Designing a Proximal Sensing Camera Acquisition System for Vineyard Applications: Results and Feedback on 8 Years of Experiments

Abstract

The potential of image proximal sensing for agricultural applications has been a prolific scientific subject in the recent literature. Its main appeal lies in the sensing of precise information about plant status, which is either harder or impossible to extract from lower-resolution downward-looking image sensors such as satellite or drone imagery. Yet, many theoretical and practical problems arise when dealing with proximal sensing, especially on perennial crops such as vineyards. Indeed, vineyards exhibit challenging physical obstacles and many degrees of variability in their layout. In this paper, we present the design of a mobile camera suited to vineyards and harsh experimental conditions, as well as the results and assessments of 8 years’ worth of studies using that camera. These projects ranged from in-field yield estimation (berry counting) to disease detection, providing new insights on typical viticulture problems that could also be generalized to orchard crops. Different recommendations are then provided using small case studies, such as the difficulties related to framing plots with different structures or the mounting of the sensor on a moving vehicle. While results stress the obvious importance and strong benefits of a thorough experimental design, they also indicate some inescapable pitfalls, illustrating the need for more robust image analysis algorithms and better databases. We believe sharing that experience with the scientific community can only benefit the future development of these innovative approaches.

Vine variety identification through leaf image classification: a large-scale study on the robustness of five deep learning models

“…To overcome the criticality highlighted in the current research, future works will explore (a) new models with larger input layers, (b) how to overcome the classical leaf image classification approach presented in this paper by experimenting new training methods, such as Triplet Loss (Dong and Shen, 2018;Ge, 2018) which allow us to build different feature spaces and (c) hybrid model architectures that include, in addition, grapes and shoot images, and other information such as day of the year, geographical coordinates or weather variables to be used as predictors or to implement a posteriori heuristics. Moreover, considering the recent innovations and developments in autonomous robotic systems in viticulture (Moreno and Andújar, 2023;Rançon et al, 2023) it is foreseeable that leaf images of a large number of varieties could be taken in a short time. This would make it possible to analyse a significant number of cultivated varieties and to improve and generalize the results of the proposed approach.…”