An Enhanced Deep Learning Approach in Forecasting Banana Harvest Yields

Abstract

This technical quest aspired to build deep multifaceted system proficient in forecasting banana harvest yields essential for extensive planning for a sustainable production in the agriculture sector. Recently, deep-learning (DL) approach has been used as a new alternative model in forecasting. In this paper, the enhanced DL approach incorporates multiple long short term memory (LSTM) layers employed with multiple neurons in each layer, fully trained and built a state for forecasting. The enhanced model used the banana harvest yield data from agrarian reform beneficiary (ARB) cooperative of Dapco in Davao del Norte, Philippines. The model parameters such as epoch, batch size and neurons underwent tuning to identify its optimal values to be used in the experiments. Additionally, the root-mean-squared error (RMSE) is used to evaluate the performance of the model. Using the same set of training and testing data, experiment exhibits that the enhanced model achieved the optimal result of 34.805 in terms of RMSE. This means that the enhanced model outperforms the single and multiple LSTM layer with 43.5 percent and 44.95 percent reduction in error rates, respectively. Since there is no proof that LSTM recurrent neutral network has been used with the same agricultural problem domain, therefore, there is no standard available with regards to the level of error reduction in the forecast. Moreover, investigating the performance of the model using diverse datasets specifically with multiple input features (multivariate) is suggested for exploration. Furthermore, extending and embedding this approach to a web-based along with a handy application is the future plan for the benefit of the medium scale banana growers of the region for efficient and effective decision making and advance planning.

Applications of deep-learning approaches in horticultural research: a review

“…The third type includes end-to-end, single-stage deep-learning object detection algorithms, which can directly return the categories and position borders of multiple objects, such as the YOLO 37 , 72 , 74 and SSD 56 methods. Based on these models, fruit yield can be automatically estimated 32 , 54 , 68 , flower and fruitlet thinning and other gardening operations can be automatically conducted 48 , 101 , and the early detection of plant stress can be accomplished 6 , 90 .…”

“…The property of a highly hierarchical structure along with the massive learning capability of deep-learning models enables them to carry out predictions and classifications particularly well with good flexibility and adaptability to a wide range of highly complicated data analysis tasks 28 . With the robust capability of automatic feature learning, many complex problems in the field of horticultural science can be solved in an effective and rapid way by utilizing deep-learning methods, includin g various recognition 29 – 31 , yield estimation 32 , 33 , quality detection 27 , 34 , stress phenotyping detection 35 , 36 , growth monitoring 37 , 38 , and other applications 39 , 40 . In the next section, we introduce these applications in detail.…”

“…From the brief introduction of several commonly used models described above, we know that deep-learning technology has a powerful role in image classification, regression and segmentation. Furthermore, there are also many other kinds of network structures that are applied, such as single shot multibox detection (SSD) 56 , long short-term memory (LSTM) 32 , you only look once (YOLO, source code is available at: http://pjreddie.com/yolo/ ) 57 , regions-CNN (R-CNN) 58 , fast region-based CNNs (Fast R-CNN, source code is available at: https://arxiv.org/abs/1504.08083 ) 33 , faster region-based CNNs (Faster-RCNN, source code is available at: https://github.com/shaoqingren/faster_rcnn (in MATLAB) and at https://github.com/rbgirshick/py-faster-rcnn (in Python)) 44 , 59 , 60 , and so on. In addition, the processed data types are not simply limited to RGB images but can also include any other data forms, such as video, hyperspectral images, and spectral data.…”

“…Rebortera et al 32 built a deep multifaceted system that incorporated a number of LSTM layers to forecast banana harvest yields. The dataset contained 35,000 observations of banana harvest yields from approximately 2014 to 2018.…”

“… Study Data Model used Performance metric and value Comparison with other methods Source code References 1 Forecasting banana harvest yields Thirty-five thousand series of observations from 2014 to 2018. mdLSTM RMSE: 34.805 Single LSTM: 61.602 (RMSE) Not available 32 2 Detecting and counting the number of bananas High-resolution RGB aerial images from 40, 50 and 60 meters above ground Faster R-CNN Inception-V2 Merging the detection results of the 40- and 50-meter datasets: 99% (Recall), 98.3% (Precision) and 98.7% (CA) NA Not available 78 3 Predicting the total yield of a commercial citrus orchard. 900 images from a UAV Faster R-CNN: for fruit detection LSTM: for yield estimation Between visual counting and the model’s fruit detection: 6.59% (SE) Between the actual and estimated yields per tree: 4.53% (SE), 0.97 kg (SD) NA Not available 77 4 Video-based mango fruit yield estimation Video: ~30 frames per tree and a shift of ~20 pixels per frame by the imaging platform.…”