Detection and Characterization of Stressed Sweet Cherry Tissues Using Machine Learning

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.

Cherry Tree Crown Extraction Using Machine Learning Based on Images from UAVs

“…Image classification and object detection are two tasks that are highly used in Smart Farming for various purposes, such as detecting or classifying diseases [25][26][27], weeds [28,29], fruits [30,31], pests [32,33] or monitoring crops [34] in the fields or greenhouses. Various machine learning and deep learning algorithms based on Convolutional Neural Networks (CNN) have been developed in recent years.…”

Improved YOLOv5: Efficient Object Detection Using Drone Images under Various Conditions

“…For example, be studied in fields such as automatic license plate recognition [5]; detection of the diseased plant [6]; traffic light detector for self-driving vehicles [7,8]; for violent individual identification [9]; and detector for ship detection in SAR Images [10]. Searching for missing objects in a disaster situation or used in operational missions in war situations, and it is necessary in a situation where medical staff can quickly find injured people at the accident site [11][12][13].…”

Drones in B5G/6G Networks as Flying Base Stations

“…B5G and 6G communications are envisaged to support wireless federated learning (WFL) [6] through the joint optimization of resources targeting a reduction in the delay for novel federated applications [7]. Inherent support for AI within the network core will be pivotal in supporting novel applications in a spectrum of domains, with a great example being drone-enabled intelligent surveillance and machine vision-supported remote sensing [8].…”