Nutrient optimization for plant growth in Aquaponic irrigation using Machine Learning for small training datasets

With the recent trends in urban agriculture and climate change,there is an emerging need for alternative plant culture techniques where dependence on soil can be eliminated.Hydroponic and aquaponic growth techniques have proven to be viable alternatives,but the lack of efficient and optimal practices for irrigation and nutrient supply limits its applications on a large-scale commercial basis.The main purpose of this research was to develop statistical methods and Machine Learning algorithms to regulate nutrient concentrations in aquaponic irrigation water based on plant needs,for achieving optimal plant growth and promoting broader adoption of aquaponic culture on a commercial scale.One of the key challenges to developing these algorithms is the sparsity of data which requires the use of Bolstered error estimation approaches.In this paper,several linear and non-linear algorithms trained on relatively small datasets using Bolstered error estimation techniques were evaluated,for selecting the best method in making decisions regarding the regulation of nutrients in hydroponic environments.After repeated tests on the dataset,it was decided that Semi-Bolstered Resubstitution Error estimation technique works best in our case using Linear Support Vector Machine as the classifier with the value of penalty parameter set to one.A set of recommended rules have been prescribed as a Decision Support System,using the output of the Machine Learning algorithm,which have been tested against the results of the baseline model.Further,the positive impact of the recommended nutrient concentrationson plant growth in aquaponic environments has been elaborately discussed.

Assessing the performance of YOLOv5 algorithm for detecting volunteer cotton plants in corn fields at three different growth stages

The feral or volunteer cotton(VC)plants when reach the pinhead squaring phase(5–6 leaf stage)can act as hosts for the boll weevil(Anthonomus grandis L.)pests.The Texas Boll Weevil Eradication Program(TBWEP)employs people to locate and eliminate VC plants growing by the side of roads or fields with rotation crops but the ones growing in the middle of fields remain undetected.In this paper,we demonstrate the application of computer vision(CV)algorithm based on You Only Look Once version 5(YOLOv5)for detecting VC plants growing in the middle of corn fields at three different growth stages(V3,V6 and VT)using unmanned aircraft systems(UAS)remote sensing imagery.All the four variants of YOLOv5(s,m,l,and x)were used and their performances were compared based on classification accuracy,mean average precision(mAP)and F1-score.It was found that YOLOv5s could detect VC plants with maximum classification accuracy of 98%and mAP of 96.3%at V6 stage of corn while YOLOv5s and YOLOv5m resulted in the lowest classification accuracy of 85%and YOLOv5m and YOLOv5l had the least mAP of 86.5%at VT stage on images of size 416×416 pixels.The developed CV algorithm has the potential to effectively detect and locate VC plants growing in the middle of corn fields as well as expedite the management aspects of TBWEP.