Reduction of Pesticide Use in Fresh-Cut Salad Production through Artificial Intelligence

Abstract

Incorrect pesticide use in plant protection often involve a risk to the health of operators and consumers and can have negative impacts on the environment and the crops. The application of artificial intelligence techniques can help the reduction of the volume sprayed, decreasing these impacts. In Italy, the production of ready-to-eat salad in greenhouses requires usually from 8 to 12 treatments per year. Moreover, inappropriate sprayers are frequently used, being originally designed for open-field operations. To solve this problem, a small vehicle suitable for moving over rough ground (named “rover”), was designed, able to carry out treatments based on a single row pass in the greenhouse, devoted to reduce significantly the sprayed product amount. To ascertain its potential, the prototype has been tested at two growth stages of some salad cultivars, adopting different nozzles and boom settings. Parameters such as boom height, nozzle spacing and inclination, pump pressure and rover traveling speed were studied. To assess the effectiveness of the spraying coverage, for each run several water-sensitive papers were placed throughout the vegetation. Compared to the commonly distributed mixture volume (1000 L/ha), the prototype is able to reduce up to 55% of product sprayed, but still assure an excellent crop coverage.

Artificial intelligence-based solutions for climate change: a review

“…Partel et al (2019) used an embedded graphics processing unit in a smart sprayer for precision weed control of artificial and amaranth weeds with 59-71% accuracy, which can significantly reduce pesticide costs, crop damage, and the risk of excessive herbicide residues, and potentially reduce environmental impacts. Facchinetti et al (2021) used a "Rover" sprayer vehicle to accurately detect color differences between salad and ground and reduce pesticide spraying by 55%. The I 2 PDM system is composed of an intelligent integrated pest management wireless sensor network that collects images, pest numbers, and species through sensor nodes and stores them in a database for analysis, thus generating models that can be visually translated into numerical information (Rustia et al 2020).…”

Monitoring System for the Management of the Common Agricultural Policy Using Machine Learning and Remote Sensing

“…Today, different research projects promote the use of AI, of which we highlight the following: NaLamKI [11], which aims to promote more efficient and sustainable agriculture by developing artificial intelligence methods to analyse remote sensing data for modelling agricultural processes and for 5G networks on farms, or knowlEdge [12], which proposes, among other things, a knowledge-based platform to distribute and exchange trained AI models. Other, more concrete, examples allow us to solve needs in the agricultural sector: reducing the environmental impact of pesticides in pest management [13] or controlling the artificial lighting system to optimise the energy efficiency of greenhouses [14].…”

Special Issue on Pesticide Applications in Agricultural Systems

“…The last paper, authored by Facchinetti et al, is an example of how artificial intelligence can be used in precision spraying to reduce the volume sprayed and, subsequently, decrease the environmental impact of pesticides [12]. In the paper, the authors present the design of a prototype "rover" capable of adjusting the spraying parameters according to the target features, which are detected in real time by means of a 3D camera.…”