Food is considered as a basic need of human being which can be satisfied through farming. Agriculture not only fulfills humans’ basic needs, but also considered as source of employment worldwide. Agriculture is considered as a backbone of economy and source of employment in the developing countries like India. Agriculture contributes 15.4% in the GDP of India. Agriculture activities are broadly categorized into three major areas: pre-harvesting, harvesting and post harvesting. Advancement in area of machine learning has helped improving gains in agriculture. Machine learning is the current technology which is benefiting farmers to minimize the losses in the farming by providing rich recommendations and insights about the crops. This paper presents an extensive survey of latest machine learning application in agriculture to alleviate the problems in the three areas of pre-harvesting, harvesting and post-harvesting. Application of machine learning in agriculture allows more efficient and precise farming with less human manpower with high quality production.

Machine learning in agriculture domain: A state-of-art survey

When integrating the Internet of Things (IoT) with Unmanned Aerial Vehicles (UAVs) occurred, tens of applications including smart agriculture have emerged to offer innovative solutions to modernize the farming sector. This paper aims to present a low-cost platform for comprehensive environmental parameter monitoring using flying IoT. This platform is deployed and tested in a real scenario on a farm in Medenine, Tunisia, in the period of March 2020 to March 2021. The experimental work fulfills the requirements of automated and real-time monitoring of the environmental parameters using both under- and aboveground sensors. These IoT sensors are on a farm collecting vast amounts of environmental data, where it is sent to ground gateways every 1 h, after which the obtained data is collected and transmitted by a drone to the cloud for storage and analysis every 12 h. This low-cost platform can help farmers, governmental, or manufacturers to predict environmental data over the geographically large farm field, which leads to enhancement in crop productivity and farm management in a cost-effective, and timely manner. Obtained experimental results infer that automated and human-made sets of actions can be applied and/or suggested, due to the innovative integration between IoT sensors with the drone. These smart actions help in precision agriculture, which, in turn, intensely boost crop productivity, saving natural resources.

https://www.mdpi.com/2071-1050/13/11/5908/pdf

A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs

An early and reliable estimation of crop yield is essential in quantitative and financial evaluation at the field level for determining strategic plans in agricultural commodities for import-export policies and doubling farmer’s incomes. Crop yield predictions are carried out to estimate higher crop yield through the use of machine learning algorithms which are one of the challenging issues in the agricultural sector. Due to this developing significance of crop yield prediction, this article provides an exhaustive review on the use of machine learning algorithms to predict crop yield with special emphasis on palm oil yield prediction. Initially, the current status of palm oil yield around the world is presented, along with a brief discussion on the overview of widely used features and prediction algorithms. Then, the critical evaluation of the state-of-the-art machine learning-based crop yield prediction, machine learning application in the palm oil industry and comparative analysis of related studies are presented. Consequently, a detailed study of the advantages and difficulties related to machine learning-based crop yield prediction and proper identification of current and future challenges to the agricultural industry is presented. The potential solutions are additionally prescribed in order to alleviate existing problems in crop yield prediction. Since one of the major objectives of this study is to explore the future perspectives of machine learning-based palm oil yield prediction, the areas including application of remote sensing, plant’s growth and disease recognition, mapping and tree counting, optimum features and algorithms have been broadly discussed. Finally, a prospective architecture of machine learning-based palm oil yield prediction has been proposed based on the critical evaluation of existing related studies. This technology will fulfill its promise by performing new research challenges in the analysis of crop yield prediction and the development of an extremely effective model for the prediction of palm oil yields with the most minimal computational difficulty.

/pdf/a-comprehensive-review-of-crop-yield-prediction-using-1csb71j5yj.pdf

A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

IMPACT OF MACHINE learning ON Management, healthcare AND AGRICULTURE

Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.

/pdf/machine-learning-applications-for-precision-agriculture-a-595xsgydlw.pdf

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

Optimal allocation of test cases to respective operations/functions is often desired and considered as a necessary task in software testing in order to provide efficient testing before its operational use. This objective is achieved in later phase of software development life cycle. Since our major concern is to improve software reliability, therefore to achieve high reliability of program under test, it would be required to consider certain functional requirement that users are going to face during its operational use and an educated guess will be made on the number of test cases that are going to be allocated to the functions. We have used three input parameters: occurrence probability, criticality and data interactivity of functions and combined this using fuzzy inference system to derive weight. Depending upon the priority of each function, they will receive test cases. The application in which the software runs and the relationship between the functions do not take into account conventional approaches to assign test cases. That is intuitively inaccurate. In this paper, the distribution of test cases with regard to software application, its use and the relationship using the fuzzy inference system model is suggested to solve this issue. The results show that taking advantage of important factors to extract the priority of feature using fuzzy inference system generally enables operational testing to outperform the achievement of a given reliability goal under the same testing budget.

Priority-wise Test Case Allocation using Fuzzy Logic

This paper defines a reusable framework called Imperative Code Execution Machine (ICEM) which enables developers to implement application logic using code stored in the form of plain text and evade the necessity of its compilation, even at runtime. ICEM aims to reduce amount of apex code, facilitate rapid development while adopting evolving business requirements, maintenance, testing and eventually shrink Time to Market (TTM) for a given application or functionality over Salesforce platform. The framework has potential to mitigate the challenges that arise with the practice of agile for development of applications with convoluted business logic by shifting the focus from “developing” a solution to “designing” a solution.

Introduction to Imperative Code Execution Machine - a framework for sustainable Salesforce application development

• Ship’s hydrodynamic performance can be assessed using the data from ship-in-service • Data can be recorded onboard the ship, obtained via AIS or as noon reports • Gathered data needs to be cleaned and processed for performance analysis • A standardized data processing framework for preparing the data is developed • Data processing framework can be easily casted for processing any similar datasets

Data Processing Framework for Ship Performance Analysis

In the software development process, the main concern of the developer is to develop a quality product and optimizing time and cost. Maintenance of the software is considered a costly activity. Regression testing is performed to test the modified software in the maintenance phase. In order to minimize the total time and cost, we can arrange test cases according to the different requirements proposed in the starting phase of the development. We can prioritize the test cases in such a manner that the higher priority test cases will execute before the lower priority test cases. This paper presents an approach to prioritize regression test cases based on requirements. Firstly, we have selected some requirement factors; based on these requirements, the weight for each specific requirement will be calculated. Further, we will map the test cases to the requirements and calculate a parameter based on the mapping, known as Test Case Weight (TCW) to the set of requirements. The higher TCW will be executed first. We have compared our proposed work with other prioritization techniques and found better results.

Prateek Gupta

Papers

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

Priority-wise Test Case Allocation using Fuzzy Logic

Introduction to Imperative Code Execution Machine - a framework for sustainable Salesforce application development

Data Processing Framework for Ship Performance Analysis

Test Case Prioritization Based on Requirement