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arxiv: 2604.24906 · v1 · submitted 2026-04-27 · 💻 cs.RO · cs.LG· cs.SY· eess.SY

An analysis of sensor selection for fruit picking with suction-based grippers

Eva Krueger , Marcus Rosette , Joseph R. Davidson This is my paper

Pith reviewed 2026-05-08 02:31 UTC · model grok-4.3

classification 💻 cs.RO cs.LGcs.SYeess.SY
keywords pickfruitsensorapplecompliantdetectdetectionfailures
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0 comments X

The pith

Multimodal sensors allow over 90% accurate detection of apple pick success and failures in real orchards

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper analyzes which sensors in a suction-based gripper are most useful at different stages of picking apples to detect if the fruit has been successfully removed or is slipping. By integrating multiple sensors and using machine learning, it identifies minimal sets that work well. Real-world tests in an orchard prove the approach can predict issues quickly. This helps robots harvest more reliably without extra damage to crops or trees.

Core claim

The authors show through orchard experiments that phase-dependent analysis of sensors in a suction gripper enables Random Forest and Multilayer Perceptron classifiers to detect successful picks and impending failures with over 90% accuracy, predicting events within 0.09 s of human ground truth.

What carries the argument

Phase-dependent multimodal sensor evaluation in a compliant suction gripper for pick state classification using pressure, force, and inertial data.

Load-bearing premise

The sensor informativeness and classifier accuracies will remain consistent under different orchard conditions, fruit sizes, stem strengths, and with gripper wear.

What would settle it

Collecting new data from a different orchard or with varied fruit and observing classifier accuracy significantly below 90% would falsify the reliability of the identified sensor sets.

Figures

Figures reproduced from arXiv: 2604.24906 by Eva Krueger, Joseph R. Davidson, Marcus Rosette.

Figure 1
Figure 1. Figure 1: Pick state classification workflow. A compliant suction-based view at source ↗
Figure 2
Figure 2. Figure 2: Compliant suction-based gripper with integrated sensing. view at source ↗
Figure 3
Figure 3. Figure 3: Definition of pick states used for time-series labeling. Sensor data are segmented into view at source ↗
Figure 4
Figure 4. Figure 4: Pick state classification workflow. Raw sensor streams are collected from the gripper during picking. Ground truth labels are created from manual view at source ↗
Figure 5
Figure 5. Figure 5: Example sensor time series from a single grasp trial. From top view at source ↗
Figure 6
Figure 6. Figure 6: Permutation-based sensor importance across pick states for Random Forest and MLP models. Values are normalized within each state to highlight view at source ↗
read the original abstract

Robotic fruit harvesting often fails to reliably detect whether a fruit has been successfully picked, limiting efficiency and increasing crop damage. This problem is difficult due to compliant fruit and grippers, variable stem attachment, and occlusions in orchard environments. Prior work has explored vision-based perception and multi-sensor learning approaches for pick state estimation. However, minimal sensor sets and phase-dependent sensing strategies for accurate pick and slip detection remain largely unexplored. In this work, we design and evaluate a multimodal sensing suite integrated into a compliant suction-based apple gripper. Our approach is unique because it identifies which sensors are most informative at different phases of the pick, enabling predictive detection of failures before they occur. The contributions of this paper are a phase-dependent evaluation of multimodal sensors and the identification of minimal sensor sets for reliable pick state classification. Experiments in a real apple orchard show that Random Forest and Multilayer Perceptron classifiers detect successful picks and impending failures with over 90% accuracy, and Random Forest predicts pick/slip events within 0.09 s of human-annotated ground truth.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript designs and evaluates a multimodal sensing suite integrated into a compliant suction-based gripper for robotic apple picking. It performs a phase-dependent analysis to determine the most informative sensors during different stages of the pick operation, identifies minimal sensor subsets for reliable state classification, and reports orchard experiments in which Random Forest and Multilayer Perceptron classifiers achieve over 90% accuracy in detecting successful picks and impending failures, with the Random Forest also predicting pick/slip event times within 0.09 s of human-annotated ground truth.

Significance. If the performance claims hold under broader conditions, the work offers practical value for increasing the reliability of robotic fruit harvesting by enabling predictive detection of pick failures before they damage fruit or stems. The phase-dependent sensor ranking and minimal-set identification provide actionable guidance for hardware design in compliant manipulation. Real-world orchard data collection is a strength, as is the explicit focus on sensor selection rather than end-to-end vision-only approaches.

major comments (2)
  1. [Section 4 and Section 5] Section 4 (Experimental Setup) and Section 5 (Results): The central accuracy (>90%) and latency (0.09 s) figures are computed exclusively against human-annotated ground truth for pick success, slip onset, and impending failure. No inter-annotator agreement statistics, number of annotators, or objective corroboration (e.g., force-threshold slip detection or high-speed video verification) are reported. Because label noise or systematic bias would propagate directly into both the classifier metrics and the phase-dependent informativeness rankings, this omission is load-bearing for the primary claims.
  2. [Section 5] Section 5 (Results): The manuscript states that Random Forest and MLP classifiers were trained and evaluated but does not specify the cross-validation procedure, train/test split strategy, or whether temporal leakage was prevented when using time-series sensor data. Without these details it is impossible to assess whether the reported accuracies reflect genuine generalization or overfitting to orchard-specific conditions.
minor comments (2)
  1. [Abstract and Section 3] The abstract and Section 3 would benefit from a concise table listing the exact sensor models, sampling rates, and mounting locations on the gripper.
  2. [Figure captions] Figure captions for the phase-dependent sensor ranking plots should explicitly state the number of trials and fruit instances contributing to each phase.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for their constructive and detailed feedback, which highlights important aspects of ground truth reliability and experimental methodology in our work on multimodal sensor selection for suction-based apple picking. We address each major comment below and indicate the revisions planned for the next version of the manuscript.

read point-by-point responses

  1. Referee: [Section 4 and Section 5] Section 4 (Experimental Setup) and Section 5 (Results): The central accuracy (>90%) and latency (0.09 s) figures are computed exclusively against human-annotated ground truth for pick success, slip onset, and impending failure. No inter-annotator agreement statistics, number of annotators, or objective corroboration (e.g., force-threshold slip detection or high-speed video verification) are reported. Because label noise or systematic bias would propagate directly into both the classifier metrics and the phase-dependent informativeness rankings, this omission is load-bearing for the primary claims.

    Authors: We agree that the quality and validation of the ground-truth labels are central to the strength of our accuracy and latency claims. The annotations were performed by a single expert annotator using synchronized high-resolution video and sensor traces, with explicit labeling criteria for successful picks, slip onset, and impending failure. In the revised manuscript we will expand Section 4 to fully document the annotation protocol, the number of annotators (one), and the labeling guidelines. We will also add an explicit discussion of this choice as a limitation. Inter-annotator agreement statistics and independent objective corroboration (high-speed video or separate force-threshold detection) were not collected in the original study; we will therefore note these as limitations rather than provide new data. revision: partial

  2. Referee: [Section 5] Section 5 (Results): The manuscript states that Random Forest and MLP classifiers were trained and evaluated but does not specify the cross-validation procedure, train/test split strategy, or whether temporal leakage was prevented when using time-series sensor data. Without these details it is impossible to assess whether the reported accuracies reflect genuine generalization or overfitting to orchard-specific conditions.

    Authors: We appreciate the referee pointing out this lack of detail, which is necessary for assessing generalization. In the revised Section 5 we will specify that a 5-fold stratified cross-validation was used, with folds constructed so that entire pick sequences (and therefore all time steps from a given grasp) remained within a single fold. An 80/20 train/test split was applied at the sequence level, drawing the test set exclusively from unseen trees and orchard sessions to avoid temporal leakage and orchard-specific overfitting. These methodological choices will be stated explicitly together with the reported accuracy and latency figures. revision: yes

standing simulated objections not resolved
  • Inter-annotator agreement statistics and objective corroboration of ground-truth labels (e.g., high-speed video verification or independent force-threshold slip detection), as these were not collected during the original orchard experiments.

Circularity Check

0 steps flagged

No circularity: empirical ML evaluation on independent orchard data

full rationale

The paper reports an experimental study that collects multimodal sensor data during real apple orchard picks, trains standard off-the-shelf classifiers (Random Forest, MLP) on those data, and evaluates detection accuracy and event timing against separate human annotations. No derivation chain, equations, fitted parameters presented as predictions, or self-citation load-bearing steps appear in the described contributions or abstract. Performance claims rest on held-out experimental measurements rather than any reduction to the inputs by construction, so the work is self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work is empirical and relies on standard supervised learning assumptions rather than new theoretical constructs.

axioms (1)
  • domain assumption Training and test samples are independent and identically distributed.
    Implicit when applying Random Forest and MLP classifiers to orchard sensor data.

pith-pipeline@v0.9.0 · 5493 in / 1145 out tokens · 33036 ms · 2026-05-08T02:31:06.153618+00:00 · methodology

discussion (0)

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Reference graph

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