(1) Background: Since early yield prediction is relevant for resource requirements of harvesting and marketing in the whole fruit industry, this paper presents a new approach of using image analysis and tree canopy features to predict early yield with artificial neural networks (ANN); (2) Methods: Two back propagation neural network (BPNN) models were developed for the early period after natural fruit drop in June and the ripening period, respectively. Within the same periods, images of apple cv. “Gala” trees were captured from an orchard near Bonn, Germany. Two sample sets were developed to train and test models; each set included 150 samples from the 2009 and 2010 growing season. For each sample (each canopy image), pixels were segmented into fruit, foliage, and background using image segmentation. The four features extracted from the data set for the canopy were: total cross-sectional area of fruits, fruit number, total cross-section area of small fruits, and cross-sectional area of foliage, and were used as inputs. With the actual weighted yield per tree as a target, BPNN was employed to learn their mutual relationship as a prerequisite to develop the prediction; (3) Results: For the developed BPNN model of the early period after June drop, correlation coefficients (R²) between the estimated and the actual weighted yield, mean forecast error (MFE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were 0.81, −0.05, 10.7%, 2.34 kg/tree, respectively. For the model of the ripening period, these measures were 0.83, −0.03, 8.9%, 2.3 kg/tree, respectively. In 2011, the two previously developed models were used to predict apple yield. The RMSE and R² values between the estimated and harvested apple yield were 2.6 kg/tree and 0.62 for the early period (small, green fruit) and improved near harvest (red, large fruit) to 2.5 kg/tree and 0.75 for a tree with ca. 18 kg yield per tree. For further method verification, the cv. “Pinova” apple trees were used as another variety in 2012 to develop the BPNN prediction model for the early period after June drop. The model was used in 2013, which gave similar results as those found with cv. “Gala”; (4) Conclusion: Overall, the results showed in this research that the proposed estimation models performed accurately using canopy and fruit features using image analysis algorithms.

Silage is a critical global feedstock, but is prone to aerobic deterioration. The dominant mechanism of O₂ transport into silage remains unresolved. Here, multiple sensors tracked O₂ and CO₂, gas pressure (ΔP) between internal silage and ambient air, pH and silage temperature (T_si) during the ensilage of maize and ryegrass. We report the first observation that CO₂ produced from microbial respiration was partially dissolved in silage water, with evidence of negative or positive ΔP depending on the changing balance between CO₂ production and dissolution. The ΔP < 0 reflected an apparent respiratory quotient (RQ) < 1. Net CO₂ production was much greater in anaerobic fermentation stage than in initial aerobic phase or later aerobic feed-out phase. O₂ transport into silage is intimately linked to the dynamics of net CO₂, ΔP, microbial activity, pH and T_si. These results suggested that both gas diffusion (based on Fick’s law) and advective transfer (Darcy’s law) play equally important roles in governing the complex temporal progression of inward and outward gas fluxes to and from the silage interior. Even though low pH suppressed microbial activity and supported aerobic stability, the negative ΔP increased the risk of O₂ entry and aerobic deterioration during feed-out phase.

Silage quality and aerobic stability are sometimes insufficient. If management requirements are not met, or to improve silage quality, additives are often used. The objective of this study is to investigate the effects of different factors on silage during aerobic conditions. Whole-crop forage maize was harvested and 24 buckets (65 L) were filled and assigned to one of four treatment groups: (1) control (no treatment); (2) chemical additive (sodium benzoate, potassium sorbate, sodium acetate); (3) a mixed biological inoculant containing Lactobacillus buchneri, L. plantarum, and Pediococcus acidilacti; and (4) a mixed biological inoculant containing L. buchneri, L. plantarum, and L. rhamnosus. An untreated variation was also ensiled. Two different densities were adjusted during ensiling. After opening, the temperature was measured for seven days and O₂ and CO₂ concentrations were analysed. The findings show that the chemical additive very effectively prevented silage from reheating and deteriorating. Aerobic reheating of silage was also successfully inhibited through biological additives and high density.