Soil Chemical Analysis

The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords’ combinations of “machine learning” along with “crop management”, “water management”, “soil management”, and “livestock management”, and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018–2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

Machine Learning in Agriculture: A Comprehensive Updated Review.

This paper proposes AgriTalk, an inexpensive Internet of Things (IoT) platform for precision farming of soil cultivation. We conduct experiments on turmeric cultivation, which indicates that the turmeric quality is significantly enhanced through AgriTalk. Specifically, the curcumin concentration is up to 4500–5500 mg/100 g, which is five times more than existing products. We demonstrate how to intuitively configure the connections between the sensors and the actuators with the desired farming intelligence, and to effectively maintain AgriTalk for precision farming. We conduct measurement, analytic analysis, and simulation experiments to investigate the IoT message delays of AgriTalk. This paper indicates that the delays for automatic control and automatic-manual control switching with long distances (more than 30 km) are very short (less than 0.2 s) and AgriTalk can easily respond to quick and dynamic change of the field environment conditions in soil cultivation.

AgriTalk: IoT for Precision Soil Farming of Turmeric Cultivation

A novel approach for efficient crop yield prediction

Background Camellia oil is obtained from the camellia seed with various cultivated species (Camellia. oleifera (C. oleifera), C. meiocarpa, C. vietnamensis, C. yuhsienensis, C. chekiangoleosa, C. semiserrata, C. reticulata, C. gigantocarpa, C. octopetala, C. semiserrata var. abliflora etc.), by widely used cold press extraction. As the earliest specie with high oil yield (40–60%) in China, C. oleifera, has become the most commonly available seed for camellia oil manufacturing. Because of its high nutritional and economic value, camellia oil is frequently adulterated with other cheap oils. Additionally, its quality is also susceptible to different species or regions, and various extraction technologies. These factors result in the incorrect labeling of camellia oil, and destabilize the local camellia oil market economies. Therefore, a rapid and accurate method should be prerequisite to authenticate camellia oil. Scope and approach The officially recommended methods are tedious, and destructive to detect camellia oil adulteration. Therefore, various rapid, precise, and non-destructive techniques should be developed for camellia oil authentication. This present review provides a critical overview of these existing analytical methods in the past few years. Key findings and conclusion The mass-chromatographic, spectroscopy techniques, and other techniques including electronic noses (e-noses), isotope-ratio mass spectrometry (IRMS), differential scanning calorimetry (DSC), ion mobility spectrometry (IMS) and DNA, have been used for camellia oil authentication. Compared with the traditional chromatographic methods, infrared spectroscopy (IR), Fourier transformed (FT)-Raman, nuclear magnetic resonance (NMR), and fluorescence spectroscopy, combined with chemometrics, respectively, are efficient alternative analytical techniques for camellia oil quality control.

Camellia oil authentication: A comparative analysis and recent analytical techniques developed for its assessment. A review

Application of Artificial Neural Network modeling for optimization and prediction of essential oil yield in turmeric (Curcuma longa L.)

Agroclimatic zone based metabolic profiling of turmeric (Curcuma Longa L.) for phytochemical yield optimization

The drug yielding potential of turmeric (Curcuma longa L.) is largely due to the presence of phyto-constituent ‘curcumin’. Curcumin has been found to possess a myriad of therapeutic activities ranging from anti-inflammatory to neuroprotective. Lack of requisite high curcumin containing genotypes and variation in the curcumin content of turmeric at different agro climatic regions are the major stumbling blocks in commercial production of turmeric. Curcumin content of turmeric is greatly influenced by environmental factors. Hence, a prediction model based on artificial neural network (ANN) was developed to map genome environment interaction basing on curcumin content, soli and climatic factors from different agroclimatic regions for prediction of maximum curcumin content at various sites to facilitate the selection of suitable region for commercial cultivation of turmeric. The ANN model was developed and tested using a data set of 119 generated by collecting samples from 8 different agroclimatic regions of Odisha. The curcumin content from these samples was measured that varied from 7.2% to 0.4%. The ANN model was trained with 11 parameters of soil and climatic factors as input and curcumin content as output. The results showed that feed-forward ANN model with 8 nodes (MLFN-8) was the most suitable one with R2 value of 0.91. Sensitivity analysis revealed that minimum relative humidity, altitude, soil nitrogen content and soil pH had greater effect on curcumin content. This ANN model has shown proven efficiency for predicting and optimizing the curcumin content at a specific site.

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Development of Prediction Model and Experimental Validation in Predicting the Curcumin Content of Turmeric (Curcuma longa L.).

Preparation of GPS and GIS based soil fertility maps for Khurda district, Odisha.

Soil fertility maps of Dhenkanal district, Odisha were prepared by using Global Positioning System (GPS) and Geographical Information System (GIS) tomake awareness among the farmers regarding use of balanced fertilization according to soil test based recommendation and integrated nutrient management for higher and sustainable crop production. Soils of different villages for each block were collected b y GPS instrument.

Antaryami Mishra

Papers

Application of Artificial Neural Network modeling for optimization and prediction of essential oil yield in turmeric (Curcuma longa L.)

Agroclimatic zone based metabolic profiling of turmeric (Curcuma Longa L.) for phytochemical yield optimization

Development of Prediction Model and Experimental Validation in Predicting the Curcumin Content of Turmeric (Curcuma longa L.).

Preparation of GPS and GIS based soil fertility maps for Khurda district, Odisha.

Soil Fertility Maps Preparation Using GPS and GIS in Dhenkanal District, Odisha, India