Prediction of spring maize yields using leaf color chart, chlorophyll meter, and GreenSeeker optical sensor

Summary Predicting in-season crop yield is a unique tool for drawing important crop management decisions for precision farming. Field experiments were conducted at two locations in northwestern India under different agro-climatic zones to predict and validate spring maize yield using various in-season spectral indices. The spectral properties measured with leaf color chart (LCC), chlorophyll meter (SPAD meter), and GreenSeeker optical sensor were used to predict grain yield. A power function based on the Normalized Difference Vegetative Index (NDVI) measured with GreenSeeker optical sensor at V9 growth stage (9th leaf with fully exposed collar) presented higher values of coefficient of determination and explained 61% of the variability in spring maize grain yield, whereas NDVI measured at early and late growth stages were not reliable for the purpose. The spectral properties recorded with the SPAD meter and LCC rendered better grain yield estimates at VT growth stage (tasseling) and were respectively able to explain 75 and 76% variability in grain yield. The developed models were validated on an independent data set from another field experiment on spring maize. The normalized root mean square error (NRMSE) was <10% for LCC and SPAD at all the growth stages and at V9 growth stage for NDVI. The LCC, SPAD, and NDVI values adjusted with cumulative growing degree day were not helpful to improve NRMSE.

Factors influencing adoption of climate resilient paddy production practices in Andhra Pradesh, India

This paper examined the factors affecting adoption of climate resilient practices in paddy production using data collected from farmers of Andhra Pradesh during the year 2019. Majority of the farmers reported increase in temperature, unpredictability in weather, reduced duration of winter, uneven and irregular rainfall as the climatic change events. The practices being followed by farmers which fit the adaptation criteria were timely sowing and weeding, proper spacing and formation of soil bunds. The climate resilient practices most preferred and prioritized by paddy farmers were direct sown rice, drought tolerant varieties, weather forecast services, integrated nutrient management, growing of green manure crops followed by crop diversification, crop insurance, system of rice intensification (SRI) and use of leaf color chart. Market demand, assured irrigation, land fertility, and availability of finance were the major factors governing the decision to grow crops. The educational level of farmers exhibited significant and positive correlation with practices namely SRI, use of leaf color chart, crop insurance, weather forecast services and drought tolerant varieties. Analysis of factors influencing the adoption of climate resilient paddy production practices would help to promote and disseminate these practices to increase the adoption rate and also aid in the framing of appropriate policies.

Classification Of Rice Leaf Color Into Leaf Color Chart Using LAB Color Space

Leaf Color Chart (LCC) is a measurement tool that can be used to measure the color intensity of rice leaves. The function of these measurements is to find out how many doses of fertilizer are needed by rice plants. However, readings made by human vision have a high level of subjectivity and risk of error. Therefore we need a method that can minimize errors and the level of subjectivity. One method that can be done is to classify the green color of rice leaves using LAB color space. Rice leaf image taken using a smartphone device is then extracted in RGB format. The color is then converted to LAB color space and then compared to the standard green color in the LCC. The comparison results are then used to classify the colors. The testing results show that the method has the value of accuracy, average precision, and average recall of 54.74%, 54.44%, and 51.16% respectively. Therefore the method can only classify correctly half of the data testing.

Consumers’ Perceptions Regarding Adoption of Leaf Color Chart for Resource Management

Aim: This study attempts to understand the consumers’ perceptions regarding adoption of leaf color chart for resource management in agriculture and how the adoption level varies among various age groups, landholding sizes and income groups. Study Design: An exploratory research study was undertaken and consumer responses were recorded using a well-structured, disguised questionnaire. Methodology: From eight villages of two major districts of Punjab state, a total of 150 farmers were selected as respondents. These respondents selected belonged to different age groups, landholding sizes and income groups in order to represent the whole population effectively. The data collected through questionnaires were analyzed using appropriate statistical tools. Major Findings: It was found that most of the respondents were aware of ill-effects of excessive usage of fertilizers, but were still practicing fertilizer inputs based on their personal experiences instead of using any technical advice or techniques established. Young farmers, farmers belonging to small and semi-medium landholding sizes and medium income groups were observed to have higher adoption level as compared to others. Conclusion: Approximately 70 percent of the respondents were not using leaf color chart, even though 73 percent of the total respondents were aware about the technology. Age, landholding sizes and income groups had significant effect on the perception of respondents towards adoption of leaf color chart for resource management.

Assessing Productivity and Profitability of Hybrid Rice With Site Specific Nutrient Management in Mid Western Terai, Nepal

Rice (Oryza sativa L.) is a dominant staple food crop of Nepal which production and productivity is significantly declining compared to several years due to inappropriate nutrient management practices. A field experiment was conducted at Lamahi, Dang to evaluate the performance of hybrid rice(US-305) under rain fed condition with five precision nutrient management practices [Viz: Nutrient Expert® -Rice (NE) recommendation; Leaf Color Chart (LCC) N and Nutrient Expert (P and K); Nutrient Expert (N) and Farmers Fertilizer Practices (P and K); Farmers Fertilizer Practices (FFP) and Government Recommendation (GR)] replicated four times in RCBD design during June to October, 2018. The experimental finding showed that SSNM based Nutrient Expert® -Rice (NE) recommendation gave higher grain yield (6.36 ton ha-1) and straw yield (12.62 ton ha-1) which leads to highest gross return (NRs 242,498) and B: C ratio(3.08). Between the treatments Nutrient Expert® -Rice (NE) recommendation was excellent to growth parameters like plant height, crop growth rate, relative growth rate and leaf area index over FFP. Further, Nutrient Expert® -Rice (NE) recommendation gave significantly higher effective tiller m-2(354.50), panicle length (26.31), panicle weight (81.50), filled grain (390) and fertility (87.56%) over FFP. Nutrient Expert® -Rice (NE) recommendation has increased the grain yield by 23.97% with yield difference of 1.23 ton ha-1 and straw yield by 39.44 % with yield difference of 3.57 ton ha-1 in comparison with FFP. Hence the experiment concluded that site specific nutrient management recommendation that accounts Nutrient Expert® -Rice and leaf color chart could be the practical decision tool for making authentic fertilizer recommendation.

Klasifikasi Citra Warna Daun Padi Menggunakan Metode Histogram of S-RGB dan Fuzzy Logic Berbasis Android

The level of greenish leaves of rice plants is one indicator to analyze the nutrient needs of the rice plant nitrogen required. In the process, one recommended way to determine nitrogen needs for the rice plant is the use Leaf Color Chart (LCC). Given the need for efficiency of time and energy, and to avoid the perception of the color differences are observed, it is important to do the development of a system to facilitate the farmers in determining the nitrogen requirements for rice.This research aims to develop an Android-based system to determine nitrogen needs for the rice crop through image processing concept. The method used is of s-RGB Histograms and Fuzzy Logic. Method of s-RGB Histogram function to extract the characteristic color of rice leaves, while Fuzzy Logic is used to classify images based on 4 levels of rice leaf color on the LCC also to determine the dose of nitrogen necessary for the needs of rice plants.Tests carried out using Samsung's smartphone brands with a capacity of 8 MP camera. The test results and evaluation system using the Confusion Matrix for Multiple Classes showed that the accuracy of the system provide the requested information is considered good enough, that is 88.19%. The success of the system to find the information back to the recall level of 88.25%. Degree of proximity between the predicted value of the system to the actual value of 88.75%, and the level of specificity obtained at 62.12%. While the system achieved computational time average of 10:14 seconds. Keywords- Histogram of s-RGB, Fuzzy Logic, Leaf Color Chart, Confusion Matrix for Multiple Classes

UPAYA EFISIENSI DAN PENINGKATAN KETERSEDIAAN NITROGEN DALAM TANAH SERTA SERAPAN NITROGEN PADA TANAMAN PADI SAWAH ( Oryza sativa L.)

Nutrients or nutrients are important factors for plant growth which can be likened to food substances for plants. One of the factors that support plants to grow and produce optimally is the availability of nutrients in sufficient quantities in the soil. The elements N, P and K, have a very important role in plant growth and production. The purpose of the preparation of the paper is to provide information about efforts to improve efficiency and availability of nitrogen in the soil and nitrogen uptake in lowland rice plants (Oryza sativa L.) Nitrogen has a role as a constituent of enzymes which plays a large role in plant metabolism but is relatively not available to plants. The efficiency of the use of Nitrogen (N) fertilizer in lowland rice can be maximized by way of 1) timely fertilization, 2) planting superior varieties that are responsive to the administration of Nitrogen (N), 3) improving cultivation techniques, 4) regulating the timing of Nitrogen fertilizer (N ) the right during the growing season with Leaf Color Chart (LCC) or Leaf Color Chart (BWD) and 5) NPK fertilization simultaneously. Efforts to increase the availability of nitrogen in the soil and uptake in wetland rice can be done by adding high-quality organic matter, the use of Azotobacter isolates as biological fertilizers to reduce the decrease in soil health due to the input of synthetic chemicals.