Aquaponics is an innovative, smart, and sustainable agricultural technology that integrates aquaculture (farming of fish) with hydroponics in growing vegetable crops symbiotically. The correct implementation of aquaponics helps in providing healthy organic foods with low consumption of water and chemical fertilizers. Numerous research attempts have been directed toward real implementations of this technology feasibly and reliably at large commercial scales and adopting it as a new precision technology. For better management of such technology, there is an urgent need to use the Internet of things (IoT) and smart sensing systems for monitoring and controlling all operations involved in the aquaponic systems. Thence, the objective of this article is to comprehensively highlight research endeavors devoted to the utilization of automated, fully operated aquaponic systems, by discussing all related aquaponic parameters aligned with smart automation scenarios and IoT supported by some examples and research results. Furthermore, an attempt to find potential gaps in the literature and future contributions related to automated aquaponics was highlighted. In the scope of the reviewed research works in this article, it is expected that the aquaponics system supported with smart control units will become more profitable, intelligent, accurate, and effective.

In the aquaponic system, plant nutrients bioavailable from fish excreta are not sufficient for optimal plant growth. Accurate and timely monitoring of the plant’s nutrient status grown in aquaponics is a challenge in order to maintain the balance and sustainability of the system. This study aimed to integrate color imaging and deep convolutional neural networks (DCNNs) to diagnose the nutrient status of lettuce grown in aquaponics. Our approach consists of multi-stage procedures, including plant object detection and classification of nutrient deficiency. The robustness and diagnostic capability of proposed approaches were evaluated using a total number of 3000 lettuce images that were classified into four nutritional classes—namely, full nutrition (FN), nitrogen deficiency (N), phosphorous deficiency (P), and potassium deficiency (K). The performance of the DCNNs was compared with traditional machine learning (ML) algorithms (i.e., Simple thresholding, K-means, support vector machine; SVM, k-nearest neighbor; KNN, and decision Tree; DT). The results demonstrated that the deep proposed segmentation model obtained an accuracy of 99.1%. Also, the deep proposed classification model achieved the highest accuracy of 96.5%. These results indicate that deep learning models, combined with color imaging, provide a promising approach to timely monitor nutrient status of the plants grown in aquaponics, which allows for taking preventive measures and mitigating economic and production losses. These approaches can be integrated into embedded devices to control nutrient cycles in aquaponics.

The symptoms of the nutrient stress in plant canopies differ among different growth stages. It is a challenge to develop individual diagnosis models to evaluate the nutrient status for a specific growth stage in time. Therefore, this study encoded spatiotemporal information of plants in a single time-series model to evaluate the nutrient status of oilseed rape more efficiently. Specifically, we combined the convolutional neural network and long-short term memory (CNN-LSTM) to classify the oilseed rape crops according to their nutrient status. The model was validated on a large number of sequential images acquired from oilseed rape canopies at different growth stages during a two-year experiment. Different pre-trained CNNs were used to extract distinctive features from every time step of sequential images and then these features were considered as the input of LSTM to classify the oilseed rape into nine classes of nutrient statuses. We demonstrated that the LSTM outperformed the traditional machine learning method and the deep features showed better performance compared to hand-crafted features. The Inceptionv3-LSTM obtained the highest overall classification accuracy of 95% when tested on the dataset of 2017/2018 and it also provided a good generalization when using a cross-dataset validation, with the highest overall accuracy of 92%. Our proposed approach presents a pathway toward automatic nutrient status diagnosis during the whole life cycle of the plants and the LSTM technique would play an essential role in the near future for time-series analysis for precision agriculture.

Plant color is a key feature for estimating parameters of the plant grown under different conditions using remote sensing images. In this case, the variation in plant color should be only due to the influence of the growing conditions and not due to external confounding factors like a light source. Hence, the impact of the light source in plant color should be alleviated using color calibration algorithms. This study aims to develop an efficient, robust, and cutting-edge approach for automatic color calibration of three-band (red green blue: RGB) images. Specifically, we combined the k-means model and deep learning for accurate color calibration matrix (CCM) estimation. A dataset of 3150 RGB images for oilseed rape was collected by a proximal sensing technique under varying illumination conditions and used to train, validate, and test our proposed framework. Firstly, we manually derived CCMs by mapping RGB color values of each patch of a color chart obtained in an image to standard RGB (sRGB) color values of that chart. Secondly, we grouped the images into clusters according to the CCM assigned to each image using the unsupervised k-means algorithm. Thirdly, the images with the new cluster labels were used to train and validate the deep learning convolutional neural network (CNN) algorithm for an automatic CCM estimation. Finally, the estimated CCM was applied to the input image to obtain an image with a calibrated color. The performance of our model for estimating CCM was evaluated using the Euclidean distance between the standard and the estimated color values of the test dataset. The experimental results showed that our deep learning framework can efficiently extract useful low-level features for discriminating images with inconsistent colors and achieved overall training and validation accuracies of 98.00% and 98.53%, respectively. Further, the final CCM provided an average Euclidean distance of 16.23 ΔΕ and outperformed the previously reported methods. This proposed technique can be used in real-time plant phenotyping at multiscale levels.

Remote sensing images often suffer from color distortion, which can pose significant challenges for accurate data interpretation. To overcome this issue, this study developed a new approach called the multi-channel convolutional neural network (mCNN). This technique treated color calibration of remote sensing images as an image-to-image regression problem and used a neural network to learn a mapping function between the distorted and calibrated images by assigning varying weights to pixels across the scene. The mCNN consisted of three convolutional layer groups, each applied to one image channel separately. The input to each group was a residual channel, which is the difference between the distorted and calibrated images for that channel. A fusion layer was added to concatenate the outputs of the last convolutional layer from all channels, and then applied a regression layer to generate a full output image while preserving its structural details. Unlike other deep learning methods that work in multiple steps, the mCNN performed color calibration in a single step, leading to more efficient processing of large datasets. The method was validated using high-quality multi-type ground-truth datasets and compared with other color correction methods as well as its variants. Results showed that the mCNN model outperformed all competitors in terms of the mean value of the estimation errors, with a margin of 34.24%. These findings suggest that the proposed method is highly effective in addressing color distortion issues in remote sensing images, which is critical for accurately representing vegetation properties.