Technologies for Forecasting Tree Fruit Load and Harvest Timing—From Ground, Sky and Time

Abstract

The management and marketing of fruit requires data on expected numbers, size, quality and timing. Current practice estimates orchard fruit load based on the qualitative assessment of fruit number per tree and historical orchard yield, or manually counting a subsample of trees. This review considers technological aids assisting these estimates, in terms of: (i) improving sampling strategies by the number of units to be counted and their selection; (ii) machine vision for the direct measurement of fruit number and size on the canopy; (iii) aerial or satellite imagery for the acquisition of information on tree structural parameters and spectral indices, with the indirect assessment of fruit load; (iv) models extrapolating historical yield data with knowledge of tree management and climate parameters, and (v) technologies relevant to the estimation of harvest timing such as heat units and the proximal sensing of fruit maturity attributes. Machine vision is currently dominating research outputs on fruit load estimation, while the improvement of sampling strategies has potential for a widespread impact. Techniques based on tree parameters and modeling offer scalability, but tree crops are complicated (perennialism). The use of machine vision for flowering estimates, fruit sizing, external quality evaluation is also considered. The potential synergies between technologies are highlighted.

Cornstarch-based, Biodegradable Superabsorbent Polymer to Improve Water Retention, Reduce Nitrate Leaching, and Result in Improved Tomato Growth and Development

“…Tomato plant dry matter production was found to be superior at cornstarch SAP 1-kg rates compared with all other rates. Although this did not translate directly into higher yields at this rate, there is a high correlation between higher biomass and greater plant yields in most horticultural crops (Anderson et al, 2021;Marcelis et al, 1998). Similar results were observed by Zangooei-Nasab et al (2012), who found that plant height, dry weight of aerial organs, and root dry weight of Saxaul seedlings was improved by 0.1%, 0.2%, 0.3%, and 0.4% rates of Stockosorb.…”

Deep-learning-based in-field citrus fruit detection and tracking

“…With the introduction of computer vision in the smart agriculture domain, fruit detection and tracking technologies, which can help to obtain fruit yield statistics and assist with automatic fruit picking and automated orchard management, have become important areas of research. Anderson et al [ 1 ] pointed out that yield prediction can help growers with farming decisions for the season, especially in regard to labor allocation for harvesting, fruit transportation, and storage methods. An experienced agronomist can use these resources and combine this information with knowledge of fruit tree physiology to provide advice on the current season and make recommendations on management practices for optimizing orchard development.…”

“…An experienced agronomist can use these resources and combine this information with knowledge of fruit tree physiology to provide advice on the current season and make recommendations on management practices for optimizing orchard development. Anderson et al [ 1 ] also pointed out that fruit yield prediction can now be broadly classified into five methods. Fruit yield estimation is a labor-intensive, monotonous, and tedious task.…”

EasyDAM_V2: Efficient Data Labeling Method for Multishape, Cross-Species Fruit Detection

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