Horticulture 4.0 in Horticulture for Meeting Sustainable Farming

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This entry is adapted from the peer-reviewed paper 10.3390/app122412557

The United Nations emphasized a significant agenda on reducing hunger and protein malnutrition as well as micronutrient (vitamins and minerals) malnutrition, which is estimated to affect the health of up to two billion people. The UN also recognized this need through Sustainable Development Goals (SDG 2 and SDG 12) to end hunger and foster sustainable agriculture by enhancing the production and consumption of fruits and vegetables. Previous studies only stressed the various issues in horticulture with regard to industries, but they did not emphasize the centrality of Industry 4.0 technologies for confronting the diverse issues in horticulture, from production to marketing in the context of sustainability.

horticulture micronutrient Industry 4.0 SDGs IoT

1. Introduction

According to the Food and Agriculture Organization and World Health Organization State of Food Security and Nutrition in the World (SOFI) report,45 million children die from the deadliest form of malnutrition under the age of five ^[1]. A chronic deficiency of essential nutrients in their nutrition has also resulted in delayed development and growth in two billion children under the age of five. This indicates that there is a need to have more emphasis on overcoming food insecurity and malnutrition due to climate extremes and economic disruption. Even the SDGs (SDG 2 and SDG 12) of the UN emphasize eradicating hunger and enhancing food security with responsible consumption and production toward sustainability ^[2]^[3]. Healthy micronutrients for overcoming malnutrition can be achieved with sustainable farming of fruits and vegetables, i.e., horticulture ^[4]. India is currently the world’s second-largest producer of fruits and vegetables, trailing only China. Horticulture comprises fruits, root and tuber crops, mushrooms, vegetables, spices, aromatic plants, flowers, bamboo, coconut, cashew, and cocoa. Different strategies such as technology promotion, research, post-harvest management, and marketing are key for the growth of horticulture. Improving horticulture production, increasing farmer income, improving nutritional security, and improving productivity by using quality germplasm, planting material, and micro irrigation to save water are the key vision of India for the promotion of holistic horticulture growth.

Horticulture crops significantly contribute to the Indian economy by enhancing farm output, generating employment, and providing raw materials to various food-processing businesses ^[5]. The amount of land allotted for horticulture is minimal, but the demand for the production of horticulture is high. Therefore, meeting the demand with minimum resources is a bit challenging, as sustainable practices need to be adopted to meet a sustainable environment ^[6]. It has also been observed that the export rate of Asian countries such as India has increased in the past few years; however, there are a few challenges such as meeting quality standards set by international countries and payment for exports. The main challenge in fruits and vegetables is the short lifetime after plucking. Additionally, given that the majority of horticulture cultivation is processed in rural areas, it has been determined that horticulture has to be promoted in urban areas.

2. Overview of Horticulture and Industry 4.0

High temperatures have two main effects on crop production: they inhibit vegetative growth and have a detrimental effect on fruit production. Prolonged transpiration along with subjection to extreme temperatures limits vegetable crops that are susceptible to considerable transpiration deficits ^[7]^[8]. Another problem is that the maximum time for the fruit set is proportional to humidity levels. Extremely high temperatures can occur. The obvious limitation encountered by cold temperatures is the freezing of plant tissues. Rapidly pushing freezing temperatures at a phase of rapid growth might cause damage to a variety of plant tissues ^[9]^[10]. Some plants can adapt to cold temperatures provided there is sufficient time and the appropriate circumstances, whereas others cannot. Changes in regional precipitation patterns may cause an increase in drought conditions in many different parts of the world as atmospheric CO₂ levels to rise ^[11]. Although the current belief is that leaf photosynthetic responds to high CO₂ as well as soil-water deficiency, the connections between CO₂ enrichment and drought stress remain unexplored ^[12]. These are the few problems that have been addressed by previous studies in the area of horticulture.

Overview of Technologies and TheirFunctions

IoT is the primary technology that enables the provision of real-time information that is required for other technologies to implement their functions. IoT is an open network of intelligent devices that may self-organize; exchange information, data, and resources; and respond and act according to circumstances and environmental changes ^[13]. Fundamentally, IoT comprises the following layers in its architecture: perception layer, access layer, network layer, middleware layer, and application layer ^[14]. The perception layer is a key layer in which sensors, actuators, and identification technologies empower the transmission of real-time information and also enable control of things from remote locations through internet connectivity. The network layer empowers the transmission of packets of data with the assistance of communication protocols that are embedded in the architecture. Wireless fidelity (Wi-Fi), Bluetooth, and Zigbee are widely used communication protocols to establish a wireless sensor network and connect it with the internet to form the IoT ^[15].

The limitations of these communication protocols are the short transmission range and high-power consumption. In view of the IoT, most IoT devices are resource-constrained (work on battery power), so it demands communication protocols that consume less energy and transmit to a long range. A low-power–wide-area network (LPWAN) enables these challenges to be overcome, as it meets the requirements of the IoT ^[16]. Long-range (LoRa), Sigfox, and narrowband (NB-IoT) are LPWAN communication technologies. As part of the application layer, a cloud server is suitable for visualization, real-time monitoring, and applying further analytics on the data available in it.AI is the science of enabling computers to accomplish intelligent tasks that could only be completed by humans.

AI is a multidisciplinary technology capable of combining machine learning, cognition, human-computer interaction, emotion recognition, data storage, and decision-making ^[17]. The bottleneck of AI was overcome with the advancement of processing power, and the development of deep learning and enhanced learning based on huge data progressed. With the constant advancement of GPUs has come the successful development of customized processors and increased computer capacity; this has established the groundwork for AI’s rapid progress. Artificial neural networks (ANNs), decision-support systems (DSSs), genetic algorithms (Gas), support-vector machines, and computer vision are some of the AI techniques that are widely applied in the field of agriculture for the management of soil, crops, disease, and weeds ^[18]. Big data is defined as data sets that are too massive or complicated to be acquired, maintained, and processed in a low-latency manner by conventional database systems ^[19]. Big data has characteristics such as high volume, high velocity, and high variety. Big-data analytics can ultimately enable better and faster decision-making, model, and forecast future outcomes, and improve business intelligence ^[20]. A distributed, unchangeable database called blockchain makes it simpler to track assets and record transactions in a corporate network ^[21]. Distributed ledger technology, immutable records, and smart contracts are the key elements of blockchain. Each transaction is logged as a “block” of data as it occurs. Each block is linked to the ones that come before and after it. Transactions are linked in an irreversible chain

Horticulture is utilizing the IoT to collect data from field planting and horticultural facilities for production, management, and service. Robots, drones, remote sensors, and computer imagery are used in horticulture as part of the IoT to monitor crops, survey, and map fields, as well as to provide data to farmers for logical farm-management strategies that will reduce both time and cost ^[22]. AI is facilitating various sectors in agriculture to enhance productiveness and performance and to overcome traditionally demanding situations in each field. The intervention of AI in horticulture is helping farmers to improve their farming efficiency and reduce hostile environmental impacts ^[23]. Blockchain horticulture empowers information to be traced throughout the food supply chain to improve food safety. The ability of blockchain to store and manage data creates traceability, which is utilized to enhance the creation and deployment of innovations for intelligent farming and index-based horticultural insurance. Improved quality control and food safety are advantages of applying blockchain to gardening. Increased supply-chain traceability of farmers’ productivity will lead to more equitable payments for farmers ^[24].

Farmers can learn in-depth information about topics like rainfall patterns, water cycles, fertilizer requirements, and more thanks to large datasets. Companies can utilize this information to choose which crops to produce and when to harvest them in order to maximize their profits ^[25]. Data on horticulture are gathered, examined, and stored using cloud computing. Farmers can better understand crop conditions by using cloud-connected wireless sensors to collect data from the field and machine-learning algorithms to analyze that real-time information ^[26]. Precision horticultural farming heavily relies on augmented reality. Farmers can use augmented reality in horticulture to boost production, reduce crop waste, and teach other farmers ^[27].

3. Intervention of Industry 4.0 in Horticulture

3.1. IoT Intervention in Horticulture

The potential of pests and plant diseases is inseparably associated in particular withspecific weather characteristics. Previous research concluded that humidity and rainfall have a significant impact on pathogen spread and propagation ^[28]. Pests and diseases are more inclined to spread as a result of the wind. According to the information presented above, pests and diseases are prevalent in crops, and monitoring of the related parameters can be achieved with the IoT. An intelligent monitoring system was proposed on the basis of the IoT with a global packet for radio service (GPRS) and Zigbee communication protocol for pest warning, planting works, and production-quality checks of apples with the assistance of soil sensors, meteorological sensors, and cameras ^[29]. The feasibility, yield, and irrigation water-use efficiency of an IoT-based precision-irrigation system with LoRaWAN technology on fresh-market tomato production was analyzed ^[30]. ET, Watermark 200SS-5 soil-metric potential sensors, MP40, and a decision-support system were used to design and test four irrigation-scheduling treatments. A study was conducted with the integration of a camera module, and the images collected were used to analyze the water-management system as well as detect plant disease within a green environment ^[31].

Researchers established a technology platform using the IoT for environmental data acquisition, disaster warning, transmission, remote control, and an information push in vegetable greenhouses in real-time and lessen the influence of climate catastrophes on vegetable development ^[32]. The data collected by an IoT board are expected to be utilized to train machine-learning models for the development of intelligent automated indoor microclimate horticulture crops ^[33]. A database contains the results of the sensor analysis and it is also linked with data from the Indonesian Weather Agency, which contains daily meteorological data from the cultivation location ^[34]. To ensure the proper operation of the greenhouse automation system, multiple measuring stations are needed in a modern greenhouse to identify the local climate parameters in various areas of a large-scale greenhouse ^[35]. The IoT paradigm is allowing the scientific community to establish integrated settings where data could be automatically transferred among many distinctive networks to provide consumers with specific relevant information ^[36]. The security of the foods supplied can be ensured by utilizing the provenance data that are kept throughout the supply chain of vegetables, including during planting, harvesting, government oversight, testing, transportation, customs clearance, warehousing, repackaging, and internal testing ^[37]^[38].

Every kind of pest and disease is believed to be harmful to plants and to have a serious unfavorable effect on horticulture. To decrease the frequent use of pesticides and fungicides and to anticipate when pests will arise, the IoT system was created ^[39]. Soft-computing technologies are used to identify fruits by combining the three essential characteristics of an object—color, shape, and texture. This method reduces the dimensions of the feature vector. As a result, the combined and normalized image features produce better classification accuracy with fewer training data ^[40]. Real-time supply-chain monitoring can give stakeholders insight into perishable food to better manage to price and take appropriate action to uphold quality requirements ^[41]. Farmers confront a variety of challenges when growing vegetables, including issues with seeds, managing pests and diseases, commodity costs, and product marketing. Farmers can use the internet and the concept of mobile cloud computing to access information and engage in an interactive dialogue about vegetable production through mobile learning ^[42]. A framework for papaya grading based on the Artificial Bee Colony algorithm was proposed to classify papaya fruits from digital photographs ^[43]^[44].

3.2. AI in Horticulture

Fresh and wholesome food is essential for feeding the expanding world population, and greenhouses and indoor agricultural techniques play a crucial role in this. In the past two decades, hyperspectral imaging research has developed, and in the decades to come, its use in horticulture is expected to grow. There are still challenges to the applicability. The automated detection of pests and diseases in plants empowers the effective monitoring of scalable fruit-and-vegetable crops. The detection of pests and diseases at the right time enhances pest- and disease-management systems ^[45]. The previous study concluded that AI algorithms can be implemented in horticulture for distinct applications, including fruit detection, pest, disease detection, weed detection, plant-stress detection, and yield prediction through the spectroscopy-and-camera system ^[46]. A study was implemented to identify common pests and diseases in apple fruit using sparse coding. Computer-vision techniques can identify pest- and disease-damaged fruits and provide data to assist in the detection and treatment of diseases and pests in the early stages ^[47]. Soil-organic-carbon (SOC) monitoring is a crucial characteristic of soil quality because it directly determines soil fertility and enables sustainable soil-nutrient management ^[48]. To improve SOC prediction, artificial neural networks (ANN), cubist regression, support-vector machines (SVM), multiple linear regression (MLR), and random forests (RF) were applied to the data of soil-nutrient indicators, total catchment area, and the topographic-wetness index.

Automatic detection of plant pests and diseases can aid in the monitoring of large farms and gardens. The application of AI in the drying process of fruits and vegetables has received a lot of attention because it can generate better-dried fruit-and-vegetable products when combined with an efficient physical field ^[49]^[50].An IoT-based tool can determine whether a climacteric fruit has been artificially ripened or not. To determine whether the fruit is ripened artificially or naturally, machine-learning algorithms are applied ^[51]. In order to reduce food waste, one study used sensors and analyzed gases produced by certain food products to detect rotten food at an early stage and boost accuracy. To forecast how frequently food will degrade, the researchers used machine learning, the IoT, and sensors.

3.3. Blockchain

Blockchain is a distributed-ledger technology with the advantage of being tamper-resistant to information. It is anticipated to be able to address the issue of resource allocation for transactions among numerous unreliable parties in the supply chain for fresh fruit ^[52]. One potential method for supply-chain traceability in the pineapple industry is blockchain technology. The fruit-chain protocol that was introduced has identical consistency and liveliness qualities as assuming an honest majority of computer power ^[53] and is roughly fair with an overwhelming probability. Although blockchain might be viewed as a viable option for food-chain traceability, it was determined that ^[54] the goal of the investigation was to learn more about blockchain technology and its potential applications in the retail industry. Additionally, potential blockchain uses that might help the retail sector and the wider industry are foreseen. The study also underlined the crucial role that blockchain technology plays in the retail sector for fruit as well as the connections between these aspects ^[55].

Consumers are driving an entirely different transformation in food procurement as a consequence of the growth in global food catastrophes that are triggering health insecurity. Consumers have called for transparency, traceability, and attentiveness along the entire fruit supply chain. The importance of the supply chain in this industry is increased by the fact that the products are perishable and have a limited shelf life. Yields are impacted by inconsistent delivery and a lack of fertilizers and insecticides as a result of dependence on middlemen, market instability, and other factors. Increased costs for input and transportation, post-harvest losses, and problems with safety and quality dominating supply-chain losses are key challenges involved in the fruit supply chain. Blockchain integration in the fruit supply chain allows for post-harvest and inventory management streamlining, increasing operational effectiveness and lowering losses. End-to-end traceability with QR codes on the fruits presents the final customer with an honest and reliable narrative. A fair price for the producers is ensured by the grouping and collaboration of all stakeholders on a single platform, which fosters confidence and transparency. Real-time data collection allows for simple tracking and tracing, which helps with recall management. In addition to this, blockchain is used for monitoring the pre-harvest process for yield and quality. Post-harvest management for monitoring the crucial phases to prevent losses and boost output, monitoring a set of procedures for confirming sustainable practices, and digital records that cannot be altered and display accurate information are necessary to meet legal requirements.

3.4. Big Data in Horticulture

A multi-sensor network system was established to accumulate smaller ecological statistical data on vegetable-growing regions. Researchers were able to identify the critical components pushing pest spread using multidimensional information such as environment, soil, meteorology of vegetable fields, and ultimately the vegetable-pest warning system premised on multidimensional big data ^[56]. The distinctive nutrition-based vegetable-production and -distribution system utilizes the inventive big-data framework and its multiple benefits to provide a healthful food recommendation to the end customer as well as various predictive analyses to boost system efficacy ^[36]. The new ICT horticulture project will heavily rely on big-data methodologies. Because fruits and vegetables are produced in such large quantities, the sensor data that are available and can be used in horticulture are now considered big data. The big data can be transmitted to the cloud server and made available in a distribution box and control box through wireless-communication protocols.

References

UN Report: Global Hunger Numbers Rose to as Many as 828 Million in 2021. Available online: https://www.fao.org/newsroom/detail/un-report-global-hunger-SOFI-2022-FAO/en (accessed on 6 September 2022).
Goal 2: Zero Hunger-United Nations Sustainable Development. Available online: https://www.un.org/sustainabledevelopment/hunger/ (accessed on 6 September 2022).
Goal 12. Department of Economic and Social Affairs. Available online: https://sdgs.un.org/goals/goal12 (accessed on 6 September 2022).
Keatinge, J.D.H.; Virchow, D.; Schreinemachers, P. Horticulture for sustainable development: Evidence for impact of international vegetable research and development. In Proceedings of the International Symposia on Tropical and Temperate Horticulture-ISTTH2016 1205, Cairns, Australia, 20 November 2016; pp. 179–190.
Qingxue, L.; Wu, H. Research on vegetable growth monitoring platform based on facility agricultural IoT. In International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem; Springer: Singapore, 2016.
Cappelli, I.; Fort, A.; Pozzebon, A.; Tani, M.; Trivellin, N.; Vignoli, V.; Bruzzi, M. Autonomous IoT Monitoring Matching Spectral Artificial Light Manipulation for Horticulture. Sensors 2022, 22, 4046.
Eftekhari, M.S. Impacts of Climate Change on Agriculture and Horticulture. In Climate Change; Bandh, S.A., Ed.; Springer: Cham, Switzerland, 2022.
Saran, P.L.; Singh, S.; Solanki, V.; Choudhary, R.; Manivel, P. Evaluation of Asparagus adscendens accessions for root yield and shatavarin IV content in India. Turk. J. Agric. For. 2021, 45, 8.
Zulfiqar, F. Effect of seed priming on horticultural crops. Sci. Hortic. 2021, 286, 110197.
Grygorieva, O.; Klymenko, S.; Kuklina, A.; Vinogradova, Y.; Vergun, O.; Sedlackova, V.H.; Brindza, J. Evaluation of Lonicera caerulea L. genotypes based on morphological characteristics offruits germplasm collection. Turk. J. Agric. For. 2021, 45, 14.
Mall, M.; Kumar, R.; Akhtar, M.Q. Horticultural crops and abiotic stress challenges. In Stress Tolerance in Horticultural Crops; Woodhead Publishing: Sawston, UK, 2021; pp. 1–19.
Alireza, A.; Dennis, R.; Ludena, A. Big data application to the vegetable production and distribution system. In Proceedings of the 2014 IEEE 10th International Colloquium on Signal Processing and Its Applications, Kuala Lumpur, Malaysia, 7–9 March 2014.
Kaburuan, E.R.; Jayadi, R. A design of IoT-based monitoring system for intelligence indoor micro-climate horticulture farming in Indonesia. Procedia Comput. Sci. 2019, 157, 459–464.
Alem, Č.; Hadžialić, M. Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues. Comput. Netw. 2018, 144, 17–39.
Mohammad Nuruzzaman, B.; Rahman, M.; Billah, M.; Saha, D. Internet of things (IoT): A review of its enabling technologies in healthcare applications, standards protocols, security, and market opportunities. IEEE Internet Things J. 2021, 8, 10474–10498.
Kais, M.; Bajic, E.; Chaxel, F.; Meyer, F. A comparative study of LPWAN technologies for large-scale IoT deployment. ICT Express 2019, 5, 1–7.
Arya, P.S.; Gangwar, M. A Proposed Architecture: Detecting Freshness of Vegetables using Internet of Things (IoT) & Deep Learning Prediction Algorithm. In Proceedings of the 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 17–18 December 2021; pp. 718–723.
Caiming, Z.; Lu, Y. Study on artificial intelligence: The state of the art and future prospects. J. Ind. Inf. Integr. 2021, 23, 100224.
Sagiroglu, S.; Sinanc, D. Big data: A review. In Proceedings of the 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA, 20–24 May 2013; pp. 42–47.
Yosra, H.; Boulila, W.; Farah, I.R.; Romdhani, I.; Hussain, A. Big data and IoT-based applications in smart environments: A systematic review. Comput. Sci. Rev. 2021, 39, 100318.
Mohd, J.; Haleem, A.; Singh, R.P.; Khan, S.; Suman, R. Blockchain technology applications for Industry 4.0: A literature-based review. Blockchain Res. Appl. 2021, 2, 100027.
Alireza, A.; Ludena, R.D.A. Big Data approach to a novel nutrition-based vegetable production and distribution system. In Proceedings of the 2013 IEEE International Conference on Computational Intelligence and Cybernetics (CYBERNETICSCOM), Yogyakarta, Indonesia, 3–4 December 2013.
Kumar, P.C.; Bhatnagar, R. Social internet of things in agriculture: An overview and future scope. In Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications; Springer: Berlin/Heidelberg, Germany, 2020; pp. 317–334.
Sun, Z.; Hui, X.; Wensheng, W. An architecture for the agricultural machinery intelligent scheduling in cross-regional work based on cloud computing and internet of things. In International Conference on Computer and Computing Technologies in Agriculture; Springer: Berlin/Heidelberg, Germany, 2010.
Coble, K.H.; Mishra, A.K.; Ferrell, S.; Griffin, T. Big data in agriculture: A challenge for the future. Appl. Econ. Perspect. Policy 2018, 40, 79–96.
Ruthie, M. Big data in agriculture and nutrition. Agric. Improv. Nutr. Seizing Momentum 2019, 142, 1–15.
Kamilaris, A.; Fonts, A.; Prenafeta-Boldύ, F.X. The rise of blockchain technology in agriculture and food supply chains. Trends Food Sci. Technol. 2019, 91, 640–652.
Codeluppi, G.; Cilfone, A.; Davoli, L.; Ferrari, G. VegIoT garden: A modular IoT management platform for urban vegetable gardens. In Proceedings of the 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Portici, Italy, 24–26 October 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 121–126.
Salgado-Salazar, C.; Shiskoff, N.; Daughtrey, M.; Palmer, C.L.; Crouch, J.A. Downy mildew: A serious disease threat to rose health worldwide. Plant Dis. 2018, 102, 1873–1882.
Feng, C.; Wu, H.R.; Zhu, H.J.; Sun, X. The design and realization of apple orchard intelligent monitoring system based on internet of things technology. In Advanced Materials Research; Trans Tech Publications Ltd.: Bäch, Switzerland, 2012; Volume 546, pp. 898–902.
Zhang, H.; He, L.; Di Gioia, F.; Choi, D.D.; Heinemann, P. Internet of things (IoT)-based precision irrigation with LoRaWAN technology applied to vegetable production. In 2021 ASABE Annual International Virtual Meeting; American Society of Agricultural and Biological Engineers: St. Joseph, MI, USA, 2021; p. 1.
Khan, F.A.; Ibrahim, A.A.; Zeki, A.M. Environmental monitoring and disease detection of plants in smart greenhouse using internet of things. J. Phys. Commun. 2020, 4, 055008.
Bhujel, A.; Basak, J.K.; Khan, F.; Arulmozhi, E.; Jaihuni, M.; Sihalath, T.; Lee, D.; Park, J.; Kim, H.T. Sensor Systems for Greenhouse Microclimate Monitoring and Control: A Review. J. Biosyst. Eng. 2020, 45, 341–361.
Wayangkau, I.H.; Mekiuw, Y.; Rachmat, R.; Suwarjono, S.; Hariyanto, H. Utilization of IoT for soil moisture and temperature monitoring system for onion growth. Emerg. Sci. J. 2020, 4, 102–115.
Alper, M.A.; Sokullu, R. An IoT-based greenhouse monitoring system with Micaz motes. Procedia Comput. Sci. 2017, 113, 603–608.
Dennis, A.L.R.; Ahrary, A.; Horibe, N.; Yang, W.S. IoT-security approach analysis for the novel nutrition-based vegetable production and distribution system. In Proceedings of the 3rd IIAI International Conference on Advanced Applied Informatics, IIAI-AAI 2014, Kokura Kita-ku, Japan, 31 August–4 September 2014.
Jie, Y.; Zhang, X.; Lu, Q.; Xin, C.; Liu, C.; Chen, Z. IOT based provenance platform for vegetables supplied to Hong Kong. In Recent Advances in Computer Science and Information Engineering; Springer: Berlin/Heidelberg, Germany, 2012; pp. 591–596.
Harish, M.P.C.S.P.; Sanghavi, K.B.P.A. Smart refrigerator and vegetable identification system using image processing and IOT. Open Access Int. J. Sci. Eng. 2021, 7, 4.
Kumar, M.; Pal, Y.; Gangadharan, S.M.P.; Chakraborty, K.; Yadav, C.S.; Kumar, H.; Tiwari, B. Apple Sweetness Measurement and Fruit Disease Prediction Using Image Processing Techniques Based on Human-Computer Interaction for Industry 4.0. Wirel. Commun. Mob. Comput. 2022, 2022, 1–12.
Rajasekar, L.; Sharmila, D. Performance analysis of soft computing techniques for the automatic classification of fruits dataset. Soft Comput. 2019, 23, 2773–2788.
John, K.; Haw, S.; Su, N.; Mulaafer, S. Kiwi Fruit IoT Shelf Life Estimation During Transportation with Cloud Computing. In Proceedings of the 2021 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), Kota Kinabalu, Malaysia, 13–15 September 2021.
Shwetapadma, P.; Sethy, P.K. Post-Harvest Grading Of Carica Papaya Fruit Using Image Segmentation And Soft Computing. Int. J. Adv. Res. Comput. Sci. 2017, 8, 7.
Yaya, W.; Nugraha, E. Development Mobile Learning For Vegetable Farming In Indonesia Based On Mobile Cloud Computing. In Proceedings of the 2020 6th International Conference on Science in Information Technology (ICSITech), Palu, Indonesia, 21–22 October 2020.
Narendra, V.G.; Ancilla, J.P. Defects Detection in Fruits and Vegetables Using Image Processing and Soft Computing Techniques. In International Conference on Harmony Search Algorithm; Springer: Singapore, 2020.
Silke, H.; de Zwart, F.; Elings, A.; Petropoulou, A.; Righini, I. Cherry Tomato Production in Intelligent Greenhouses—Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality. Sensors 2020, 20, 6430.
Kingsley, J.; Isong, I.A.; Kebonye, N.M.; Ayito, E.O.; Agyeman, P.C.; Afu, S.M. Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil. Land 2020, 9, 487.
Yousef, A.-G.; Aghabara, A.; Davari, M.; Maja, J.M. Feasibility of Using Computer Vision and Artificial Intelligence Techniques in Detection of Some Apple Pests and Diseases. Appl. Sci. 2022, 12, 906.
Diaz-Gonzalez, F.A.; Vuelvas, J.; Correa, C.A.; Vallejo, V.E.; Patino, D. Machine learning and remote sensing techniques applied to estimate soil indicators–Review. Ecol. Indicators 2022, 135, 108517.
Maione, C.; Araujo, E.M.; Santos-Araujo, S.N.D.; Boim, A.G.F.; Barbosa, R.M.; Alleoni, L.R.F. Determining the geographical origin of lettuce with data mining applied to micronutrients and soil properties. Sci. Agric. 2021, 79, e20200011.
Chen, J.; Zhang, M.; Xu, B.; Sun, J.; Mujumdar, A.S. Artificial intelligence assisted technologies for controlling the drying of fruits and vegetables using physical fields: A review. Trends Food Sci. Technol. 2020, 105, 251–260.
Sreeraj, M.; Joy, J.; Kuriakose, A.; Sujith, M.R.; Unni, H. CLadron*: AI assisted device for identifying artificially ripened climacteric fruits. Procedia Comput. Sci. 2020, 171, 635–643.
Rafael, P.; Shi, E. Fruitchains: A fair blockchain. In Proceedings of the ACM Symposium on Principles of Distributed Computing, Washington, DC, USA, 25–27 July 2017.
Zhang, Y.; Chen, L.; Battino, M.; Farag, M.A.; Xiao, J.; Simal-Gandara, J.; Jiang, W. Blockchain: An emerging novel technology to upgrade the current fresh fruit supply chain. Trends Food Sci. Technol. 2022, 124, 1–22.
Arsenii, S. Traceability technology: Fruits and vegetables trader case study. In Proceedings of the International Conference on Digital Technologies in Logistics and Infrastructure (ICDTLI 2019), St. Petersburg, Russia, 4–5 April 2019; Atlantis Press: Hong Kong, China, 2019.
Sharif, K.I.M.; Hassan, M.G.; Miraz, M.H.; Zulkifly, E.; Udin, Z.M.; Omar, M. Factors Affecting Blockchain in Fruit Retail Market: An Unveiling Myth of Blockchain. In Intelligent Manufacturing and Mechatronics; Springer: Singapore, 2021; pp. 1095–1106.
Zhang, C.; Cai, J.; Xiao, D.; Ye, Y.; Chehelamirani, M. Research on vegetable pest warning system based on multidimensional big data. Insects 2018, 9, 66.

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