a paddyfield and transplanted into pots at the age of 8leaves.Water stress started at the tillering stage byholding water from 0 MPa of the soil water potentialin pots till the leaves showed seriously wilting.

Anther culture of Transgenic rice plants

Anther culture is widely used in rice improve-ment and genome research.It is useful forgene transformation to stabilize foreign gene(s)and estimate the integrated copy number.We used two transgenic plants JB-3 andJB-4 as donors for anther culture.Their originwas a japonica cultivar Jingyin 119,whose im-mature embryos were transformed by particlebombardment with plasmid pCB1(a cecropin B

Effect of CRMS on male sterility of rice plant

The experiments were carried out in paddy field of CNRRI in 1985-1989. Seedlings of the indica variety Erjiufeng were raised in a seedbed and transplanted to pots with area of 0.1 m~2 at seedling age of 30 d. Treatments with (CRMS) were conducted at different stages: pistil and stigma primordium formation

Comparative Analysis on Rice Plant Type of Two Super Hybrids and Shanyou63

The characteristics of plant type and physiological indices for photosynthesis were analyzed using two super hybrid rices, Liangyoupeijiu and LiangyouE32 as materials, and an indica hybrid, Shan-you63 as a control. Based on the present analysis and various theories and breeding practices on rice plant type, a model of plant ideotype for super hybrid rice (indica) in the lower reaches of Yangtze River Valley was proposed. This was: a length of 35 - 40 cm for top leaf with a width of 2 cm, and that of 50 - 55 cm and 55-60 cm, respectively, for the second and third leaves from top, meanwhile, having a leaf angle of 5°, 10°and 15° respectively, and a curvature of 1 - 1. 5 cm-1for the leaf face at heading stage; the uppermost three leaves keeping their activities for as long as 70 d, which could lead a LAI of 3 at full ripeness; loose plant type with thin (SLW=2.5-3 g cm-2, dry weight) and curve-slant leaves during early growing stage, and compact plant type by thick (SLW=4 - 5 g cm-2) and erect leaves during the middle and late growing stages; with a coefficient of light extinction of 0. 3 - 0. 4, which allowed for an optimal LAI high of 8-10 during the middle growth period; plant height of 110-120 cm, with 2 - 4 cm basal internode and a long uppermost internode occupying 45% of total stem length; 25 - 28 cm panicle with 8-10 spikelets per centimeter showing bend-type in ripening; a rich chlorophyll which led to a high net photosynthetic rate, and tolerance to light shading and photooxidation which is of benefit to increasing the adaptability to varying light conditions.

Effect of Cultural Measures on Nutrient Contellts in Rice Plants with Erect Panicles

Since1981,newricevarietieswitherectpanicles,suchasLiaojing5,Shennong91,Liaojing326andShennong8801,havebeenbredinLiaoningProvinceofChina.Experimelltalresultsindicatedthatthegrainyieldsofvarietieswitherectpaniclesnotonlyexceededthoseofcultivarswithcurv...

Nutritive Equilibrium in Rice Plant Populations for High Yield

The effects of nitrogen, phosphorus and potassium application level, seed rate and transplanting density on the growth and development of rice plants were studied to find out nutrient status in high-yielding rice plants and to increase grain yield by adequate fertilization. There was an equilibrium relationship among nutrient elements for high-yielding rice plant populations. The equilibrium index of nutrient amount, content and distribution in high-yielding rice plants should be generally greater than-2 but less than 2. The optimum nutritive proportion of nitrogen: phosphorus: potassium assimilated by the plants was about 10: 2: 9 at the ripening stage. But the content and the proportion varied with the growth stages. Therefore, the nutrient in rice plant populations should be in a dynamic equilibrium, so as to achieve high yield.

Characteristics of photosynthesis in rice plants transformed with an antisense Rubisco activase gene

Transgenic rice plants with an antisense gene inserted via Agrobacterium tumefaciens were used to explore the impact of the reduction of Rubisco activase (RCA) on Rubisco and photosynthesis. In this study, transformants containing 15% to 35% wild type Rubisco activase were selected, which could survive in ambient CO2 concentration but grew slowly compared with wild type controls. Gas exchange measurements indicated that the rate of photosynthesis decreased sig-nificantly, while stomatal conductance and transpiration rate did not change; and that the intercellular CO2 concentration even increased. Rubisco determination showed that these plants had approximately twice as much Rubisco as the wild types,although they showed 70% lower rate of photosynthesis, whichRubsico activase and/or the reduction in carbamylation.was likely an acclimation response to the reduction in Rubsico activase and/or the reduction in carbamylation.

Dissipation of pencycuron in rice plant

Pencycuron is a non-systemic protective fungicide for controlling sheath blight of rice. However, information on the fate of pencycuron in rice plant is lacking. The degradation of pencycuron in waterlogged tropic rice field was investigated.Pencycuron was applied at recommended field dose (187.5 g a.i./ha) and double recommended dose to cropped plots for three consecutive years. Pencycuron was rapidly degraded in rice plant at all doses of pencycuron application with first order half-lives of 1.57～2.77 d. The study revealed that pencycuron is safe from the human and environmental contamination point of view.

Use of Linear Spectral Mixture Model to Estimate Rice Planted Area Based on MODIS Data

MODIS （Moderate Resolution Imaging Spectroradiometer） is a key instrument aboard the Terra （EOS AM） and Aqua （EOS PM） satellites. Linear spectral mixture models are applied to MOIDS data for the sub-pixel classification of land covers. Shaoxing county of Zhejiang Province in China was chosen to be the study site and early rice was selected as the study crop. The derived proportions of land covers from MODIS pixel using linear spectral mixture models were compared with unsupervised classification derived from TM data acquired on the same day, which implies that MODIS data could be used as satellite data source for rice cultivation area estimation, possibly rice growth monitoring and yield forecasting on the regional scale.

Characteristics of Rice Plant with Heavy Panicle

Using 15 indica rice varieties with different panicle weight, some ideal plant type characteristics in heavy panicle type of hybrid rice （HPT） and their relation to yield and its components were studied. Results showed that the leaf area index （LAI） of the HPT varieties was lower than that of medium panicle type （MPT） and light panicle type （LPT） varieties, but its decreasing rate of LAI and efficient LAI after heading was slower and had much higher percentage of efficient LAI, specific leaf weight, and ratio of grain to leaf area （cm^2） in comparison with the MPT and the LPT varieties. The length, width, thickness, and area of top three leaves of the HPT varieties were significantly larger than those of the MPT and the LPT varieties, and these components of top three leaves were significantly and positively correlated with the number of spikelets and filled grains, grain weight per panicle, and grain yield. The flag leaf in HPT varieties was erect with sorrow leaf angle, and their leaf angle of 2nd and 3rd leaf from top increased in sequence. The plant height of the HPT varieties was higher than that of the MPT and the LPT varieties, and their leaf site of top three leaves also increased in sequence. Therefore, HPT varieties as an ideal plant type could increase the utilization efficiency of sunlight energy. The ideal plant type characteristics and their adjuncts for the HPT varieties are proposed in this article.

Contribution of Rice Plants and Cover Crop Biomass Amended Soil on Methane Emission

Rice plant and soil are playing vital role for produce of methane (CH4) emission from flooded rice soil. Contribution of rice plants and cover crop biomass amended soil on methane emission has not been yet studied under different cover crop biomass incorporated in paddy fields. Closed-chamber method was used to estimate CH4 emission rates during rice cultivation under soil plus rice plants and soil alone condition. Soil plus rice plants chambers 62 × 62 × 112 cm3 and soil alone chambers 20 × 20 cm2 were placed at the same time during rice cultivation (0 days after rice transplanting). Therefore, to evaluate the contribution of soil plus rice plants and soil alone on methane (CH4) emission under different rates of cover crop biomass incorporated soil during rice cultivation. Methane emission from soil plus rice plants increased up to 53 days after transplanting (DAT) and then it’s decreased and continued till harvesting. It was found that ca. 47% - 52% CH4 was mediated by rice plants and ca. 48% - 53% through rice soil alone under 12 Mg·ha-1 cover crop biomass incorporated treated plots. Whereas, only ca. 9% - 10% CH4 emission was mediated by rice plants and ca. 90% - 91% by rice soil alone when 0 and 3 Mg·ha-1 cover crop biomass was incorporated. Therefore, it could be concluded that rice soil alone was more influenced for CH4 emission than rice plants in paddy fields.