Automatic Watering Systems in Vertical Farming Using the Adaline Algorithm

Siregar, Riki Ruli A.; Palupiningsih, Pritasari; Lailah, Inas Suha; Sangadji, Iriansyah Bm; Sukmajati, Sigit; Pahiyanti, Novi Gusti

doi:10.2991/aer.k.201221.070

Cited by 2 publications

(2 citation statements)

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“…Optimalisasi sistem irigasi dianggap sebagai langkah yang baik guna mengatasi kelangkaan air, penghematan air, dan juga pemerataan pendapatan air bagi lahan pertanian tadah hujan [5]. Mengoptimalkan distribusi air pada sistem irigasi terdiri dari pengoptimalan tata letak, meminimalkan panjang pipa, melihat kebutuhan air pada tanah dan elemenelemen lain yang saling berhubungan [11], [12] [13]. Ant Colony Optimization (ACO) saat ini menjadi alternatif terbaru untuk mendesain sistem irigasi [14][15]- [19] ACO adalah pencarian metaheuristic terinspirasi dari perilaku semut dalam mencari makan, pada saat mencari makan semut akan mencari jalur terpendek dan kemudian meningalkan zat kimia yang disebut dengan feronom sebagai sebuah media penanda atau komunikasi dengan semut yang lain [20] [21].…”

Section: Pendahuluanunclassified

Implementasi Ant Colony Optimization (ACO) pada Sistem Irigasi Lahan Tadah Hujan

Haris

Juwita

Aziza

et al. 2021

kilat

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The purpose of this research is to produce an optimal water distribution system for irrigation of rainfed land. The problem with conventional irrigation systems is that the water distribution process cannot be controlled and monitored automatically and in real time. The impact on water distribution becomes ineffective. The implementation of Ant Colony Optimization (ACO) is used in research as a method to determine the location or node based on the pheromone pattern of the soil dryness level at the sprinkler nodes to be distributed by the water flow, taking into account the criteria level on the soil as a trend of probability values and determining the nodes according to the needs in the flow water. The results obtained from this study indicate that the data displayed is the level of dryness of each node, the volume of water in the reservoir, and the flow of water flowing. The ACO test shows the sequence of nodes that will be passed after the optimization process of water distribution in a rainfed irrigation system using the ACO method gets an error value calculated by the MAPE method of 43% so that it gets an accuracy value of 57%.

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Section: Pendahuluanunclassified

Implementasi Ant Colony Optimization (ACO) pada Sistem Irigasi Lahan Tadah Hujan

Haris

Juwita

Aziza

et al. 2021

kilat

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“…DL algorithms applied to CEA across all units have provided insights into farmers’ support and action. Computer vision and DL algorithms have been implemented to automate the irrigation in vertical stack farms [ 13 ], and microclimate control [ 14 ], which facilitated the growers to carry out a quantitative assessment for high-level decision-making.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

Ojo

Zahid

2022

Sensors

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Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop monitoring, detecting biotic and abiotic stresses, irrigation, microclimate prediction, energy efficient controls, and crop growth prediction. However, no review study assess DL’s state of the art to solve diverse problems in CEA. To fill this gap, we systematically reviewed DL methods applied to CEA. The review framework was established by following a series of inclusion and exclusion criteria. After extensive screening, we reviewed a total of 72 studies to extract the useful information. The key contributions of this article are the following: an overview of DL applications in different CEA facilities, including greenhouse, plant factory, and vertical farm, is presented. We found that majority of the studies are focused on DL applications in greenhouses (82%), with the primary application as yield estimation (31%) and growth monitoring (21%). We also analyzed commonly used DL models, evaluation parameters, and optimizers in CEA production. From the analysis, we found that convolutional neural network (CNN) is the most widely used DL model (79%), Adaptive Moment Estimation (Adam) is the widely used optimizer (53%), and accuracy is the widely used evaluation parameter (21%). Interestingly, all studies focused on DL for the microclimate of CEA used RMSE as a model evaluation parameter. In the end, we also discussed the current challenges and future research directions in this domain.

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Automatic Watering Systems in Vertical Farming Using the Adaline Algorithm

Cited by 2 publications

References 5 publications

Implementasi Ant Colony Optimization (ACO) pada Sistem Irigasi Lahan Tadah Hujan

Implementasi Ant Colony Optimization (ACO) pada Sistem Irigasi Lahan Tadah Hujan

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

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