Abstract
Recent technological developments in the primary sector and machine learning algorithms allow the combined application of many promising solutions in precision agriculture. For example, the YOLOv5 (You Only Look Once) and ResNet Deep Learning architecture provide high-precision real-time identifications of objects. The advent of datasets from different perspectives provides multiple benefits, such as spheric view of objects, increased information, and inference results from multiple objects detection per image. However, it also raises crucial obstacles such as total identifications (ground truths) and processing concerns that can lead to devastating consequences, including false-positive detections with other erroneous conclusions or even the inability to extract results. This paper introduces experimental results from the machine learning algorithm (Yolov5) on a novel dataset based on perennial fruit crops, such as sweet cherries, aiming to enhance precision agriculture resiliency. Detection is oriented on two points of interest: (a) Infected leaves and (b) Infected branches. It is noteworthy that infected leaves or branches indicate stress, which may be due to either a stress/disease (e.g., Armillaria for sweet cherries trees, etc.) or other factors (e.g., water shortage, etc). Correspondingly, the foliage of a tree shows symptoms, while this indicates the stages of the disease.
Highlights
Accepted: 16 December 2021In the modern industrial era, the primary production sectors have made a tremendous leap in automating and optimizing their subsequent production and processing methods.In particular, with the ever ending infiltration of smart technologies [1], such as UnmannedAerial Vehicles (UAVs) [2], Robots [3], smart supply chains, the continuous evolution of Computer Vision (CV) and the continuous amelioration of Artificial Intelligence (AI) in most industrial technologies, the production methods in the primary sector [4,5] have undergone a serious upgrade to new quality standards [6]
On the subject of product quality assurance, the agricultural sector has been influenced by many novel AI-enabled technologies, and implementations based on the Machine Learning (ML) and Deep Learning (DL) paradigm
The location of the study has a latitude “40.81007636843986” and longitude “21.800632900335405”. These coordinates were acquired from google maps which use World Geodetic System (WGS) 84 format
Summary
Aerial Vehicles (UAVs) [2], Robots [3], smart supply chains, the continuous evolution of Computer Vision (CV) and the continuous amelioration of Artificial Intelligence (AI) in most industrial technologies, the production methods in the primary sector [4,5] have undergone a serious upgrade to new quality standards [6]. This phenomenon has increasingly been seen in the Agricultural sector, as new methods to procure the quality of the product and establish its sustainability are needed [7].
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