Efficient IoT-Based Control for a Smart Subsurface Irrigation System to Enhance Irrigation Management of Date Palm

Physiological response, phytochemicals, antioxidant, and enzymatic activity of date palm (Phoenix dactylifera L.) cultivated under different storage time, harvesting Stages, and temperatures

The quality of date palm is highly influenced by postharvest techniques, storage, and processing effects. Fruits stored at room temperature result in dehydration, whereas higher temperatures accelerate the enzymatic browning of fruit. This study aimed to enhance postharvest quality of date palms through improved harvesting and storage techniques. The fruits of date palm (Phoenix dactylifera L. cv. Dhakki) were harvested at khalal (mature, firm), rutab (fully ripe), or tamar (dry) stages and stored at different temperatures (12, 18, or 24 °C) for 0, 15, 30, or 45 days. The analysis of the data showed that the studied attributes significantly different at various ripening stages and storage temperatures. The fruits harvested at Khalal stage proved to be the best in retaining moisture content (23.16%), total soluble solids (20.36 oBrix), fruit juice pH (4.97), ascorbic acid (24.65 mg 100 g-1), non-reducing sugars (26.84%), percent acidity (0.39%), antioxidant activity (211.0 mg 100 g-1), total phenolic (40.07 mg100g-1), flavonoids (45.8 mg 100 g-1), tannin (70.7 mg100g-1), catalase (1.82 U g-1), peroxidase (1.4 U g-1), soluble protein (38.2 mg kg-1), brightness (29.9), chroma (16.4), hue angle (34.9), color (16.8), and with minimum weight loss (8.48%) as compared to fruit harvested at Rutab and Tamar stage. Regarding the means for storage temperature, the fruits stored at 12 ± 3 °C retained the highest moisture content (23.2%), total soluble solids (13.5 oBrix), fruit juice pH (5.42), percent acidity (0.29%), ascorbic acid (24.4 mg100g-1), reducing sugars (31.1%), non-reducing sugars (26.5%), antioxidant activity (214.6 mg100g-1), total phenolic (41.6 mg100 g-1), flavonoids (44.7 mg100 g-1), tannin (71.7 mg 100 g-1), catalase (1.56 U g-1), peroxidase (1.21 U g-1), soluble protein (31.8 mg kg-1), brightness (28.8), chroma (15.3), hue angle (29.6), color (16.2),with minimum weight loss (9.91%). It was concluded that for quality fruit production of date palm cv. Dhakki could be harvested at Khalal stage and stored at a temperature of 12 ± 3 °C.

Towards making the fields talks: A real-time cloud enabled IoT crop management platform for smart agriculture

Agriculture is the primary and oldest industry in the world and has been transformed over the centuries from the prehistoric era to the technology-driven 21^st century, where people are always solving complex problems with the aid of technology. With the power of Information and Communication Technologies (ICTs), the world has become a global village, where every digital object that prevails in the world is connected to each other with the Internet of Things (IoT). The fast proliferation of IoT-based technology has revolutionized practically every sector, including agriculture, shifting the industry from statistical to quantitative techniques. Such profound transformations are reshaping traditional agricultural practices and generating new possibilities in the face of various challenges. With the opportunities created, farmers are now able to monitor the condition of crops in real time. With the automated IoT solutions, farmers can automate tasks in the farmland, as these solutions are capable of making precise decisions based on underlying challenges and executing actions to overcome such difficulties, alerting farmers in real-time, eventually leading to increased productivity and higher harvest. In this context, we present a cloud-enabled low-cost sensorized IoT platform for real-time monitoring and automating tasks dealing with a tomato plantation in an indoor environment, highlighting the necessity of smart agriculture. We anticipate that the findings of this study will serve as vital guides in developing and promoting smart agriculture solutions aimed at improving productivity and quality while also enabling the transition to a sustainable environment.

An intelligent approach to improve date palm crop yield and water productivity under different irrigation and climate scenarios

Drought, rising demand for water, declining water resources, and mismanagement have put society at serious risk. Therefore, it is essential to provide appropriate solutions to increase water productivity (WP). As an element of research, this study presents a hybrid machine learning approach and investigates its potential for estimating date palm crop yield and WP under different levels of subsurface drip irrigation (SDI). The amount of applied water in the SDI system was compared at three levels of 125% (T1), 100% (T2), and 75% (T3) of water requirement. The proposed ACVO-ANFIS approach is composed of an anti-coronavirus optimization algorithm (ACVO) and an adaptive neuro-fuzzy inference system (ANFIS). Since the effect of irrigation factors, climate, and crop characteristics are not equal in estimating the WP and yield, the importance of these factors should be measured in the estimation phase. To fulfill this aim, ACVO-ANFIS employed eight different feature combination models based on irrigation factors, climate, and crop characteristics. The proposed approach was evaluated on a benchmark dataset that contains information about the groves of Behbahan agricultural research station located in southeast Khuzestan, Iran. The results explained that the treatment T3 advanced data palm crop yield by 3.91 and 1.31%, and WP by 35.50 and 20.40 kg/m³, corresponding to T1 and T2 treatments, respectively. The amount of applied water in treatment T3 was 7528.80 m³/ha, which suggests a decrease of 5019.20 and 2509.6 m³/ha of applied water compared to the T1 and T2 treatments. The modeling results of the ACVO-ANFIS approach using a model with factors of crop variety, irrigation (75% water requirement of SDI system), and effective rainfall achieved RMSE = 0.005, δ = 0.603, and AICC = 183.25. The results confirmed that the ACVO-ANFIS outperformed its counterparts in terms of performance criteria.

IoT-Based Monitoring System Applied to Aeroponics Greenhouse

The inclusion of the Internet of Things (IoT) in greenhouses has become a fundamental tool for improving cultivation systems, offering information relevant to the greenhouse manager for decision making in search of optimum yield. This article presents a monitoring system applied to an aeroponic greenhouse based on an IoT architecture that provides user information on the status of the climatic variables and the appearance of the crop in addition to managing the irrigation timing and the frequency of visual inspection using an application developed for Android mobile devices called Aeroponics Monitor. The proposed IoT architecture consists of four layers: a device layer, fog layer, cloud layer and application layer. Once the information about the monitored variables is obtained by the sensors of the device layer, the fog layer processes it and transfers it to the Thingspeak and Firebase servers. In the cloud layer, Thingspeak analyzes the information from the variables monitored in the greenhouse through its IoT analytic tools to generate historical data and visualizations of their behavior, as well as an analysis of the system’s operating status. Firebase, on the other hand, is used as a database to store the results of the processing of the images taken in the fog layer for the supervision of the leaves and roots. The results of the analysis of the information of the monitored variables and of the processing of the images are presented in the developed app, with the objective of visualizing the state of the crop and to know the function of the monitoring system in the event of a possible lack of electricity or a service line failure in the fog layer and to avoid the loss of information. With the information about the temperature of the plant leaf and the relative humidity inside the greenhouse, the vapor pressure deficit (VPD) in the cloud layer is calculated; the VPD values are available on the Thingspeak server and in the developed app. Additionally, an analysis of the VPD is presented that demonstrates a water deficiency from the transplanting of the seedling to the cultivation chamber. The IoT architecture presented in this paper represents a potential tool for the study of aeroponic farming systems through IoT-assisted monitoring.

An idea on Smart Farming: IoT monitoring of water production from dihydrogen combustion

Smart Farming is a concept developing rapidly and gaining momentum. The management of livestock and farm products is done in an automated way thanks to IoT technology. The large field of data at hand offers the possibility of analysis for a better understanding of issues and more efficient decision-making. The management of water consumption is one of the most relevent Smart Farming use cases. In the event of drought, the pressure on water resources becomes increasingly strong. What if we produced water then? The idea of not worrying about the consequence of drought on agricultural production would be interesting.

One of the first experiences you learn in a chemistry class is that the combustion of dihydrogen produces water. However, it is necessary to follow this experience closely because of the risk of explosion. Dihydrogen can be produced by the gasification of (agricultural) biomass. Here, the technology takes over, by the means of a supervising IoT system. This system will manage the overall process from biomass production, then dihydrogen production (biomass-to-hydrogen), to water production (dihydrogen-to-water).

If the idea proves to be viable on a large scale, the result would be valuable in reducing the issue of water scarcity, in times of drought, in agricultural areas, and even in allowing energy autonomy on farms.

Assessing the Performance of Irrigation Systems in Large Scale Urban Parks: Application to the Case of Valdebebas, Madrid (Spain)

This paper presents a novel approach to assess spatial and temporal irrigation performance in urban parks and can assist park manager/operator decisions in irrigation management. First, irrigation needs are estimated by traditional irrigation scheduling and the irrigation zones with similar water needs that share the same electric valve (hydrozones) are identified. Then, irrigation performance is calculated using the relative water supply (RWS) indicator and mapped (GIS software). This approach can be adapted to various spatial and temporal scales. In this study, it was applied to the Valdebebas urban development VBB (Madrid) between the 2017 and 2019 irrigation seasons. The results for the VBB parks showed high spatio-temporal variation in irrigation performance among plant typologies within an irrigation season, which can be explained by the landscape coefficient KL variation across the parks. Likewise, this variation was also observed among the three evaluated seasons; explained among other factors by differences in irrigation management. For each hydrozone, the estimation of the NDVI index by Sentinel-2A satellite images in 2019 showed a threshold on irrigation performance. Thus, the remote sensing data together with the proposed approach can be a valuable tool for helping park managers/technicians adopt better decisions on irrigation practices.

Applications of Electromagnetic Induction and Electrical Resistivity Tomography for Digital Monitoring and Assessment of the Soil: A Case Study of Al-Ahsa Oasis, Saudi Arabia

Al-Ahsa Oasis is one of the main and oldest agricultural centers in Saudi Arabia and one of the largest natural oases in the world. It is the largest irrigated agricultural oasis worldwide. The cultivated area is about 12,000 hectares in the region of Al-Ahsa. Groundwater was considered the main water source, if not the only source, for the different purposes of Al-Ahsa Oasis. However, due to groundwater depletion from these aquifers, treated wastewater is currently used to meet the shortage of irrigation water at Al-Ahsa Oasis. Today, several problems affect the agricultural productivity at Al-Ahsa Oasis, and modern geophysical techniques could contribute effectively to solving these problems; such problems are increasing soil salinity and the existence of a hardpan layer in some parts of the Al-Ahsa oasis. The soil texture is considered a critical factor to be taken into consideration. Therefore, the objectives of this study were to evaluate the effectiveness of modern geophysical techniques to study the agricultural aspects of Al-Ahsa oasis. Ten profiles of 2-D electrical tomography using SuperSting R8/IP 8 channel multielectrode resistivity and IP imaging system with 112 electrodes at one-meter spacing were implemented. Moreover, twenty electrical conductivity profiles were measured of 2240 points using EM38-MK2 at vertical mode with transmitter–receiver coils of 0.5 and one meter spacing. The results indicated that the salinity variation along the measured profiles was changed vertically and laterally due to the changes in the soil type, texture, moisture content, sabkha, and the whole oasis. Analysis and interpretation of the interpreted resistively tomograms of the ten 2-D profiles indicate remarkably the different geoelectric units of different hydrogeological conditions and soil texture across the oasis. These results will be of good utility for farmers, development sectors, and research institutions, especially in agriculture development studies, at Al-Ahsa Oasis, to ensure the sustainability of agriculture in this important historical Oasis.

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.

The Combined Effects of Precision-Controlled Temperature and Relative Humidity on Artificial Ripening and Quality of Date Fruit

Due to climatic variation, in-situ date palm fruit ripening is significantly delayed, and some fruits (Biser) cannot become ripe naturally on the tree. Because of that issue, the vast quantity of produce is mere wasted. Few traditional methods are adopted to ripe these unripe fruits through open sun drying or solar tunnel dehydration techniques. However, these methods have minimal use due to ambient temperature and relative humidity (RH) instability. Therefore, the present study was designed to find a precise combination of temperature and RH to artificially ripe the unripe Biser fruits under controlled environment chambers. For that purpose, eighteen automated artificial ripening systems were developed. The Biser fruits (cv. Khalas) were placed immediately after harvesting in the treatment chambers of the systems with three set-point temperatures (45, 50, and 55 °C) and six set-point RH (30, 35, 40, 45, 50, and 55%) until ripening. The optimal treatment combination for artificial ripening of Biser fruits was 50 °C and 50% RH. This combination provided good fruit size, color, firmness, total soluble solids (TSS), pH, and sugars content. As a result, there was a reduction in fruit weight loss and had optimum fruit ripening time. On the other hand, low temperature and RH delayed the ripening process, deteriorated fruit quality, and caused more weight loss. Although the combination of the highest temperature and RH (55 °C and 55%) reduced ripening time, the fruits have higher weight loss and negative quality. Therefore, the artificial ripening of unripe date palm Biser fruits can be achieved using 50 °C temperature and 50% RH combination. These findings can be applied in the field using solar energy systems on a commercial scale to reduce the postharvest loss of date palm fruits.

Development of an Algorithm for an Automatic Determination of the Soil Field Capacity Using of a Portable Weighing Lysimeter

The challenge today is to optimize agriculture water consumption and minimize leaching of pollutants in agro-ecosystems in order to ensure a sustainable agriculture. The use of different technologies and the adoption of different irrigation strategies can facilitate efficient fertigation management. In this respect, the determination of soil field capacity point is of utmost importance. The use of a portable weighing lysimeter allows an accurate quantification of crop water consumption and water leaching, as well as the detection of soil field capacity point. In this work, a novel algorithm is developed to obtain the soil field capacity point, in order to give autonomy and objectivity to efficient irrigation management using a portable weighing lysimeter. The development was tested in field grown horticultural crops and proved to be useful for optimizing irrigation management.

Smart Control and Energy Efficiency in Irrigation Systems Using LoRaWAN

Irrigation installations in cities or agricultural operations use large amounts of water and electrical energy in their activity. Therefore, optimising these resources is essential nowadays. Wireless networks offer ideal support for such applications. The long-range wide-area network (LoRaWAN) used in this research offers a large coverage of up to 5 km, has low power consumption and does not need additional hardware such as repeaters or signal amplifiers. This research develops a control and monitoring system for irrigation systems. For this purpose, an irrigation algorithm is designed that uses rainfall probability data to regulate the irrigation of the installation. The algorithm is complemented by checking the sending and receiving of information in the LoRa network to reduce the loss of information packets. In addition, two temperature and humidity measurement devices for LoRaWAN (THMDLs) and an electrovalve control device for LoRaWAN (ECDLs) were developed. The hardware and software were also designed, and prototypes were built with the development of the electronic board. The wide coverage of the LoRaWAN allows the covering of small to large irrigation areas.