New strategies for row-crop management based on cost-effective remote sensors

Agricultural technology can be an excellent antidote to resource scarcity. Its growth has led to the extensive study of spatial and temporal in-field variability. The challenge of accurate management has been addressed in recent years through the use of accurate high-cost measurement instruments by researchers. However, low rates of technological adoption by farmers motivate the development of alternative technologies based on affordable sensors, in order to improve the sustainability of agricultural biosystems. This doctoral thesis has as main objective the development and evaluation of systems based on affordable sensors, in order to address two of the main aspects affecting the producers: the need of an accurate plant water status characterization to perform a proper irrigation management and the precise weed control. To address the first objective, two data acquisition methodologies based on aerial platforms have been developed, seeking to compare the use of infrared thermometry and thermal imaging to determine the water status of two most relevant row-crops in the region, sugar beet and super high-density olive orchards. From the data obtained, the use of an airborne low-cost infrared sensor to determine the canopy temperature has been validated. Also the reliability of sugar beet canopy temperature as an indicator its of water status has been confirmed. The empirical development of the Crop Water Stress Index (CWSI) has also been carried out from aerial thermal imaging combined with infrared temperature sensors and ground measurements of factors such as water potential or stomatal conductance, validating its usefulness as an indicator of water status in super high-density olive orchards. To contribute to the development of precise weed control systems, a system for detecting tomato plants and measuring the space between them has been developed, aiming to perform intra-row treatments in a localized and precise way. To this end, low cost optical sensors have been used and compared with a commercial LiDAR laser scanner. Correct detection results close to 95% show that the implementation of these sensors can lead to promising advances in the automation of weed control. The micro-level field data collected from the evaluated affordable sensors can help farmers to target operations precisely before plant stress sets in or weeds infestation occurs, paving the path to increase the adoption of Precision Agriculture techniques

Cloud based Smart Irrigation for Agricultural Area of Pakistan

A beneficial product of Smart Irrigation for Agricultural Area of Pakistan has been presented in this paper. Pakistan stands in need of a participatory solution that is efficiently workable, sustainable, and profitable, to develop the way for the agricultural sector by improving crop productivity with minimum water loss. The goal of this project is to introduce Cloud support to the Smart Irrigation System for Agricultural Area of Pakistan. To achieve this objective Wireless Sensor Network (WSN) is used to determine how much water to apply and when to irrigate. The system is divided into four main modules, i.e. Sensor node, Coordinator node, Server Module and Web Application. On the basis of acquired parameters from the WSN, the software application is programmed to take intelligent decisions increase the efficiency of the agricultural system

Intelligent Agricultural Greenhouse Control System Based on Internet of Things and Machine Learning

This study endeavors to conceptualize and execute a sophisticated agricultural greenhouse control system grounded in the amalgamation of the Internet of Things (IoT) and machine learning. Through meticulous monitoring of intrinsic environmental parameters within the greenhouse and the integration of machine learning algorithms, the conditions within the greenhouse are aptly modulated. The envisaged outcome is an enhancement in crop growth efficiency and yield, accompanied by a reduction in resource wastage. In the backdrop of escalating global population figures and the escalating exigencies of climate change, agriculture confronts unprecedented challenges. Conventional agricultural paradigms have proven inadequate in addressing the imperatives of food safety and production efficiency. Against this backdrop, greenhouse agriculture emerges as a viable solution, proffering a controlled milieu for crop cultivation to augment yields, refine quality, and diminish reliance on natural resources [b1]. Nevertheless, greenhouse agriculture contends with a gamut of challenges. Traditional greenhouse management strategies, often grounded in experiential knowledge and predefined rules, lack targeted personalized regulation, thereby resulting in resource inefficiencies. The exigencies of real-time monitoring and precise control of the greenhouse's internal environment gain paramount importance with the burgeoning scale of agriculture. To redress this challenge, the study introduces IoT technology and machine learning algorithms into greenhouse agriculture, aspiring to institute an intelligent agricultural greenhouse control system conducive to augmenting the efficiency and sustainability of agricultural production

LITERATURE REVIEW IOT SOFTWARE ARCHITECTURE ON AGRICULTURE

Context – Internet of Things (IoT) interrelates computing devices, machines, animals, or people and things that use the power of internet usage to utilize data to be much more usable. Food is one of the mandatory human needs to survive, and most of it is produced by agriculture. Using IoT in agriculture needs appropriate software architecture that plays a prominent role in optimizing the gain. Objective and Method – Implementing a solution in a specific field requires a particular condition that belongs to it. The objectives of this research study are to classify the state of the art IoT solution in the software architecture domain perspective. We have used the Evidence- Based Software Engineering (EBSE) and have 24 selected existing studies related to software architecture and IoT solutions to map to the software architecture needed on IoT solutions in agriculture. Result and Implications – The results of this study are the classification of various IoT software architecture solutions in agriculture. The highlighted field, especially in the areas of cloud, big data, integration, and artificial intelligence/machine learning. We mapped the agriculture taxonomy classification with IoT software architecture. For future work, we recommend enhancing the classification and mapping field to the utilization of drones in agriculture since drones can reach a vast area that is very fit for fertilizing, spraying, or even capturing crop images with live cameras to identify leaf disease

Efficient Solar-Powered IoT Drip Irrigation for Tomato Yield and Quality: An Evaluation of the Effects of Irrigation and Fertilizer Frequency

The optimal management of irrigation and fertilization is crucial for maximizing the yield and quality of tomatoes grown in greenhouses. To address this challenge, this study aimed to develop and implement a solar-powered Internet of Things (IoT) based drip irrigation system for tomato cultivation in plastic roof net houses. Additionally, the study evaluated the effects of water and fertilizer frequency on tomato yield and quality. The experiment was designed with 2 irrigation frequencies (1 time in a day and 1 time in 2 days) and 3 fertilizer frequencies (1 time in 2, 4, and 6 days), with 4 replicates of the tomato variety CH154. The results showed that the solar-powered IoT-based drip irrigation system was efficient, precise in water and fertilizer control, and inexpensive to install and maintain. This allows for real-time monitoring of water flow rate, flow sensor status, treatment status, and electrical parameters on the Node-Red dashboard. Irrigation frequency had a significant impact (p < 0.05) on fruit number, weight, and length per plant, with 1-day irrigation resulting in a higher yield than 2-day irrigation. No significant interaction effect was found between irrigation and fertilizer frequency on tomato yield or quality. In conclusion, the solar-powered IoT-based drip irrigation system demonstrated precise control over water and fertilizer, proving its efficiency and cost-effectiveness. Real-time monitoring capabilities and the observed impact of irrigation frequency underscore its potential for enhancing tomato cultivation in greenhouses, offering a valuable contribution to sustainable and technology-driven agricultural practices

Precision Agriculture for Crop and Livestock Farming—Brief Review

In the last few decades, agriculture has played an important role in the worldwide economy. The need to produce more food for a rapidly growing population is creating pressure on crop and animal production and a negative impact to the environment. On the other hand, smart farming technologies are becoming increasingly common in modern agriculture to assist in optimizing agricultural and livestock production and minimizing the wastes and costs. Precision agriculture (PA) is a technology-enabled, data-driven approach to farming management that observes, measures, and analyzes the needs of individual fields and crops. Precision livestock farming (PLF), relying on the automatic monitoring of individual animals, is used for animal growth, milk production, and the detection of diseases as well as to monitor animal behavior and their physical environment, among others. This study aims to briefly review recent scientific and technological trends in PA and their application in crop and livestock farming, serving as a simple research guide for the researcher and farmer in the application of technology to agriculture. The development and operation of PA applications involve several steps and techniques that need to be investigated further to make the developed systems accurate and implementable in commercial environments.info:eu-repo/semantics/publishedVersio

Ag-IoT for crop and environment monitoring: Past, present, and future

CONTEXT: Automated monitoring of the soil-plant-atmospheric continuum at a high spatiotemporal resolution is a key to transform the labor-intensive, experience-based decision making to an automatic, data-driven approach in agricultural production. Growers could make better management decisions by leveraging the real-time field data while researchers could utilize these data to answer key scientific questions. Traditionally, data collection in agricultural fields, which largely relies on human labor, can only generate limited numbers of data points with low resolution and accuracy. During the last two decades, crop monitoring has drastically evolved with the advancement of modern sensing technologies. Most importantly, the introduction of IoT (Internet of Things) into crop, soil, and microclimate sensing has transformed crop monitoring into a quantitative and data-driven work from a qualitative and experience-based task. OBJECTIVE: Ag-IoT systems enable a data pipeline for modern agriculture that includes data collection, transmission, storage, visualization, analysis, and decision-making. This review serves as a technical guide for Ag-IoT system design and development for crop, soil, and microclimate monitoring. METHODS: It highlighted Ag-IoT platforms presented in 115 academic publications between 2011 and 2021 worldwide. These publications were analyzed based on the types of sensors and actuators used, main control boards, types of farming, crops observed, communication technologies and protocols, power supplies, and energy storage used in Ag-IoT platforms

Smart Pumping System using Energy Efficiency Control Algorithm

Water is one of the most important resources for sustaining life on earth. It is essential for daily activities like cooking, cleaning, and bathing. In residential areas, water supply systems are usually managed by Municipal Corporations or other governing bodies. This project mainly focuses on the IoT based solution for automation of the water tank filling system in our residential area. In this project two Esp8266 MCU connected to the same cloud channel are used, one will be at the Water Source side and other will be at Tank side. The Ultrasonic Sensor connected to the Esp8266 senses the water level in the tank, if the Water level is low the Esp8266 will sent the Motor ON signal to the ThingSpeak IoT Cloud and the Esp8266 at the river or dam side is also connected to the same cloud channel will read that signal and turn the ‘Motor ON’. If the tank is full the Esp8266 sends the ‘Motor OFF signal’ over the cloud and the other microcontroller at the river/dam side will turn OFF the motor. A web interface displaying graphs and indicators is also provided for the user to study and analyze the data from the system

Soil Moisture Workshop

The Soil Moisture Workshop was held at the United States Department of Agriculture National Agricultural Library in Beltsville, Maryland on January 17-19, 1978. The objectives of the Workshop were to evaluate the state of the art of remote sensing of soil moisture; examine the needs of potential users; and make recommendations concerning the future of soil moisture research and development. To accomplish these objectives, small working groups were organized in advance of the Workshop to prepare position papers. These papers served as the basis for this report