Abstract:
Purpose: This study assesses and consolidates Artificial Intelligence (AI) and robotic
based farm automation advancements, focusing on machine learning (ML) and deep
learning (DL). The paper compares AI algorithms and architectures for plant disease
detection, weed and crop identification, fruit counting, land cover classification, and
crop and plant recognition.
Design/methodology/approach: This article analyses the current ML and DL
algorithm advances in agricultural robotics over the last decade using a systematic
literature review. Region-based Convolutional Neural Networks (RCNN), ResNet-18,
and Fully Convolutional Networks (FCN) are compared to traditional ML algorithms
like Multi-Layer Perceptron (MLP), K-nearest Neighbour (KNN), Random Forest
(RF), and Support Vector Machine (SVM) to determine their precision and
effectiveness.
Findings: RCNNs identify plant diseases at 79.78% vs 57.18% for MLP and KNN.
ResNet-18 has a high Area Under the Curve (AUC) of 91.74% for crop-weed
separation. This discriminates better than RF and SVM. FCN outperforms SVM and
RF in land cover classification at 84.9%. The data show that DL techniques improve
agricultural automation very well.
Practical implications: This investigation shows that DL algorithms can considerably
improve agricultural automation. Agriculture professionals may enhance disease
identification, crop classification, and land coverage analysis by using advanced DL
models.
Originality value: This paper analyses the current ML and DL breakthroughs in
agricultural automation to expand knowledge. It offers fresh viewpoints on AI model
efficacy and highlights key research areas.
