Pathogens feed on fruits and vegetables causing great food losses or at least reduction of their shelf life. These pathogens can cause losses of the final product or in the farms were the products are grown, attacking leaves, stems and trees. This review analyses disease detection sensors and algorithms for both the farm and postharvest management of fruit and vegetable quality. Mango, avocado, apple, tomato, potato, citrus and grapes were selected as the fruits and vegetables for study due to their world-wide consumption. Disease warning systems for predicting pathogens and insects on farms during fruit and vegetable production are commonly used for all the crops and are available where meteorological stations are present. It can be seen that these disease risk systems are being slowly replaced by remote sensing monitoring in developed countries. Satellite images have reduced their temporal resolution, but are expensive and must become cheaper for their use world-wide. In the last 30 years, a lot of research has been carried out in non-destructive sensors for food quality. Actually, non-destructive technology has been applied for sorting high quality fruit which is desired by the consumer. The sensors require algorithms to work properly; the most used being discriminant analysis and training neural networks. New algorithms will be required due to the high quantity of data acquired and its processing, and for disease warning strategies for disease detection.

Fruit flies cause production losses in mango orchards affecting fruit quality. A National Campaign against Fruit Flies (NCFF) evaluates farm status using the fruit flies per trap per day index (FTD). Traps with attractant are installed manually within orchards in Mexico, but counting the flies trapped every week requires excessive numbers of trained personal. Electronic traps (e-traps) use sensors to monitor fruit fly population, saving labor and obtaining the real-time orchard infestation. The objective of this work was to acquire an image within a e-trap at 17:00 when an insect was detected and binarize the information in real-time to count the number of flies. Each e-trap was implemented with a polyethylene PET bottle screwed to a tap containing an ESP32-CAM camera. E-traps from several hectares of mango trees were sampled and transmitted through WSN wireless sensor networks. This original system presents a star topology network within each hectare with the long range LoRa transceiver at the central tower. It receives the fly count from five e-traps and finally transmits data to the house tower end point. Another contribution of this research was the use of a DJI mini2 for acquiring the e-trap data, and the 8-ha flight took 15 min and 35 s. This period can be reduced if the drone flies higher.

Mexico has dominated the international mango trade having high yields and excellent fruit quality. The use of soil, plant and environmental sensors can monitor plant hydric status and optimize water application. Linear resistance dendrometers were developed and installed in a high-density 8-year old commercial mango orchard during two seasons; 10 in 2019 and 40 in 2020. Stem diameter fluctuations were acquired every 10 minutes to obtain maximum diameter (MXD), minimum diameter (MND), morning slope (MS) and afternoon slope (AS). Mango stem response during flowering, fruit-fall and fruit-growth was analyzed after applying daily irrigation (DI) or reduced deficit irrigation (RDI). Yield was similar for both treatments, but water use efficiency was higher with deficit irrigation. A new variable known as negative integral (NI) was obtained from the dendrometer signature after adding all the values between 8:00 and 18:00. NI provided a stem shrinkage magnitude, peaks attributed to transpiration and slopes showing flow transport to and from the trunk. NI presented a high correlation of 0.85 against T7 during the flowering stage for RDI-1. If NI crossed zero, a severe stress was present; a high AS present in the evening, indicates nutrient solution provided to fruits. Fruit yield depends on panicle number and fruit-drop during the first production stages, being RDI-1 more efficient in retaining fruits.

Aims: A dissolved oxygen controller was designed to optimize photovoltaic energy and maintain minimum DO variations in the fish tanks, independent on environmental conditions. Study Design: Software critical parameters have to be tuned including discontinuous routine (DR) interval, use throughout the night and cloud sensing. Place and Duration of Study: Department of Irrigation at Tlapeaxco, Texcoco, Mexico at the Universidad Autonoma Chapingo, between April and July 2011. Methodology: The controller was assembled and its software tested to maintain the desired water dissolved oxygen concentration in the tanks. Solar radiation and clouds presented during May and June of 2011 were monitored correlating an infrared sensor with a sunshine indicator. Energy produced by the solar panels was acquired with a data logger together with the energy stored in the batteries; overvoltage and deep discharges were avoided by the battery. A discontinuous routine was turned on when clouds were present, evaluating whether it should operate during all the night. Results: May presented 26 cloudy days and 45% of these days presented less than 2 hours of more than 1000 W/m² of irradiation. Cloud cover sensing correlation between the infrared and sunshine sensor has a R²=0.97. Cloud cover peaks can last from minutes to hours and using the one-hour discontinuous routine (DR) instantaneous peaks were avoided. Optimal DR period was ten minutes as it saves the same quantity of energy but maintains dissolved oxygen (DO) concentration over 4.1 ppm; hourly DR intervals, decreases DO to 2.4 ppm stressing the carps. Battery charge should be at least 39 Ah@19:00 to supply the energy required by the aerators during the night. Conclusion: A sunshine sensor was selected to detect cloud cover and its sampling period was decreased from ten to one minute. DO concentration on tanks became more stable when the ten minute discontinuous period was employed.

Water is getting scarce and irrigation practices should become more efficient. Mango orchards require great quantities of water, and policies in developing countries are substituting surface gravity irrigation by pressurized systems. A commercial orchard having mature 25-year-old trees and a 10-year-old HD high-density section were irrigated with micro sprinklers using 100% ETc (crop evapotranspiration) and reduced deficit irrigation treatments of 75% and 50% ETc. Water soil measurements were made with EC-5 probes at 10 and 35 cm in depth to study the effect of the different irrigation treatments. After the 2020 harvest, mature trees were trimmed without achieving pruning severity greater than 1.3. Canopy volume, mango size, fruit yield and water-use efficiency WUE were analyzed during 2020 and 2021. Sporadic storms produced sprinkler watering problems as weeds proliferated within trees. A controller with a fuzzy algorithm optimized orchard management and saved water in trees without decreasing yield and fruit size. It was found that one year after mature trees were trimmed by taking away the larger internal branch, more light penetrated the canopy, increasing yield by 60%; pruning in HD trees presented a yield increase of 5.37%. WUE (water-use efficiency) also increased with pruning and its value increased to 87.6 when the fuzzy controller and the 50% DI treatments were used in mature trees. This value was 260% greater than the one obtained in pruned trees without the controller. HD trees presented a lower WUE and yield per hectare than mature trees.